From 1914952f68b07ea8b622db13fb8ef40508dadb29 Mon Sep 17 00:00:00 2001
From: ezicStar <juanpabloricapito@gmail.com>
Date: Mon, 7 Jul 2025 11:06:31 +0200
Subject: [PATCH 1/4] add mcp server

---
 mcp/README.md  |  93 ++++++
 mcp/server.py  | 779 +++++++++++++++++++++++++++++++++++++++++++++++++
 pyproject.toml |   3 +
 uv.lock        | 337 +++++++++++++++++++--
 4 files changed, 1194 insertions(+), 18 deletions(-)
 create mode 100644 mcp/README.md
 create mode 100644 mcp/server.py

diff --git a/mcp/README.md b/mcp/README.md
new file mode 100644
index 00000000..ec915ba3
--- /dev/null
+++ b/mcp/README.md
@@ -0,0 +1,93 @@
+# STAMP MCP Server
+
+A FastMCP-based Model Context Protocol server wrapping [STAMP](https://github.com/KatherLab/STAMP)’s CLI, enabling seamless integration of STAMP preprocessing, training, encoding, evaluation, and inference into LLM-based pipelines.
+
+## Overview
+
+This server lets LLM agents invoke STAMP tools via structured calls. It exposes the following tools:
+
+- `preprocess_stamp(...)`: tile & extract WSI features  
+- `train_stamp(...)`: train weakly supervised models  
+- `crossval_stamp(...)`: k-fold cross‑validation  
+- `deploy_stamp(...)`: inference on held‑out data
+- `encode_slides_stamp(...)`: slide-level feature encoding  
+- `encode_patients_stamp(...)`: patient-level feature encoding
+- `heatmaps_stamp(...)`: model-based heatmap visualization  
+- `statistics_stamp(...)`: compute classification metrics  
+- `read_file(...)` & `list_files(...)`: safe disk access  
+- `check_available_devices()`: query Torch/Platform device availability  
+
+Each tool serializes config into YAML, launches `stamp <mode>`, streams logs back, and returns stdout/stderr.
+
+## Example using OpenAI Agents SDK
+
+This example demonstrates how to connect an STAMP MCP server into an agent workflow using the official OpenAI Agents SDK v0.1.0
+
+Install OpenAI Agents SDK in your virtual environment.
+
+```bash
+uv pip install openai-agents==0.1.0
+```
+
+The following scripts defines an agent that extract features from given Whole Slide Images:
+
+```python
+# stamp_agent.py
+import asyncio
+from agents import Agent, Runner, OpenAIChatCompletionsModel, enable_verbose_stdout_logging
+from agents.mcp import MCPServerStreamableHttp
+from datetime import timedelta
+
+enable_verbose_stdout_logging()
+
+model = OpenAIChatCompletionsModel(model="gpt-o3",)
+
+async def preprocess_workflow(mcp_server: MCPServerStreamableHttp):
+    agent = Agent(
+        name="Oncology researcher",
+        instructions=(
+            "You are a bioinformatic researcher assigned to extract features from Whole Slide Images"
+        ),
+        model=model,
+        mcp_servers=[mcp_server],
+        model_settings=ModelSettings(tool_choice="required"),
+    )
+    message = "Step 1: Check available devices via `check_available_devices`. " \
+            "Step 2: Run `preprocess_stamp` on slides in `agent_workspace/slides`, " \
+            "extracting features with extractor='ctranspath', " \
+            "no cache, output to `agent_workspace/feats`, " \
+            "using the best device. " \
+            "Step 3: If preprocessing fails, adjust and retry once. " \
+            "Finally, summarize which device was used and whether it succeeded."
+    result = await Runner.run(starting_agent=agent, input=message)
+    print("Agent finished:", result.final_output)
+
+async def main():
+    async with MCPServerStreamableHttp(
+        name="STAMP MCP",
+        params={
+            "url": "http://127.0.0.1:8000/mcp",
+            "timeout": timedelta(minutes=10),
+            "sse_read_timeout": timedelta(hours=4),
+        },
+        client_session_timeout_seconds=6000,
+    ) as server:
+        await preprocess_workflow(server)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+> :warning: Note: Some STAMP tools (e.g., preprocessing, cross-validation) may run for a long time—please adjust both timeout and sse_read_timeout to avoid connection drops.
+
+Start STAMP MCP server with:
+
+```
+python server.py
+```
+
+On a different console, run your agent:
+
+```
+python stamp_agent.py
+```
diff --git a/mcp/server.py b/mcp/server.py
new file mode 100644
index 00000000..9174d495
--- /dev/null
+++ b/mcp/server.py
@@ -0,0 +1,779 @@
+import asyncio
+import logging
+import os
+import platform
+import subprocess
+import tempfile
+from typing import Annotated
+
+import torch
+import yaml
+from fastmcp import Context, FastMCP
+from pydantic import Field
+
+# Initialize the FastMCP server
+mcp = FastMCP("STAMP MCP Server")
+
+
+STAMP_LOGGER = logging.getLogger("stamp")
+# TODO: add proper filesystem management
+base_dir = "./"
+
+
+class MCPLogHandler(logging.Handler):
+    def __init__(self, ctx):
+        super().__init__()
+        self.ctx = ctx
+
+    def emit(self, record):
+        msg = self.format(record)
+        # Fire-and-forget the coroutine
+        asyncio.create_task(self.ctx.log(msg))
+
+
+async def _run_stamp(mode, config, ctx):
+    """
+    Run the STAMP command as a subprocess and capture its console output.
+
+    Args:
+        mode (str): The mode to run the STAMP command in (e.g., "preprocess", "train").
+        config (dict): The configuration dictionary to pass to the command.
+
+    Returns:
+        str: The combined stdout and stderr output from the command.
+    """
+    with tempfile.NamedTemporaryFile(
+        mode="w", delete=False, suffix=".yaml"
+    ) as tmp_config:
+        yaml.dump(config, tmp_config)
+        tmp_config_path = tmp_config.name
+
+    handler = MCPLogHandler(ctx)
+    handler.setLevel(logging.DEBUG)
+    STAMP_LOGGER.addHandler(handler)
+
+    print("Running command...")
+
+    try:
+        cmd = ["stamp", "--config", tmp_config_path, mode]
+        result = subprocess.run(cmd, capture_output=True, text=True, check=True)
+        print("Result returned...")
+        print(f"Command completed successfully:\n{result.stdout}\n{result.stderr}")
+        return f"Command completed successfully:\n{result.stdout}\n{result.stderr}"
+    except subprocess.CalledProcessError as e:
+        return f"Command failed with error:\n{e.stdout}\n{e.stderr}"
+    finally:
+        os.remove(tmp_config_path)
+        STAMP_LOGGER.removeHandler(handler)
+
+
+@mcp.tool
+async def preprocess_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to savethe results to")
+    ],
+    wsi_dir: Annotated[
+        str, Field(description="Path of the directory containing whole slide images")
+    ],
+    extractor: Annotated[
+        str,
+        Field(
+            description="Name of the extractor to use "
+            'for feature extraction. Possible options are "ctranspath" and "uni"'
+        ),
+    ] = "ctranspath",
+    cache_dir: Annotated[
+        str | None,
+        Field(
+            description="Directory to save preprocessed slide tiles for optimizing future preprocessing."
+        ),
+    ] = None,
+    device: Annotated[
+        str,
+        Field(
+            description="The device to use for computation. "
+            "Possible options are 'cuda' for NVIDIA GPUs, 'cpu' for general-purpose "
+            "processors, and 'mps' for Apple Silicon GPUs. Default is 'cuda'."
+        ),
+    ] = "cuda",
+    max_workers: Annotated[
+        int,
+        Field(
+            description="The maximum number of parallel "
+            "workers to use for computation. Increasing this value can improve performance "
+            "on systems with multiple CPU cores or GPUs. Default is 8."
+        ),
+    ] = 8,
+    tile_size_um: Annotated[
+        float,
+        Field(
+            description="Size of each tile in micrometers. "
+            "The default works well for most domains."
+        ),
+    ] = 256.0,
+    tile_size_px: Annotated[
+        int,
+        Field(
+            description="Resolution of the slide's tiles. "
+            "The default works well for most domains."
+        ),
+    ] = 224,
+    default_slide_mpp: Annotated[
+        str | None,
+        Field(
+            description="MPP of the slide to use if none can be inferred from the WSI"
+        ),
+    ] = None,
+) -> str:
+    """
+    Preprocess WSIs and extract patch-level features using STAMP.
+    Extracts features with the selected model, and optionally caches tiles.
+    Outputs include features (e.g., .h5) and tile preview images.
+
+    Returns:
+        str: message indicating success or failure.
+
+    Example:
+        >>> preprocess_stamp(
+                output_dir="output/features",
+                wsi_dir="input/slides",
+                extractor="ctranspath",
+                cache_dir="cache/tiles",
+                device="cuda",
+                max_workers=8
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "preprocessing": {
+            "output_dir": output_dir,
+            "wsi_dir": wsi_dir,
+            "extractor": extractor,
+            "cache_dir": cache_dir,
+            "device": device,
+            "max_workers": max_workers,
+            "tile_size_um": tile_size_um,
+            "tile_size_px": tile_size_px,
+            "default_slide_mpp": default_slide_mpp,
+        }
+    }
+    return await _run_stamp(mode="preprocess", config=config, ctx=ctx)
+
+
+@mcp.tool
+async def train_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to savethe results to")
+    ],
+    clini_table: Annotated[
+        str, Field(description="Path to a CSV or Excel to readclinical data from")
+    ],
+    feature_dir: Annotated[
+        str, Field(description="Path to a Directory containingfeature files")
+    ],
+    slide_table: Annotated[
+        str,
+        Field(
+            description="Path to a CSV or Excel to readpatient-slide associations from"
+        ),
+    ],
+    ground_truth_label: Annotated[
+        str,
+        Field(description="Name of categorical column in clinical table to train on"),
+    ],
+    categories: Annotated[
+        list[str],
+        Field(
+            description="The categories "
+            "occurring in the target label column of the clini table."
+        ),
+    ],
+    patient_label: Annotated[
+        str,
+        Field(
+            description="Name of the column "
+            "in the clini table that contains a unique ID for each patient"
+        ),
+    ] = "PATIENT",
+    filename_label: Annotated[
+        str,
+        Field(
+            description="Name of the column "
+            "in the slide table containing the feature file path relative to `feature_dir`"
+        ),
+    ] = "FILENAME",
+    bag_size: Annotated[
+        int,
+        Field(
+            description="Amount of tiles to sample when training. "
+            "Reducing this value reduces memory usage, but it is not recommended as the model can miss"
+            "relevant regions of the slide. Default value works well on H&E tissue images."
+        ),
+    ] = 512,
+    batch_size: Annotated[
+        int, Field(description="Amount of bags processed together.")
+    ] = 64,
+) -> str:
+    """
+    Train a model using clinical data and WSI-derived features via STAMP.
+    Takes in a clinical table, slide associations, and extracted features
+    to train a model on a specified label.
+
+    Returns:
+        str: message indicating the success or failure of the training operation,
+        along with any relevant console output.
+
+    Example:
+        >>> train_stamp(
+                output_dir="output/models",
+                clini_table="input/clinical_data.csv",
+                feature_dir="input/features",
+                slide_table="input/slide_table.csv",
+                ground_truth_label="OUTCOME",
+                categories=["Positive", "Negative"],
+                patient_label="PATIENT",
+                filename_label="FILENAME"
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "training": {
+            "output_dir": output_dir,
+            "clini_table": clini_table,
+            "feature_dir": feature_dir,
+            "slide_table": slide_table,
+            "ground_truth_label": ground_truth_label,
+            "categories": categories,
+            "patient_label": patient_label,
+            "filename_label": filename_label,
+            "bag_size": bag_size,
+            "batch_size": batch_size,
+        }
+    }
+    return await _run_stamp(mode="train", config=config, ctx=ctx)
+
+
+@mcp.tool
+async def crossval_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to savethe results to")
+    ],
+    clini_table: Annotated[
+        str, Field(description="Path of aCSV or Excel to readclinical data from")
+    ],
+    feature_dir: Annotated[
+        str, Field(description="Path of the directory containingfeature files")
+    ],
+    slide_table: Annotated[
+        str,
+        Field(
+            description="Path to a CSV or Excel to readpatient-slide associations from"
+        ),
+    ],
+    ground_truth_label: Annotated[
+        str,
+        Field(description="Name of categorical column in clinical table to train on"),
+    ],
+    categories: Annotated[
+        list[str],
+        Field(
+            description="The categories "
+            "occurring in the target label column of the clini table."
+        ),
+    ],
+    patient_label: Annotated[
+        str,
+        Field(
+            description="Name of the column "
+            "in the clini table that contains a unique ID for each patient"
+        ),
+    ] = "PATIENT",
+    filename_label: Annotated[
+        str,
+        Field(
+            description="Name of the column "
+            "in the slide table containing the feature file path relative to `feature_dir`"
+        ),
+    ] = "FILENAME",
+    n_folds: Annotated[
+        int, Field("Number of folds to split the data into for cross-validation")
+    ] = 3,
+    bag_size: Annotated[
+        int,
+        Field(
+            description="Amount of tiles to sample when training. "
+            "Reducing this value reduces memory usage, but it is not recommended as the model can miss"
+            "relevant regions of the slide. Default value works well on H&E tissue images."
+        ),
+    ] = 512,
+    batch_size: Annotated[
+        int, Field(description="Amount of bags processed together.")
+    ] = 64,
+) -> str:
+    """
+    Perform cross-validation for model training using STAMP.
+    Splits the data into folds and trains a model on each to assess
+    generalization. Uses clinical data, features, and slide mappings.
+
+    Returns:
+        str: A message indicating the success or failure of the cross-validation operation, along with
+             any relevant console output from the STAMP pipeline.
+
+    Example:
+        >>> crossval_stamp(
+                output_dir="output/crossval",
+                clini_table="input/clinical_data.csv",
+                feature_dir="input/features",
+                slide_table="input/slide_table.csv",
+                ground_truth_label="OUTCOME",
+                categories=["Positive", "Negative"],
+                patient_label="PATIENT",
+                filename_label="FILENAME",
+                n_folds=5
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "training": {
+            "output_dir": output_dir,
+            "clini_table": clini_table,
+            "feature_dir": feature_dir,
+            "slide_table": slide_table,
+            "ground_truth_label": ground_truth_label,
+            "categories": categories,
+            "patient_label": patient_label,
+            "filename_label": filename_label,
+            "n_folds": n_folds,
+            "bag_size": bag_size,
+            "batch_size": batch_size,
+        }
+    }
+    return await _run_stamp(mode="crossval", config=config, ctx=ctx)
+
+
+@mcp.tool
+async def deploy_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to save the results to")
+    ],
+    clini_table: Annotated[
+        str, Field(description="Path to a CSV or Excel to read clinical data from")
+    ],
+    feature_dir: Annotated[
+        str, Field(description="Path of the directory containing feature files")
+    ],
+    slide_table: Annotated[
+        str,
+        Field(
+            description="Path to a CSV or Excel to read patient-slide associations from"
+        ),
+    ],
+    ground_truth_label: Annotated[
+        str,
+        Field(description="Name of categorical column in clinical table to train on"),
+    ],
+    categories: Annotated[
+        list[str],
+        Field(
+            description="The categories "
+            "occurring in the target label column of the clini table."
+        ),
+    ],
+    checkpoint_paths: Annotated[
+        list[str],
+        Field(
+            description="You can also "
+            "combine multiple models to get a majority vote from multiple models. "
+            "This is especially handy to e.g. combine the models from a cross-validation."
+        ),
+    ],
+    patient_label: Annotated[
+        str,
+        Field(
+            description="Name of the column "
+            "in the clini table that contains a unique ID for each patient"
+        ),
+    ] = "PATIENT",
+    filename_label: Annotated[
+        str,
+        Field(
+            description="Name of the column "
+            "in the slide table containing the feature file path relative to `feature_dir`"
+        ),
+    ] = "FILENAME",
+) -> str:
+    """
+    Run inference using trained STAMP model(s).
+
+    Generates predictions for patients using one or more model checkpoints and associated feature data.
+
+    Returns:
+        str: A message indicating the success or failure of the deployment operation, along with
+             any relevant console output from the STAMP pipeline.
+
+    Example:
+        >>> deploy_stamp(
+                output_dir="output/predictions",
+                clini_table="input/clinical_data.csv",
+                feature_dir="input/features",
+                slide_table="input/slide_table.csv",
+                ground_truth_label="OUTCOME",
+                categories=["Positive", "Negative"],
+                checkpoint_paths=["models/checkpoint1.pth", "models/checkpoint2.pth"],
+                patient_label="PATIENT",
+                filename_label="FILENAME"
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "training": {
+            "output_dir": output_dir,
+            "clini_table": clini_table,
+            "feature_dir": feature_dir,
+            "slide_table": slide_table,
+            "ground_truth_label": ground_truth_label,
+            "categories": categories,
+            "patient_label": patient_label,
+            "filename_label": filename_label,
+            "checkpoint_paths": checkpoint_paths,
+        }
+    }
+    return await _run_stamp(mode="deploy", config=config, ctx=ctx)
+
+
+@mcp.tool
+async def statistics_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to save the results to")
+    ],
+    ground_truth_label: Annotated[str, Field(description="Name of the target label.")],
+    true_class: Annotated[
+        str,
+        Field(description="The positive class to calculate ove-vs-all statistics for."),
+    ],
+    pred_csvs: Annotated[
+        list[str],
+        Field(
+            description="List of CSV filepaths containing "
+            "patient predictions to generate statistics from."
+        ),
+    ],
+) -> str:
+    """
+    Generate evaluation metrics for model predictions.
+
+    Computes classification statistics (e.g., precision, recall, F1)
+    from prediction CSVs, using the specified target and positive class.
+
+    Returns:
+        str: A message indicating the success or failure of the statistics generation operation,
+             along with any relevant console output from the STAMP pipeline.
+
+    Example:
+        >>> statistics_stamp(
+                output_dir="output/statistics",
+                ground_truth_label="OUTCOME",
+                true_class="Positive",
+                pred_csvs=["predictions/fold1.csv", "predictions/fold2.csv"]
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "statistics": {
+            "output_dir": output_dir,
+            "ground_truth_label": ground_truth_label,
+            "true_class": true_class,
+            "pred_csvs": pred_csvs,
+        }
+    }
+    return await _run_stamp(mode="statistics", config=config, ctx=ctx)
+
+
+@mcp.tool
+async def heatmaps_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to savethe results to")
+    ],
+    feature_dir: Annotated[
+        str, Field(description="Path of the directory containingfeature files")
+    ],
+    wsi_dir: Annotated[
+        str, Field(description="Path of the directory containing whole slide images")
+    ],
+    checkpoint_path: Annotated[
+        str, Field(description="Path of the model to generate the heatmaps with.")
+    ],
+    slide_paths: Annotated[
+        list[str] | None,
+        Field(
+            description="List of slide paths relative "
+            "to `wsi_dir` to generate heatmaps for. If not specified, heatmaps will be generated "
+            "for all slides in `wsi_dir`."
+        ),
+    ] = None,
+    topk: Annotated[
+        int | None, Field(description="Number of top-scoring tiles to extract")
+    ] = None,
+    bottomk: Annotated[
+        int | None, Field(description="Number of bottom-scoring tiles to extract")
+    ] = None,
+) -> str:
+    """
+    Generate heatmaps and tile scorings from WSIs using a trained model.
+    Produces visual explanations and optionally extracts top/bottom
+    scoring tiles.
+
+    Returns:
+        str: A message indicating the success or failure of the heatmap generation operation,
+             along with any relevant console output from the STAMP pipeline.
+
+    Example:
+        >>> heatmaps_stamp(
+                output_dir="output/heatmaps",
+                feature_dir="input/features",
+                wsi_dir="input/slides",
+                checkpoint_path="models/checkpoint.pth",
+                slide_paths=["slide1.svs", "slide2.svs"],
+                topk=10,
+                bottomk=5
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "heatmaps": {
+            "output_dir": output_dir,
+            "feature_dir": feature_dir,
+            "wsi_dir": wsi_dir,
+            "checkpoint_path": checkpoint_path,
+            "slide_paths": slide_paths,
+            "topk": topk,
+            "bottomk": bottomk,
+        }
+    }
+    return await _run_stamp(mode="heatmaps", config=config, ctx=ctx)
+
+
+@mcp.tool
+async def encode_slides_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to savethe results to")
+    ],
+    feature_dir: Annotated[
+        str, Field(description="Path of the directory containingfeature files")
+    ],
+    wsi_dir: Annotated[
+        str, Field(description="Path of the directory containing whole slide images")
+    ],
+    encoder: Annotated[
+        str,
+        Field(
+            description="Name of the encoder to use "
+            'for feature extraction. Possible options are "chief" and "cobra"'
+        ),
+    ] = "chief",
+    device: Annotated[
+        str,
+        Field(
+            description="The device to use for computation. "
+            "Possible options are 'cuda' for NVIDIA GPUs, 'cpu' for general-purpose "
+            "processors, and 'mps' for Apple Silicon GPUs. Default is 'cuda'."
+        ),
+    ] = "cuda",
+) -> str:
+    """Tile-Level features can be enconded into a single feature per slide,
+    this is useful when trying to capture global patterns across whole slides.
+    This tool takes as input tile-level features stored in .h5 files (one file per slide
+    containing the features in the field "feats" concatenated in a tensor).
+    The output is one .h5 file per slide.
+
+    Returns:
+        str: A message indicating the success or failure of the encoding operation, along with
+             any relevant console output from the STAMP pipeline.
+
+    Example:
+        >>> encode_slides_stamp(
+                output_dir="output/features",
+                wsi_dir="input/slides",
+                encoder="chief",
+                device="cuda",
+                feature_dir="input/features"
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "slide_encoding": {
+            "output_dir": output_dir,
+            "wsi_dir": wsi_dir,
+            "encoder": encoder,
+            "device": device,
+            "feature_dir": feature_dir,
+        }
+    }
+
+    return await _run_stamp(mode="encode_slides", config=config, ctx=ctx)
+
+
+@mcp.tool
+async def encode_patients_stamp(
+    ctx: Context,
+    output_dir: Annotated[
+        str, Field(description="Path of the directory to savethe results to")
+    ],
+    feature_dir: Annotated[
+        str, Field(description="Path of the directory containingfeature files")
+    ],
+    wsi_dir: Annotated[
+        str, Field(description="Path of the directory containing whole slide images")
+    ],
+    slide_table: Annotated[
+        str,
+        Field(
+            description="Path to a CSV or Excel to readpatient-slide associations from"
+        ),
+    ],
+    encoder: Annotated[
+        str,
+        Field(
+            description="Name of the encoder to use "
+            'for feature extraction. Possible options are "chief" and "cobra"'
+        ),
+    ] = "chief",
+    device: Annotated[
+        str,
+        Field(
+            description="The device to use for computation. "
+            "Possible options are 'cuda' for NVIDIA GPUs, 'cpu' for general-purpose "
+            "processors, and 'mps' for Apple Silicon GPUs. Default is 'cuda'."
+        ),
+    ] = "cuda",
+) -> str:
+    """Tile-Level features can be enconded into a single feature per patient,
+    this is useful when trying to capture global patterns across whole slides.
+    This tool takes as input tile-level features stored in .h5 files (one file per slide
+    containing the features in the field "feats" concatenated in a tensor).
+    The output is one .h5 file per feature.
+
+    Returns:
+        str: A message indicating the success or failure of the encoding operation, along with
+             any relevant console output from the STAMP pipeline.
+
+    Example:
+        >>> encode_patients_stamp(
+                output_dir="output/features",
+                wsi_dir="input/slides",
+                encoder="chief",
+                device="cuda",
+                feature_dir="input/features"
+                slide_table="input/slide_table.csv",
+            )
+        "Command completed successfully: ..."
+    """
+    config = {
+        "patient_encoding": {
+            "output_dir": output_dir,
+            "wsi_dir": wsi_dir,
+            "encoder": encoder,
+            "device": device,
+            "feature_dir": feature_dir,
+            "slide_table": slide_table,
+        }
+    }
+
+    return await _run_stamp(mode="encode_patients", config=config, ctx=ctx)
+
+
+def _resolve_path(path: str) -> str:
+    """
+    Resolves the absolute path and ensures it's within the allowed base directory.
+
+    Args:
+        path (str): Relative path from the allowed base directory.
+
+    Returns:
+        str: Safe, absolute path.
+
+    Raises:
+        PermissionError: If the resolved path is outside the allowed directory.
+    """
+    abs_path = os.path.abspath(os.path.join(base_dir, path))
+    if not abs_path.startswith(base_dir):
+        raise PermissionError(f"Access denied to path: {path}")
+    return abs_path
+
+
+@mcp.tool
+def read_file(path: str) -> str:
+    """
+    Read the contents of a file inside the allowed folder.
+
+    Args:
+        path (str): Relative path to the file.
+
+    Returns:
+        str: Content of the file.
+    """
+    safe_path = _resolve_path(path)
+    with open(safe_path, "r", encoding="utf-8") as f:
+        return f.read()
+
+
+@mcp.tool
+def list_files(subdir: str = "") -> list:
+    """
+    List all files under the given subdirectory (default is root), recursively,
+    returning paths relative to the base directory.
+
+    Args:
+        subdir (str): Relative subdirectory path to list files from.
+
+    Returns:
+        list: List of relative file paths found.
+    """
+    safe_path = _resolve_path(subdir)
+    file_list = []
+    for root, _, files in os.walk(safe_path):
+        for f in files:
+            full_path = os.path.join(root, f)
+            rel_path = os.path.relpath(full_path, base_dir)
+            file_list.append(rel_path)
+    return file_list
+
+
+@mcp.tool
+def check_available_devices() -> str:
+    """
+    Check which computation devices are available on the system.
+    This includes checking for cuda (NVIDIA GPUs) and mps (Apple Silicon GPUs).
+
+    Returns:
+        A string describing the available devices.
+    """
+    devices = []
+
+    # Check for CUDA availability
+    if torch.cuda.is_available():
+        devices.append("cuda")
+
+    # Check for MPS availability (Apple Silicon GPUs)
+    if platform.system() == "Darwin" and torch.backends.mps.is_available():
+        devices.append("mps")
+
+    # Check for CPU (always available)
+    devices.append("cpu")
+
+    # Format the result
+    if devices:
+        return f"Available devices: {', '.join(devices)}"
+    else:
+        return "No computation devices are available."
+
+
+if __name__ == "__main__":
+    mcp.run(transport="streamable-http")
diff --git a/pyproject.toml b/pyproject.toml
index df2a4c79..f38cf7ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -89,6 +89,9 @@ musk = [
 plip = [
     "transformers>=4.45.2"
 ]
+fastmcp = [
+    "fastmcp>=2.7.0"
+]
 
 # Blanket target
 all = ["stamp[conch,ctranspath,uni,virchow2,chief_ctranspath,conch1_5,prism,madeleine,musk,plip]"]
diff --git a/uv.lock b/uv.lock
index d29d37c7..9de85f04 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 1
 requires-python = ">=3.11"
 resolution-markers = [
     "python_version < '0'",
@@ -121,6 +122,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
+[[package]]
+name = "anyio"
+version = "4.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
+]
+
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -148,6 +163,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815 },
 ]
 
+[[package]]
+name = "authlib"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a2/9d/b1e08d36899c12c8b894a44a5583ee157789f26fc4b176f8e4b6217b56e1/authlib-1.6.0.tar.gz", hash = "sha256:4367d32031b7af175ad3a323d571dc7257b7099d55978087ceae4a0d88cd3210", size = 158371 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/84/29/587c189bbab1ccc8c86a03a5d0e13873df916380ef1be461ebe6acebf48d/authlib-1.6.0-py2.py3-none-any.whl", hash = "sha256:91685589498f79e8655e8a8947431ad6288831d643f11c55c2143ffcc738048d", size = 239981 },
+]
+
 [[package]]
 name = "autograd"
 version = "1.8.0"
@@ -444,6 +471,47 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/87/68/7f46fb537958e87427d98a4074bcde4b67a70b04900cfc5ce29bc2f556c1/contourpy-1.3.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8c5acb8dddb0752bf252e01a3035b21443158910ac16a3b0d20e7fed7d534ce5", size = 221791 },
 ]
 
+[[package]]
+name = "cryptography"
+version = "45.0.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi", marker = "platform_python_implementation != 'PyPy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/95/1e/49527ac611af559665f71cbb8f92b332b5ec9c6fbc4e88b0f8e92f5e85df/cryptography-45.0.5.tar.gz", hash = "sha256:72e76caa004ab63accdf26023fccd1d087f6d90ec6048ff33ad0445abf7f605a", size = 744903 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f0/fb/09e28bc0c46d2c547085e60897fea96310574c70fb21cd58a730a45f3403/cryptography-45.0.5-cp311-abi3-macosx_10_9_universal2.whl", hash = "sha256:101ee65078f6dd3e5a028d4f19c07ffa4dd22cce6a20eaa160f8b5219911e7d8", size = 7043092 },
+    { url = "https://files.pythonhosted.org/packages/b1/05/2194432935e29b91fb649f6149c1a4f9e6d3d9fc880919f4ad1bcc22641e/cryptography-45.0.5-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3a264aae5f7fbb089dbc01e0242d3b67dffe3e6292e1f5182122bdf58e65215d", size = 4205926 },
+    { url = "https://files.pythonhosted.org/packages/07/8b/9ef5da82350175e32de245646b1884fc01124f53eb31164c77f95a08d682/cryptography-45.0.5-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e74d30ec9c7cb2f404af331d5b4099a9b322a8a6b25c4632755c8757345baac5", size = 4429235 },
+    { url = "https://files.pythonhosted.org/packages/7c/e1/c809f398adde1994ee53438912192d92a1d0fc0f2d7582659d9ef4c28b0c/cryptography-45.0.5-cp311-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:3af26738f2db354aafe492fb3869e955b12b2ef2e16908c8b9cb928128d42c57", size = 4209785 },
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+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -556,6 +624,18 @@ wheels = [
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+version = "1.3.0"
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+sdist = { url = "https://files.pythonhosted.org/packages/02/2e/58d83848dd1a79cb92ed8e63f6ba901ca282c5f09d04af9423ec26c56fd7/openapi_pydantic-0.5.1.tar.gz", hash = "sha256:ff6835af6bde7a459fb93eb93bb92b8749b754fc6e51b2f1590a19dc3005ee0d", size = 60892 }
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+    { url = "https://files.pythonhosted.org/packages/12/cf/03675d8bd8ecbf4445504d8071adab19f5f993676795708e36402ab38263/openapi_pydantic-0.5.1-py3-none-any.whl", hash = "sha256:a3a09ef4586f5bd760a8df7f43028b60cafb6d9f61de2acba9574766255ab146", size = 96381 },
+]
+
 [[package]]
 name = "opencv-python"
 version = "4.11.0.86"
@@ -2180,6 +2370,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/32/56/8a7ca5d2cd2cda1d245d34b1c9a942920a718082ae8e54e5f3e5a58b7add/pydantic_core-2.33.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:329467cecfb529c925cf2bbd4d60d2c509bc2fb52a20c1045bf09bb70971a9c1", size = 2066757 },
 ]
 
+[[package]]
+name = "pydantic-settings"
+version = "2.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pydantic" },
+    { name = "python-dotenv" },
+    { name = "typing-inspection" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/68/85/1ea668bbab3c50071ca613c6ab30047fb36ab0da1b92fa8f17bbc38fd36c/pydantic_settings-2.10.1.tar.gz", hash = "sha256:06f0062169818d0f5524420a360d632d5857b83cffd4d42fe29597807a1614ee", size = 172583 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/58/f0/427018098906416f580e3cf1366d3b1abfb408a0652e9f31600c24a1903c/pydantic_settings-2.10.1-py3-none-any.whl", hash = "sha256:a60952460b99cf661dc25c29c0ef171721f98bfcb52ef8d9ea4c943d7c8cc796", size = 45235 },
+]
+
 [[package]]
 name = "pygments"
 version = "2.19.1"
@@ -2322,6 +2526,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "python-multipart"
+version = "0.0.20"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/87/f44d7c9f274c7ee665a29b885ec97089ec5dc034c7f3fafa03da9e39a09e/python_multipart-0.0.20.tar.gz", hash = "sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13", size = 37158 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/45/58/38b5afbc1a800eeea951b9285d3912613f2603bdf897a4ab0f4bd7f405fc/python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104", size = 24546 },
+]
+
 [[package]]
 name = "pytorch-lightning"
 version = "2.5.1.post0"
@@ -2558,6 +2771,19 @@ socks = [
     { name = "pysocks" },
 ]
 
+[[package]]
+name = "rich"
+version = "14.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a1/53/830aa4c3066a8ab0ae9a9955976fb770fe9c6102117c8ec4ab3ea62d89e8/rich-14.0.0.tar.gz", hash = "sha256:82f1bc23a6a21ebca4ae0c45af9bdbc492ed20231dcb63f297d6d1021a9d5725", size = 224078 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0d/9b/63f4c7ebc259242c89b3acafdb37b41d1185c07ff0011164674e9076b491/rich-14.0.0-py3-none-any.whl", hash = "sha256:1c9491e1951aac09caffd42f448ee3d04e58923ffe14993f6e83068dc395d7e0", size = 243229 },
+]
+
 [[package]]
 name = "ruff"
 version = "0.11.12"
@@ -2885,6 +3111,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ea/f1/5e9b3ba5c7aa7ebfaf269657e728067d16a7c99401c7973ddf5f0cf121bd/shapely-2.1.1-cp313-cp313t-win_amd64.whl", hash = "sha256:8cb8f17c377260452e9d7720eeaf59082c5f8ea48cf104524d953e5d36d4bdb7", size = 1723061 },
 ]
 
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de", size = 10310 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755 },
+]
+
 [[package]]
 name = "six"
 version = "1.17.0"
@@ -2903,6 +3138,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/04/be/d09147ad1ec7934636ad912901c5fd7667e1c858e19d355237db0d0cd5e4/smmap-5.0.2-py3-none-any.whl", hash = "sha256:b30115f0def7d7531d22a0fb6502488d879e75b260a9db4d0819cfb25403af5e", size = 24303 },
 ]
 
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc", size = 20372 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235 },
+]
+
 [[package]]
 name = "soupsieve"
 version = "2.7"
@@ -2912,6 +3156,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e7/9c/0e6afc12c269578be5c0c1c9f4b49a8d32770a080260c333ac04cc1c832d/soupsieve-2.7-py3-none-any.whl", hash = "sha256:6e60cc5c1ffaf1cebcc12e8188320b72071e922c2e897f737cadce79ad5d30c4", size = 36677 },
 ]
 
+[[package]]
+name = "sse-starlette"
+version = "2.3.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8c/f4/989bc70cb8091eda43a9034ef969b25145291f3601703b82766e5172dfed/sse_starlette-2.3.6.tar.gz", hash = "sha256:0382336f7d4ec30160cf9ca0518962905e1b69b72d6c1c995131e0a703b436e3", size = 18284 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/05/78850ac6e79af5b9508f8841b0f26aa9fd329a1ba00bf65453c2d312bcc8/sse_starlette-2.3.6-py3-none-any.whl", hash = "sha256:d49a8285b182f6e2228e2609c350398b2ca2c36216c2675d875f81e93548f760", size = 10606 },
+]
+
 [[package]]
 name = "stack-data"
 version = "0.6.3"
@@ -2962,7 +3218,6 @@ all = [
     { name = "einops-exts" },
     { name = "environs" },
     { name = "gdown" },
-    { name = "gigapath" },
     { name = "huggingface-hub" },
     { name = "madeleine" },
     { name = "musk" },
@@ -2990,6 +3245,9 @@ conch1-5 = [
 ctranspath = [
     { name = "gdown" },
 ]
+fastmcp = [
+    { name = "fastmcp" },
+]
 gigapath = [
     { name = "gigapath" },
 ]
@@ -3027,14 +3285,15 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "beartype", specifier = ">=0.19.0" },
-    { name = "cobra", marker = "extra == 'cobra'", git = "https://github.com/KatherLab/COBRA.git?rev=f1a576e1133330ffc2d1df6ee110701921c7b7c9#f1a576e1133330ffc2d1df6ee110701921c7b7c9" },
-    { name = "conch", marker = "extra == 'conch'", git = "https://github.com/Mahmoodlab/CONCH.git?rev=02d6ac59cc20874bff0f581de258c2b257f69a84#02d6ac59cc20874bff0f581de258c2b257f69a84" },
+    { name = "cobra", marker = "extra == 'cobra'", git = "https://github.com/KatherLab/COBRA.git?rev=f1a576e1133330ffc2d1df6ee110701921c7b7c9" },
+    { name = "conch", marker = "extra == 'conch'", git = "https://github.com/Mahmoodlab/CONCH.git?rev=02d6ac59cc20874bff0f581de258c2b257f69a84" },
     { name = "einops", specifier = ">=0.8.0" },
     { name = "einops-exts", marker = "extra == 'conch1-5'", specifier = "==0.0.4" },
     { name = "environs", marker = "extra == 'prism'", specifier = "==11.0.0" },
+    { name = "fastmcp", marker = "extra == 'fastmcp'", specifier = ">=2.7.0" },
     { name = "gdown", marker = "extra == 'chief-ctranspath'", specifier = ">=5.2.0" },
     { name = "gdown", marker = "extra == 'ctranspath'", specifier = ">=5.2.0" },
-    { name = "gigapath", marker = "extra == 'gigapath'", git = "https://github.com/EzicStar/prov-gigapath.git?rev=d4cf55321df37aaf867e24a31c61bcf490a296eb#d4cf55321df37aaf867e24a31c61bcf490a296eb" },
+    { name = "gigapath", marker = "extra == 'gigapath'", git = "https://github.com/EzicStar/prov-gigapath.git?rev=d4cf55321df37aaf867e24a31c61bcf490a296eb" },
     { name = "h5py", specifier = ">=3.12.1" },
     { name = "huggingface-hub", marker = "extra == 'conch'", specifier = ">=0.26.2" },
     { name = "huggingface-hub", marker = "extra == 'uni'", specifier = ">=0.26.2" },
@@ -3042,9 +3301,9 @@ requires-dist = [
     { name = "jaxtyping", specifier = ">=0.2.36" },
     { name = "jinja2", marker = "extra == 'cobra'", specifier = ">=3.1.4" },
     { name = "lightning", specifier = ">=2.4.0" },
-    { name = "madeleine", marker = "extra == 'madeleine'", git = "https://github.com/mahmoodlab/MADELEINE.git?rev=de7c85acc2bdad352e6df8eee5694f8b6f288012#de7c85acc2bdad352e6df8eee5694f8b6f288012" },
+    { name = "madeleine", marker = "extra == 'madeleine'", git = "https://github.com/mahmoodlab/MADELEINE.git?rev=de7c85acc2bdad352e6df8eee5694f8b6f288012" },
     { name = "matplotlib", specifier = ">=3.9.2" },
-    { name = "musk", marker = "extra == 'musk'", git = "https://github.com/lilab-stanford/MUSK.git?rev=e1699c27687f44bbf6d4adfcbb2abe89795d347f#e1699c27687f44bbf6d4adfcbb2abe89795d347f" },
+    { name = "musk", marker = "extra == 'musk'", git = "https://github.com/lilab-stanford/MUSK.git?rev=e1699c27687f44bbf6d4adfcbb2abe89795d347f" },
     { name = "numpy", specifier = ">=2.2.2" },
     { name = "opencv-python", specifier = ">=4.10.0.84" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -3058,7 +3317,7 @@ requires-dist = [
     { name = "sacremoses", marker = "extra == 'prism'", specifier = "==0.1.1" },
     { name = "scikit-learn", specifier = ">=1.5.2" },
     { name = "scipy", specifier = ">=1.15.1" },
-    { name = "stamp", extras = ["conch", "ctranspath", "uni", "virchow2", "chief-ctranspath", "conch1-5", "gigapath", "prism", "madeleine", "musk", "plip"], marker = "extra == 'all'" },
+    { name = "stamp", extras = ["conch", "ctranspath", "uni", "virchow2", "chief-ctranspath", "conch1-5", "prism", "madeleine", "musk", "plip"], marker = "extra == 'all'" },
     { name = "timm", specifier = ">=0.9.11" },
     { name = "torch", specifier = ">=2.5.1" },
     { name = "torch", marker = "extra == 'chief-ctranspath'", specifier = ">=2.0.0" },
@@ -3070,6 +3329,7 @@ requires-dist = [
     { name = "transformers", marker = "extra == 'plip'", specifier = ">=4.45.2" },
     { name = "uni", marker = "extra == 'uni'", git = "https://github.com/mahmoodlab/UNI.git" },
 ]
+provides-extras = ["conch", "conch1-5", "ctranspath", "chief-ctranspath", "gigapath", "uni", "virchow2", "cobra", "prism", "madeleine", "musk", "plip", "fastmcp", "all"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -3080,6 +3340,19 @@ dev = [
     { name = "ruff", specifier = ">=0.8.1" },
 ]
 
+[[package]]
+name = "starlette"
+version = "0.47.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0a/69/662169fdb92fb96ec3eaee218cf540a629d629c86d7993d9651226a6789b/starlette-0.47.1.tar.gz", hash = "sha256:aef012dd2b6be325ffa16698f9dc533614fb1cebd593a906b90dc1025529a79b", size = 2583072 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/82/95/38ef0cd7fa11eaba6a99b3c4f5ac948d8bc6ff199aabd327a29cc000840c/starlette-0.47.1-py3-none-any.whl", hash = "sha256:5e11c9f5c7c3f24959edbf2dffdc01bba860228acf657129467d8a7468591527", size = 72747 },
+]
+
 [[package]]
 name = "sympy"
 version = "1.13.1"
@@ -3357,6 +3630,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0a/93/f28a696fa750b9b608baa236f8225dd3290e5aff27433b06143adc025961/triton-3.3.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce4700fc14032af1e049005ae94ba908e71cd6c2df682239aed08e49bc71b742", size = 156580729 },
 ]
 
+[[package]]
+name = "typer"
+version = "0.16.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "rich" },
+    { name = "shellingham" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c5/8c/7d682431efca5fd290017663ea4588bf6f2c6aad085c7f108c5dbc316e70/typer-0.16.0.tar.gz", hash = "sha256:af377ffaee1dbe37ae9440cb4e8f11686ea5ce4e9bae01b84ae7c63b87f1dd3b", size = 102625 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/76/42/3efaf858001d2c2913de7f354563e3a3a2f0decae3efe98427125a8f441e/typer-0.16.0-py3-none-any.whl", hash = "sha256:1f79bed11d4d02d4310e3c1b7ba594183bcedb0ac73b27a9e5f28f6fb5b98855", size = 46317 },
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.14.0"
@@ -3412,6 +3700,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813", size = 128680 },
 ]
 
+[[package]]
+name = "uvicorn"
+version = "0.35.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/42/e0e305207bb88c6b8d3061399c6a961ffe5fbb7e2aa63c9234df7259e9cd/uvicorn-0.35.0.tar.gz", hash = "sha256:bc662f087f7cf2ce11a1d7fd70b90c9f98ef2e2831556dd078d131b96cc94a01", size = 78473 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/e2/dc81b1bd1dcfe91735810265e9d26bc8ec5da45b4c0f6237e286819194c3/uvicorn-0.35.0-py3-none-any.whl", hash = "sha256:197535216b25ff9b785e29a0b79199f55222193d47f820816e7da751e9bc8d4a", size = 66406 },
+]
+
 [[package]]
 name = "wadler-lindig"
 version = "0.1.6"

From acc43fb8b80e228ec3b703d950e521768e038488 Mon Sep 17 00:00:00 2001
From: Juan Pablo <juanpabloricapito@gmail.com>
Date: Fri, 8 Aug 2025 14:47:11 +0100
Subject: [PATCH 2/4] update tools with latest version

---
 mcp/server.py | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/mcp/server.py b/mcp/server.py
index 9174d495..c436e4f9 100644
--- a/mcp/server.py
+++ b/mcp/server.py
@@ -259,18 +259,18 @@ async def train_stamp(
 async def crossval_stamp(
     ctx: Context,
     output_dir: Annotated[
-        str, Field(description="Path of the directory to savethe results to")
+        str, Field(description="Path of the directory to save the results to")
     ],
     clini_table: Annotated[
-        str, Field(description="Path of aCSV or Excel to readclinical data from")
+        str, Field(description="Path of a CSV or Excel to read clinical data from")
     ],
     feature_dir: Annotated[
-        str, Field(description="Path of the directory containingfeature files")
+        str, Field(description="Path of the directory containing feature files")
     ],
     slide_table: Annotated[
         str,
         Field(
-            description="Path to a CSV or Excel to readpatient-slide associations from"
+            description="Path to a CSV or Excel to read patient-slide associations from"
         ),
     ],
     ground_truth_label: Annotated[
@@ -298,9 +298,12 @@ async def crossval_stamp(
             "in the slide table containing the feature file path relative to `feature_dir`"
         ),
     ] = "FILENAME",
-    n_folds: Annotated[
-        int, Field("Number of folds to split the data into for cross-validation")
-    ] = 3,
+    n_splits: Annotated[
+        int,
+        Field(
+            description="Number of folds to split the data into for cross-validation"
+        ),
+    ] = 5,
     bag_size: Annotated[
         int,
         Field(
@@ -332,12 +335,12 @@ async def crossval_stamp(
                 categories=["Positive", "Negative"],
                 patient_label="PATIENT",
                 filename_label="FILENAME",
-                n_folds=5
+                n_splits=5
             )
         "Command completed successfully: ..."
     """
     config = {
-        "training": {
+        "crossval": {  # Changed from "training" to "crossval"
             "output_dir": output_dir,
             "clini_table": clini_table,
             "feature_dir": feature_dir,
@@ -346,10 +349,12 @@ async def crossval_stamp(
             "categories": categories,
             "patient_label": patient_label,
             "filename_label": filename_label,
-            "n_folds": n_folds,
+            "n_splits": n_splits,
+        },
+        "advanced_config": {  # Add advanced config for bag_size and batch_size
             "bag_size": bag_size,
             "batch_size": batch_size,
-        }
+        },
     }
     return await _run_stamp(mode="crossval", config=config, ctx=ctx)
 

From 3967b7c1893a19ebcd83b91d81702cb8861028b6 Mon Sep 17 00:00:00 2001
From: Juan Pablo <juanpabloricapito@gmail.com>
Date: Mon, 11 Aug 2025 15:30:16 +0100
Subject: [PATCH 3/4] add fastmcp optional dep

---
 pyproject.toml |   3 +
 uv.lock        | 697 ++++++++++++++++++++++++++++++++++++++++++++++---
 2 files changed, 667 insertions(+), 33 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index a6273f26..de0fc0c6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -129,6 +129,9 @@ plip_cpu = [
 plip = [
     "stamp[plip_cpu, flash-attention]",
 ]
+mcp = [
+    "fastmcp>=2.11.2",
+]
 
 
 # Blanket target
diff --git a/uv.lock b/uv.lock
index fab90c4d..741dfa9e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -119,7 +119,7 @@ version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "frozenlist" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" }
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+[[package]]
+name = "anyio"
+version = "4.10.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
+wheels = [
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+]
+
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 name = "appnope"
 version = "0.1.4"
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 ]
 
+[[package]]
+name = "authlib"
+version = "1.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8e/a1/d8d1c6f8bc922c0b87ae0d933a8ed57be1bef6970894ed79c2852a153cd3/authlib-1.6.1.tar.gz", hash = "sha256:4dffdbb1460ba6ec8c17981a4c67af7d8af131231b5a36a88a1e8c80c111cdfd", size = 159988, upload-time = "2025-07-20T07:38:42.834Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f9/58/cc6a08053f822f98f334d38a27687b69c6655fb05cd74a7a5e70a2aeed95/authlib-1.6.1-py2.py3-none-any.whl", hash = "sha256:e9d2031c34c6309373ab845afc24168fe9e93dc52d252631f52642f21f5ed06e", size = 239299, upload-time = "2025-07-20T07:38:39.259Z" },
+]
+
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 name = "autograd"
 version = "1.8.0"
@@ -330,7 +356,7 @@ name = "click"
 version = "8.2.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/60/6c/8ca2efa64cf75a977a0d7fac081354553ebe483345c734fb6b6515d96bbc/click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202", size = 286342, upload-time = "2025-05-20T23:19:49.832Z" }
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+[[package]]
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+version = "45.0.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+]
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+    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/04/57/d062573f391d062710d4088fa1369428c38d51460ab6fedff920efef932e/starlette-0.47.2.tar.gz", hash = "sha256:6ae9aa5db235e4846decc1e7b79c4f346adf41e9777aebeb49dfd09bbd7023d8", size = 2583948, upload-time = "2025-07-20T17:31:58.522Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f7/1f/b876b1f83aef204198a42dc101613fefccb32258e5428b5f9259677864b4/starlette-0.47.2-py3-none-any.whl", hash = "sha256:c5847e96134e5c5371ee9fac6fdf1a67336d5815e09eb2a01fdb57a351ef915b", size = 72984, upload-time = "2025-07-20T17:31:56.738Z" },
+]
+
 [[package]]
 name = "sympy"
 version = "1.14.0"
@@ -3441,23 +4047,23 @@ dependencies = [
     { name = "fsspec" },
     { name = "jinja2" },
     { name = "networkx" },
-    { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cufile-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cusparselt-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "setuptools", marker = "python_full_version >= '3.12'" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cudnn-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cufft-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cufile-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-curand-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cusolver-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cusparse-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-cusparselt-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-nccl-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "nvidia-nvtx-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
+    { name = "setuptools", marker = "python_full_version >= '3.12' or (extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
     { name = "sympy" },
-    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "triton", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu') or (sys_platform != 'linux' and extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
     { name = "typing-extensions" },
 ]
 wheels = [
@@ -3546,7 +4152,7 @@ name = "tqdm"
 version = "4.67.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" }
 wheels = [
@@ -3649,7 +4255,7 @@ dependencies = [
     { name = "torchvision" },
     { name = "tqdm" },
     { name = "transformers" },
-    { name = "xformers", marker = "sys_platform != 'darwin'" },
+    { name = "xformers", marker = "sys_platform != 'darwin' or (extra == 'extra-5-stamp-cpu' and extra == 'extra-5-stamp-gpu')" },
 ]
 
 [[package]]
@@ -3661,6 +4267,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" },
 ]
 
+[[package]]
+name = "uvicorn"
+version = "0.35.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/42/e0e305207bb88c6b8d3061399c6a961ffe5fbb7e2aa63c9234df7259e9cd/uvicorn-0.35.0.tar.gz", hash = "sha256:bc662f087f7cf2ce11a1d7fd70b90c9f98ef2e2831556dd078d131b96cc94a01", size = 78473, upload-time = "2025-06-28T16:15:46.058Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/e2/dc81b1bd1dcfe91735810265e9d26bc8ec5da45b4c0f6237e286819194c3/uvicorn-0.35.0-py3-none-any.whl", hash = "sha256:197535216b25ff9b785e29a0b79199f55222193d47f820816e7da751e9bc8d4a", size = 66406, upload-time = "2025-06-28T16:15:44.816Z" },
+]
+
 [[package]]
 name = "wadler-lindig"
 version = "0.1.7"
@@ -3723,6 +4342,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d9/00/aca6beb3658dab4ed3dbb41a78e6e7f31342e0b41d28088f205525751601/webdataset-1.0.2-py3-none-any.whl", hash = "sha256:3dbfced32b25c0d199c6b9787937b6f85742bc3c84f652c846893075c1c082d9", size = 74956, upload-time = "2025-06-19T23:26:20.354Z" },
 ]
 
+[[package]]
+name = "werkzeug"
+version = "3.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/32/af/d4502dc713b4ccea7175d764718d5183caf8d0867a4f0190d5d4a45cea49/werkzeug-3.1.1.tar.gz", hash = "sha256:8cd39dfbdfc1e051965f156163e2974e52c210f130810e9ad36858f0fd3edad4", size = 806453, upload-time = "2024-11-01T16:40:45.462Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ee/ea/c67e1dee1ba208ed22c06d1d547ae5e293374bfc43e0eb0ef5e262b68561/werkzeug-3.1.1-py3-none-any.whl", hash = "sha256:a71124d1ef06008baafa3d266c02f56e1836a5984afd6dd6c9230669d60d9fb5", size = 224371, upload-time = "2024-11-01T16:40:43.994Z" },
+]
+
 [[package]]
 name = "wrapt"
 version = "1.17.2"

From 107c3d8a58dc619d1925f4cad6deff65d2fb858a Mon Sep 17 00:00:00 2001
From: Juan Pablo <juanpabloricapito@gmail.com>
Date: Mon, 11 Aug 2025 15:48:34 +0100
Subject: [PATCH 4/4] add fastmcp dependency

---
 mcp/README.md | 6 ++++++
 uv.lock       | 8 +++++---
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/mcp/README.md b/mcp/README.md
index ec915ba3..d7ee16f8 100644
--- a/mcp/README.md
+++ b/mcp/README.md
@@ -19,6 +19,12 @@ This server lets LLM agents invoke STAMP tools via structured calls. It exposes
 
 Each tool serializes config into YAML, launches `stamp <mode>`, streams logs back, and returns stdout/stderr.
 
+## Installation
+To run the MCP server is as simple as intalling STAMP as it is explained in the main README.md file, but adding `--extra mcp` to the command. For a GPU repository installation it would be like this:
+```bash
+uv sync --extra build --extra gpu --extra mcp
+```
+
 ## Example using OpenAI Agents SDK
 
 This example demonstrates how to connect an STAMP MCP server into an agent workflow using the official OpenAI Agents SDK v0.1.0
diff --git a/uv.lock b/uv.lock
index 741dfa9e..7f1fe21c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3669,7 +3669,6 @@ source = { editable = "." }
 dependencies = [
     { name = "beartype" },
     { name = "einops" },
-    { name = "fastmcp" },
     { name = "h5py" },
     { name = "jaxtyping" },
     { name = "lightning" },
@@ -3805,6 +3804,9 @@ gpu = [
 madeleine = [
     { name = "madeleine" },
 ]
+mcp = [
+    { name = "fastmcp" },
+]
 musk = [
     { name = "fairscale" },
     { name = "flash-attn" },
@@ -3859,7 +3861,7 @@ requires-dist = [
     { name = "environs", marker = "extra == 'prism-cpu'", specifier = "==11.0.0" },
     { name = "fairscale", marker = "extra == 'gigapath'" },
     { name = "fairscale", marker = "extra == 'musk-cpu'" },
-    { name = "fastmcp", specifier = ">=2.11.2" },
+    { name = "fastmcp", marker = "extra == 'mcp'", specifier = ">=2.11.2" },
     { name = "flash-attn", marker = "extra == 'flash-attention'", specifier = ">=2.8.2" },
     { name = "fvcore", marker = "extra == 'gigapath'" },
     { name = "gdown", marker = "extra == 'chief-ctranspath'", specifier = ">=5.2.0" },
@@ -3922,7 +3924,7 @@ requires-dist = [
     { name = "wandb", marker = "extra == 'gigapath'" },
     { name = "webdataset", marker = "extra == 'gigapath'" },
 ]
-provides-extras = ["build", "flash-attention", "conch", "conch1-5-cpu", "conch1-5", "ctranspath", "chief-ctranspath", "gigapath", "uni", "virchow2", "cobra", "prism-cpu", "prism", "madeleine", "musk-cpu", "musk", "plip-cpu", "plip", "cpu", "gpu", "all"]
+provides-extras = ["build", "flash-attention", "conch", "conch1-5-cpu", "conch1-5", "ctranspath", "chief-ctranspath", "gigapath", "uni", "virchow2", "cobra", "prism-cpu", "prism", "madeleine", "musk-cpu", "musk", "plip-cpu", "plip", "mcp", "cpu", "gpu", "all"]
 
 [package.metadata.requires-dev]
 dev = [