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Fledge MCP Server

by Krupalp525

IntegrationFledgeCursor AIData ManagementAutomation

The Fledge MCP Server connects Fledge functionality to Cursor AI, enabling interaction with Fledge instances through natural language commands. It acts as a bridge, allowing AI to access and manage Fledge data and services.

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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:
git clone https://github.com/Krupalp525/fledge-mcp.git
cd fledge-mcp
  1. Install the dependencies:
pip install -r requirements.txt

Running the Server

  1. Make sure Fledge is running:
fledge start
  1. Start the MCP server:
python mcp_server.py

For secure operation with API key authentication:

python secure_mcp_server.py
  1. Verify it's working by accessing the health endpoint:
curl http://localhost:8082/health

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

  1. get_service_status: Get the status of all Fledge services
  2. start_stop_service: Start or stop a Fledge service by type
  3. update_config: Update Fledge configuration parameters

Frontend Code Generation

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

Real-Time Data Streaming

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

Debugging and Validation

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

Documentation and Schema

  1. get_api_schema: Get information about available Fledge API endpoints
  2. list_plugins: List available Fledge plugins

Advanced AI-Assisted Features

  1. generate_mock_data: Generate realistic mock sensor data for testing

Testing the API

You can test the server using the included test scripts:

# For standard server
python test_mcp.py

# For secure server with API key
python test_secure_mcp.py

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

# Validate API connection
curl -X POST -H "Content-Type: application/json" -d '{"name": "validate_api_connection"}' http://localhost:8082/tools

# Generate mock data
curl -X POST -H "Content-Type: application/json" -d '{"name": "generate_mock_data", "parameters": {"sensor_id": "temp1", "count": 5}}' http://localhost:8082/tools

# Generate React chart component
curl -X POST -H "Content-Type: application/json" -d '{"name": "generate_ui_component", "parameters": {"component_type": "chart", "sensor_id": "temp1"}}' http://localhost:8082/tools

# For secure server, add API key header
curl -X POST -H "Content-Type: application/json" -H "X-API-Key: YOUR_API_KEY" -d '{"name": "list_sensors"}' http://localhost:8082/tools

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