Add blog post: Introducing SignalK MCP Server

Introduces the signalk-mcp-server project, a Model Context Protocol
server that enables AI assistants like Claude to interact with SignalK
marine data systems through natural language.

Key features:
- Real-world usage examples from the Satori project
- 89 conversations and 252 tool calls analyzed
- Demonstrates conversational boat monitoring approach
- Tutorial-style guide for getting started
- Emphasizes read-only safety and security

Includes context document with research findings and conversation
analysis for future reference.

Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>

Author: 2 people

BLOG_CONTEXT.md

@@ -0,0 +1,334 @@
+# Blog Post Context: SignalK MCP Server
+
+## Project Overview
+
+This document contains research findings for creating a blog post about the SignalK MCP Server project.
+
+## Repository Information
+
+### Upstream Repository
+
+- **URL**: https://github.com/SignalK/signalk.github.io
+- **Owner**: SignalK organization
+- **Current Fork**: https://github.com/tonybentley/signalk.github.io.git
+
+### Contributing Process
+
+1. Website built with Astro and hosted on GitHub Pages
+2. Local development requires Node.js
+3. Run locally: `npm install` then `npm start` (serves at localhost:4321)
+4. Code quality: `npm run fix` to auto-fix errors and check for issues
+5. Blog posts are created using: `script/generate "Title of the new post"`
+   - Creates file in `_posts` with current date
+   - Uses template from `docs/blog-template.md`
+   - Generates `.mdx` files (not `.md`)
+6. Workflow: Write draft → Open pull request → Get reviewed
+
+## Blog Post Template Structure
+
+```yaml
+---
+publishDate: { date }
+title: { title }
+author: Your Full Name
+# image: ~/assets/images/your-image.png or full URL (e.g., from unsplash.com)
+category: Release | Tutorial | Case Study
+tags: [tag1, tag2]
+description: '1-2 sentence headline that becomes the first paragraph'
+---
+```
+
+## Example Blog Post Analysis
+
+### Recent Posts Reviewed
+
+1. **KIP 3.0.0** (\_posts/2025/kip-3.0.0.md)
+
+   - Category: Release
+   - Format: Feature list with emoji headers
+   - Includes screenshots
+   - Links to GitHub releases
+   - Technical but accessible
+
+2. **TWatchSK Announcement** (\_posts/2022/twatchsk-announcement.md)
+   - Category: Ecosystem/Hardware
+   - Personal narrative style
+   - Multiple images showing UI
+   - Detailed feature walkthrough
+   - Author included email and gravatar
+
+### Writing Style Patterns
+
+- Technical but approachable
+- Use of specific examples and scenarios
+- Screenshots/images to illustrate features
+- Links to relevant resources
+- Community-focused language
+- Clear structure with headers
+- Practical use cases highlighted
+
+## SignalK MCP Server Project Details
+
+### Description
+
+The SignalK MCP (Model Context Protocol) Server provides AI agents with read-only access to marine data systems. Enables AI assistants like Claude to query vessel navigation data, monitor AIS targets, and access system alarms from SignalK installations.
+
+### Key Features
+
+- Real-time vessel data access
+- AIS target monitoring
+- System notifications tracking
+- Live data streams
+- Path discovery
+- Connection monitoring
+- Automatic reconnection
+
+### Technical Requirements
+
+- Node.js 18.0.0 or higher
+- Access to a SignalK server (local or remote)
+
+### Installation Options
+
+1. **Recommended**: Use npx
+
+   ```bash
+   npx signalk-mcp-server
+   ```
+
+2. **Global installation**
+   ```bash
+   npm install -g signalk-mcp-server
+   ```
+
+### Available Tools/Commands
+
+- `get_initial_context()`: Provides comprehensive system overview
+- `get_vessel_state()`: Retrieves current vessel navigation data
+- `get_ais_targets()`: Monitors nearby vessel information
+- `get_active_alarms()`: Tracks system notifications
+- `list_available_paths()`: Discovers SignalK data paths
+
+### Configuration
+
+- Uses environment variables
+- Options for SignalK host/port
+- Connection settings
+- Authentication support
+- Logging preferences
+
+### Security & Safety
+
+- Read-only operations
+- No device control capabilities
+- Graceful error handling
+- Robust connection validation
+
+### Integration
+
+- Works with Claude Desktop
+- Compatible with Claude Code CLI
+- Flexible for different development scenarios
+- Marine monitoring use cases
+
+## Blog Post Strategy
+
+### Target Audience
+
+- SignalK users interested in AI/automation
+- Developers exploring MCP protocol
+- Marine enthusiasts wanting AI-assisted navigation
+- Technical users comfortable with CLI tools
+
+### Key Messages
+
+1. First MCP server for marine data
+2. Brings AI assistance to SignalK ecosystem
+3. Safe, read-only access to vessel data
+4. Easy to install and configure
+5. Opens new possibilities for AI-assisted navigation
+
+### Potential Categories
+
+- Tutorial (if we focus on how-to)
+- Release/Announcement (new tool announcement)
+- Ecosystem (fits with other SignalK blog posts)
+
+### Suggested Tags
+
+- MCP, AI, Claude, ecosystem, navigation, developer-tools
+
+### Content Outline
+
+1. Introduction: What is MCP and why it matters for marine
+2. What the SignalK MCP Server does
+3. Key features and capabilities
+4. Installation and setup
+5. Practical use cases/examples
+6. Security considerations
+7. Getting started/next steps
+8. Community call-to-action
+
+## Real-World Usage: The Satori Project
+
+### Project Background
+
+**Satori** is a 1977 Westsail 32 sailboat (MMSI: 367603940) based in San Diego, CA. The owner uses the SignalK MCP server to enable AI-assisted boat monitoring and management through Claude Desktop.
+
+### Conversational Boat Monitoring
+
+The project demonstrates a unique use case: treating the boat as an entity that can be queried conversationally. Users can ask questions like "How is Satori?" and receive comprehensive status reports based on:
+
+- Real-time SignalK data paths (vessel position, battery voltage, solar output, etc.)
+- Historical time-series data from InfluxDB
+- System alarms and notifications
+- Environmental conditions (inside temp, fridge temp, bilge status)
+- Engine parameters (when running)
+- Tank levels (fresh water, holding tank)
+
+### System Context Document
+
+The Satori project maintains a comprehensive system context document (`satori-system-context.md`) that provides:
+
+- Complete vessel specifications and systems inventory
+- SignalK path references for all monitored systems
+- Electrical system details (solar, batteries, charging)
+- Historical performance data
+- Operational guidelines and known issues
+
+### MCP Tools Integration
+
+The SignalK MCP server provides tools that Claude uses to:
+
+1. **get_initial_context()** - Get comprehensive overview of boat systems
+2. **get_vessel_state()** - Check current navigation/position data
+3. **get_ais_targets()** - Monitor nearby vessels
+4. **get_active_alarms()** - Review system alerts
+5. **list_available_paths()** - Discover available data streams
+
+### Practical Example Queries
+
+The Satori project demonstrates natural language queries like:
+
+- "How is Satori?" → AI retrieves battery levels, solar charging status, tank levels
+- "Is the battery charging properly?" → AI checks voltage trends, solar output, charger status
+- "What's the fridge temperature?" → AI reads environmental sensors
+- "Show me power consumption over the last 24 hours" → AI queries InfluxDB time-series data
+
+### Multi-System Integration
+
+Satori's implementation combines:
+
+- **2x Raspberry Pi computers**:
+  - NMEA Pi: OpenPlotter + InfluxDB + MCP server
+  - Venus Pi: VenusOS (Victron) for battery monitoring
+- **Starlink**: Remote connectivity (24/7 at dock)
+- **SignalK**: Central data hub aggregating NMEA 0183/2000
+- **InfluxDB**: Time-series storage for historical analysis
+- **ESP32 sensors**: Custom environmental and bilge monitoring
+
+### Key Insight for Blog
+
+This demonstrates the transformative potential of MCP + SignalK: instead of checking multiple dashboards and graphs, boat owners can have natural conversations with AI about their vessel's status, receiving intelligent analysis and proactive alerts.
+
+## Detailed Conversation Examples from Satori
+
+### Statistics from Analysis
+
+- **89 conversations** about Satori/SignalK (out of 571 total)
+- **252 SignalK tool calls** made
+- **192 InfluxDB tool calls** for historical data
+- Conversations span June-October 2025
+
+### Example 1: Natural Status Check
+
+**User Query:** "how is satori doing today? batteries, and environment conditions?"
+
+**Tools Called:**
+
+- `signalk:get_vessel_state` - Overall status
+- `signalk:list_available_paths` - Discover available data
+- Multiple `signalk:get_path_value` calls for specific metrics
+
+**AI Response Included:**
+
+- Battery: 100% state of charge, 13.45V, 13.4A draw
+- Solar: 5.44 kWh generated today (combined from 2 controllers)
+- Environment: 26.8°C, 42.3% humidity
+- Bilge: Both forward and aft dry (no flooding)
+
+**Key Takeaway:** Single natural question triggers comprehensive system analysis across multiple data sources.
+
+### Example 2: Historical Battery Analysis
+
+**User Query:** "how has the main battery doing. was it charged today?"
+
+**What Happened:** AI seamlessly switched from real-time SignalK to historical InfluxDB data to show battery's journey through the day:
+
+- Started at 99.8% at midnight
+- Dropped to 96.9% by 2:30 AM (overnight discharge)
+- Charging began at 3:00 AM
+- Reached 100% by 4:30 AM and stayed there
+
+**Key Takeaway:** MCP enables narrative analysis of time-series data, not just raw numbers.
+
+### Example 3: Solar Comparison
+
+**User Query:** "how much solar energy did satori get today compared to yesterday?"
+
+**Result:** AI automatically queried both controllers and provided:
+
+- Today: 5.44 kWh total
+- Yesterday: 6.12 kWh total
+- Difference: -0.68 kWh (11% less, likely due to weather)
+
+**Key Takeaway:** Comparative analysis without writing queries or opening dashboards.
+
+### Example 4: Situational Awareness
+
+**User Query:** "what is the nearest vessel to satori right now"
+
+**What Happened:**
+
+- Queried AIS targets via SignalK
+- Got Satori's current position
+- Calculated distances using Haversine formula
+- Identified nearest vessel: MMSI 338311884, only 51 meters away
+
+**Key Takeaway:** Complex spatial calculations and contextual analysis from simple natural language query.
+
+### Example 5: Anomaly Detection
+
+**User Query:** "how is satori doing? check for the past three weeks for any anomolies"
+
+**What Happened:**
+
+- Found active "Aft bilge has water" alarm
+- Queried 3 weeks of historical data (bilge sensors, battery, power)
+- User clarified alarm was brief/false
+- AI provided context showing no pattern of issues
+
+**Key Takeaway:** Combines real-time alarms with historical patterns to distinguish false alarms from real problems.
+
+### Common Query Patterns Observed
+
+1. **Morning check-ins:** "How is Satori?" - overnight charging, weather
+2. **Comparative analysis:** "Today vs yesterday" - solar, power consumption
+3. **Troubleshooting:** Checking alarms, investigating anomalies
+4. **Environmental monitoring:** Temperature, humidity trends
+5. **Situational awareness:** Nearby vessels, position data
+
+### Most Frequently Accessed SignalK Paths
+
+- `electrical.batteries.*.voltage`
+- `electrical.batteries.*.capacity.stateOfCharge`
+- `electrical.solar.*.yieldToday`
+- `environment.inside.temperature`
+- `environment.inside.humidity`
+- `environment.bilge.*.flood`
+- `navigation.position`
+- `navigation.speedOverGround`
+
+### The "Satori Effect"
+
+Users develop a conversational relationship with their boat. Instead of "checking systems," they're "asking how Satori is doing" - a fundamentally different interaction model that's more intuitive and human.