diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..40f9a2f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+invocation_reasons.yaml
+
diff --git a/README.md b/README.md
index 59887d6..e7aa74c 100644
--- a/README.md
+++ b/README.md
@@ -34,13 +34,35 @@ docker buildx build --platform linux/arm64,linux/amd64 -f mcp-local/Dockerfile -
 For a single-platform build (faster):
 
 ```bash
-docker buildx build -f mcp-local/Dockerfile -t arm-mcp mcp-local
+# NOTE - building locally involves generating the Knowledge Base semantic embeddings and can take up to 20 minutes
+docker buildx build -f mcp-local/Dockerfile -t arm-mcp mcp-local --load
 ```
 
 ### 2. Configure Your MCP Client
 
 Choose the configuration that matches your MCP client:
 
+#### Claude Code
+
+Add to `.mcp.json` in your project:
+
+```json
+{
+  "mcpServers": {
+    "arm-mcp": {
+      "command": "docker",
+      "args": [
+        "run",
+        "--rm",
+        "-i",
+        "-v", "/path/to/your/workspace:/workspace",
+        "arm-mcp"
+      ]
+    }
+  }
+}
+```
+
 #### GitHub Copilot (VS Code)
 
 Add to `.vscode/mcp.json` in your project, or globally at `~/Library/Application Support/Code/User/mcp.json` (macOS):
diff --git a/embedding-generation/generate-chunks.py b/embedding-generation/generate-chunks.py
index 7f777fe..6bd2845 100644
--- a/embedding-generation/generate-chunks.py
+++ b/embedding-generation/generate-chunks.py
@@ -12,12 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import requests
-from requests.adapters import HTTPAdapter
-from urllib3.util.retry import Retry
-from bs4 import BeautifulSoup
 import argparse
-import sys, os
+import sys
+import os
 import re
 import uuid
 import yaml
@@ -25,6 +22,14 @@
 import datetime
 import json
 
+import boto3
+from botocore.exceptions import NoCredentialsError, ClientError
+from bs4 import BeautifulSoup
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+
+
 # Create a session with retry logic for resilient HTTP requests
 def create_retry_session(retries=5, backoff_factor=1, status_forcelist=(500, 502, 503, 504)):
     """Create a requests session with automatic retry on failures."""
@@ -45,10 +50,6 @@ def create_retry_session(retries=5, backoff_factor=1, status_forcelist=(500, 502
 # Global session for all HTTP requests
 http_session = create_retry_session()
 
-# Boto3 for S3 operations
-import boto3
-from botocore.exceptions import NoCredentialsError, ClientError
-
 
 def ensure_intrinsic_chunks_from_s3(local_folder='intrinsic_chunks',
                                     s3_bucket='arm-github-copilot-extension',
@@ -57,7 +58,6 @@ def ensure_intrinsic_chunks_from_s3(local_folder='intrinsic_chunks',
     Ensure the local 'intrinsic_chunks' folder exists and is populated with files from S3.
     If the folder does not exist, create it and download all files from the S3 prefix.
     """
-    import os
     if not os.path.exists(local_folder):
         os.makedirs(local_folder, exist_ok=True)
         print(f"Created local folder: {local_folder}")
@@ -86,10 +86,8 @@ def ensure_intrinsic_chunks_from_s3(local_folder='intrinsic_chunks',
 To fix:
 1. Prevent multiple learning paths from being used (compare URLs to existing chunks OR delete overlaps)
 2. Learning Path titles must come from index page...send through function along with Graviton.
-
 '''
 
-
 yaml_dir = 'yaml_data'
 details_file = 'info/chunk_details.csv'
 
@@ -98,7 +96,6 @@ def ensure_intrinsic_chunks_from_s3(local_folder='intrinsic_chunks',
 # Global var to prevent duplication entries from cross platform learning paths
 cross_platform_lps_dont_duplicate = []
 
-
 # Increase the file size limit, which defaults to '131,072'
 csv.field_size_limit(10**9) #1,000,000,000 (1 billion), smaller than 64-bit space but avoids 'python overflowerror'
 
@@ -196,7 +193,6 @@ def createTextSnippet(main_row):
     return 
 
 
-
 def createIntrinsicsDatabaseChunks():
     def htmlToMarkdown(html_string):
         # Step 0: Remove '<h4>Operation</h4>' as it isn't needed
@@ -315,7 +311,6 @@ def htmlToMarkdown(html_string):
     '''
 
 
-
 def processLearningPath(url,type):
     github_raw_link = "https://raw.githubusercontent.com/ArmDeveloperEcosystem/arm-learning-paths/refs/heads/production/content"
     site_link = "https://learn.arm.com"
@@ -462,6 +457,7 @@ def readInCSV(csv_file):
 
     return csv_dict, csv_length
 
+
 def getMarkdownGitHubURLsFromPage(url):
     GH_urls = []
     SITE_urls = []
@@ -523,6 +519,7 @@ def obtainMarkdownContentFromGitHubMDFile(gh_url):
 
     return md_content
 
+
 def obtainTextSnippets__Markdown(content, min_words=300, max_words=500, min_final_words=200):
     """Split content into chunks based on headers and word count constraints."""
 
@@ -620,6 +617,7 @@ def createChunk(text_snippet,WEBSITE_url,keywords,title):
 
     return chunk
 
+
 def printChunks(chunks):
     for chunk_dict in chunks:
         print('='*100)
@@ -690,7 +688,6 @@ def recordChunk():
     print(f"{file_name} === {chunk.title}")
 
 
-
 def main():
     
 
diff --git a/embedding-generation/local_vectorstore_creation.py b/embedding-generation/local_vectorstore_creation.py
index 45e6ec5..08f5899 100644
--- a/embedding-generation/local_vectorstore_creation.py
+++ b/embedding-generation/local_vectorstore_creation.py
@@ -24,6 +24,7 @@
 from sentence_transformers import SentenceTransformer
 from usearch.index import Index
 
+
 def load_local_yaml_files() -> List[Dict]:
     """Load locally stored YAML files and return their contents as a list of dictionaries."""
     print("Loading local YAML files")
@@ -63,6 +64,7 @@ def load_local_yaml_files() -> List[Dict]:
     print(f"Successfully loaded {len(yaml_contents)} YAML files")
     return yaml_contents
 
+
 def create_embeddings(contents: List[str], model_name: str = 'all-MiniLM-L6-v2') -> np.ndarray:
     """Create embeddings for the given contents using SentenceTransformers."""
     print(f"Creating embeddings using model: {model_name}")
@@ -71,6 +73,7 @@ def create_embeddings(contents: List[str], model_name: str = 'all-MiniLM-L6-v2')
     print(f"Created embeddings with shape: {embeddings.shape}")
     return embeddings
 
+
 def create_usearch_index(embeddings: np.ndarray, metadata: List[Dict]) -> Tuple[Index, List[Dict]]:
     """Create a USearch index with the given embeddings and metadata."""
     print("Creating USearch index")
@@ -100,6 +103,7 @@ def create_usearch_index(embeddings: np.ndarray, metadata: List[Dict]) -> Tuple[
     print(f"Added {len(index)} vectors to the index")
     return index, metadata
 
+
 def main():
     print("Starting the USearch datastore creation process")