Fledge MCP Server

This is a Model Context Protocol (MCP) server that connects Fledge functionality to Cursor AI, allowing the AI to interact with Fledge instances via natural language commands.

Prerequisites

• Fledge installed locally or accessible via API (default: http://localhost:8081)
• Cursor AI installed
• Python 3.8+

Installation

1. Clone this repository:

2. Install the dependencies:

Running the Server

1. Make sure Fledge is running:

2. Start the MCP server:

For secure operation with API key authentication:

3. Verify it's working by accessing the health endpoint:

You should receive "Fledge MCP Server is running" as the response.

Connecting to Cursor

1. In Cursor, go to Settings > MCP Servers
2. Add a new server:
   • URL: http://localhost:8082/tools
   • Tools file: Upload the included tools.json or point to its local path
3. For the secure server, configure the "X-API-Key" header with the value from the api_key.txt file that is generated when the secure server starts.
4. Test it: Open Cursor's Composer (Ctrl+I), type "Check if Fledge API is reachable," and the AI should call the validate_api_connection tool.

Available Tools

Data Access and Management

1. get_sensor_data: Fetch sensor data from Fledge with optional filtering by time range and limit
2. list_sensors: List all sensors available in Fledge
3. ingest_test_data: Ingest test data into Fledge, with optional batch count

Service Control

4. get_service_status: Get the status of all Fledge services
5. start_stop_service: Start or stop a Fledge service by type
6. update_config: Update Fledge configuration parameters

Frontend Code Generation

7. generate_ui_component: Generate React components for Fledge data visualization
8. fetch_sample_frontend: Get sample frontend templates for different frameworks
9. suggest_ui_improvements: Get AI-powered suggestions for improving UI code

Real-Time Data Streaming

10. subscribe_to_sensor: Set up a subscription to sensor data updates
11. get_latest_reading: Get the most recent reading from a specific sensor

Debugging and Validation

12. validate_api_connection: Check if the Fledge API is reachable
13. simulate_frontend_request: Test API requests with different methods and payloads

Documentation and Schema

14. get_api_schema: Get information about available Fledge API endpoints
15. list_plugins: List available Fledge plugins

Advanced AI-Assisted Features

16. generate_mock_data: Generate realistic mock sensor data for testing

Testing the API

You can test the server using the included test scripts:

Security Options

The secure server (secure_mcp_server.py) adds API key authentication:

1. On first run, it generates an API key stored in api_key.txt
2. All requests must include this key in the X-API-Key header
3. Health check endpoint remains accessible without authentication

Example API Requests

Extending the Server

To add more tools:

1. Add the tool definition to tools.json
2. Implement the tool handler in mcp_server.py and secure_mcp_server.py

Production Considerations

For production deployment:

• Use HTTPS
• Deploy behind a reverse proxy like Nginx
• Implement more robust authentication (JWT, OAuth)
• Add rate limiting
• Set up persistent data storage for subscriptions

Deploying on Smithery.ai

The Fledge MCP Server can be deployed on Smithery.ai for enhanced scalability and availability. Follow these steps to deploy:

1. Prerequisites
   • Docker installed on your local machine
   • A Smithery.ai account
   • The Smithery CLI tool installed
2. Build and Deploy
   # Build the Docker image docker build -t fledge-mcp . # Deploy to Smithery.ai smithery deploy
3. Configuration The smithery.json file contains the configuration for your deployment:
   • WebSocket transport on port 8082
   • Configurable Fledge API URL
   • Tool definitions and parameters
   • Timeout settings
4. Environment Variables Set the following environment variables in your Smithery.ai dashboard:
   • FLEDGE_API_URL: Your Fledge API endpoint
   • API_KEY: Your secure API key (if using secure mode)
5. Verification After deployment, verify your server is running:
   smithery status fledge-mcp
6. Monitoring Monitor your deployment through the Smithery.ai dashboard:
   • Real-time logs
   • Performance metrics
   • Error tracking
   • Resource usage
7. Updating To update your deployment:
   # Build new image docker build -t fledge-mcp . # Deploy updates smithery deploy --update

JSON-RPC Protocol Support

The server implements the Model Context Protocol (MCP) using JSON-RPC 2.0 over WebSocket. The following methods are supported:

1. initialize
   { "jsonrpc": "2.0", "method": "initialize", "params": {}, "id": "1" }
   Response:
   { "jsonrpc": "2.0", "result": { "serverInfo": { "name": "fledge-mcp", "version": "1.0.0", "description": "Fledge Model Context Protocol (MCP) Server", "vendor": "Fledge", "capabilities": { "tools": true, "streaming": true, "authentication": "api_key" } }, "configSchema": { "type": "object", "properties": { "fledge_api_url": { "type": "string", "description": "Fledge API URL", "default": "http://localhost:8081/fledge" } } } }, "id": "1" }
2. tools/list
   { "jsonrpc": "2.0", "method": "tools/list", "params": {}, "id": "2" }
   Response: Returns the list of available tools and their parameters.
3. tools/call
   { "jsonrpc": "2.0", "method": "tools/call", "params": { "name": "get_sensor_data", "parameters": { "sensor_id": "temp1", "limit": 10 } }, "id": "3" }

Error Codes

The server follows standard JSON-RPC 2.0 error codes:

• -32700: Parse error
• -32600: Invalid Request
• -32601: Method not found
• -32602: Invalid params
• -32000: Server error