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MCP File System API

@Vijayk-213

Repository

Basic information

Category

Developer Tools

Transports

stdio

Author

Vijayk-213

About MCP File System API

MCP implementation code that sets up the server, integrates the LLaMA model for summarization, and serves it via a Flask application.

MCP File System API config

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

{
  "mcpServers": {
    "Model-Context-Protocol": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

MCP File System API 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 🚀 MCP File System API?

This project implements an MCP (Model Context Protocol) server that allows interaction with a file system via HTTP requests using FastAPI. It supports file creation, reading, copying, moving, and deletion, and integrates with the Google Gemini API to process and summarize file contents.

How to use 🚀 MCP File System API?

Clone the repository, create and activate a Python 3.9+ virtual environment, then install dependencies from requirements.txt. Set the MCP_SERVER_URL and GEMINI_API_KEY environment variables in a .env file. Start the MCP server with uvicorn mcp_server:app --host 127.0.0.1 --port 8000 --reload, then run python main.py. Use the GET /read-text-from-file?file_path=... endpoint to read files and POST /invoke to call MCP functions.

Key features of 🚀 MCP File System API

  • Read .txt, .csv, .json, .xml, and .docx files.
  • Stream large files efficiently.
  • Integrate Google Gemini API for text summarization.
  • Cloud Run deployment support.

Use cases of 🚀 MCP File System API

  • Read and summarize text files using AI.
  • Manage file operations via an HTTP API.
  • Process various file formats programmatically.

Frequently asked questions

1

What file formats are supported?

The server supports .txt, .csv, .json, .xml, and .docx files.

2

What runtime and dependencies are required?

Python 3.9+ is required, along with FastAPI, Uvicorn, httpx, aiofiles, and the Google Gemini API library.

3

How do I obtain and configure the Gemini API key?

Set the `GEMINI_API_KEY` environment variable in your `.env` file with a valid key from Google.

4

Can this server be deployed to the cloud?

Yes, it includes Docker support and is designed for deployment on Google Cloud Run.

5

What transports and authentication are used?

The server uses HTTP transports (FastAPI). No built-in authentication is described beyond the Gemini API key used for summarization.

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