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

2. Install the dependencies:

pip install -r requirements.txt

Running the Server

1. Make sure Fledge is running:

fledge start

2. Start the MCP server:

python mcp_server.py

For secure operation with API key authentication:

python secure_mcp_server.py

3. 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

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:

# 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