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Fledge MCP Server

@Krupalp525

代码仓库

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

Krupalp525

关于 Fledge MCP Server

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

#fledge#mcp#cursor-ai

Fledge MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Fledge MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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`.

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