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Fledge MCP Server

@Krupalp525

Repository

Basic information

Category

Other

License

MIT

Transports

stdio

Author

Krupalp525

About Fledge MCP Server

Fledge Model Context Protocol (MCP) Server for Cursor AI integration

#fledge#mcp#cursor-ai

Fledge MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "fledge-mcp": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "fledge-mcp",
        "."
      ]
    }
  }
}

Fledge MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Fledge MCP Server?

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. It requires Fledge installed locally or accessible via API (default: http://localhost:8081).

How to use Fledge MCP Server?

Install dependencies with pip install -r requirements.txt, ensure Fledge is running (fledge start), then launch the server with python mcp_server.py or python secure_mcp_server.py for API‑key authentication. In Cursor AI, add an MCP server with URL http://localhost:8082/tools and optionally the X-API-Key header. Test by typing “Check if Fledge API is reachable” in Cursor’s Composer.

Key features of Fledge MCP Server

  • Exposes 16 tools for data access, service control, and UI generation
  • Supports secure API key authentication via secure_mcp_server.py
  • Implements JSON-RPC 2.0 over WebSocket transport
  • Generates React components for Fledge data visualization
  • Enables real-time sensor data subscriptions and latest readings
  • Can be deployed on Smithery.ai for enhanced scalability

Use cases of Fledge MCP Server

  • Validate Fledge API connectivity using natural language commands
  • Fetch and filter sensor data for analysis or visualization
  • Generate mock sensor data for testing and development
  • Start or stop Fledge services via Cursor AI commands
  • Create frontend UI components for Fledge dashboards

Frequently asked questions

1

What are the prerequisites?

Fledge installed locally or accessible via API, Cursor AI, and Python 3.8+.

2

How does secure mode work?

Run `python secure_mcp_server.py`. It generates an API key stored in `api_key.txt`. All requests must include the `X-API-Key` header; the health endpoint remains unauthenticated.

3

What transport does the server use?

The server uses WebSocket transport and the Model Context Protocol implemented via JSON-RPC 2.0.

4

Can it be deployed to the cloud?

Yes, it can be deployed on Smithery.ai using Docker and the `smithery` CLI, with configurable environment variables for the Fledge API URL and API key.

5

How to add new tools?

Add the tool definition to `tools.json` and implement the handler in both `mcp_server.py` and `secure_mcp_server.py`.

Comments

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