🚀 MCP File System API
About
MCP implementation code that sets up the server, integrates the LLaMA model for summarization, and serves it via a Flask application.
Details
- Author
- Vijayk-213
- GitHub stars
- 3
- Downloads
- 47
- Categories
- Developer Tools
Jump to
- Read .txt, .csv, .json, .xml, and .docx files.
- Stream large files efficiently.
- Integrate Google Gemini API for text summarization.
- Cloud Run deployment support.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
🚀 MCP File System APICommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"\ud83d\ude80 mcp file system api": {
"Model-Context-Protocol": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"Model-Context-Protocol": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}🚀 MCP File System API
📌 Overview
This project implements an MCP (Model Context Protocol) Server that allows interaction with a file system via HTTP requests. It supports file creation, reading, copying, moving, and deletion using FastAPI. Additionally, it integrates with Google Gemini API to process and summarize file contents.---
📂 Features
✅ Read various file formats (.txt, .csv, .json, .xml, .docx) ✅ Stream large files efficiently ✅ Integrate with Google Gemini API for text summarization ✅ Cloud Run deployment support---
🛠️ Tech Stack
- Python 3.9+ - FastAPI - MCP (Model Context Protocol) - Google Gemini API - Uvicorn (ASGI Server) - httpx (Async HTTP requests) - aiofiles (Async File Handling) - Docker & Cloud Run---
🚀 Getting Started
1️⃣ Clone the Repository
$ git clone https://github.com/Vijayk-213/Model-Context-Protocol.git
$ cd Model-Context-Protocol
2️⃣ Set Up a Virtual Environment
$ python3 -m venv venv
$ source venv/bin/activate # On Windows use venv\Scripts\activate
3️⃣ Install Dependencies
$ pip install -r requirements.txt
4️⃣ Set Environment Variables
Create a.env file and add your Google Gemini API Key:
MCP_SERVER_URL=http://127.0.0.1:8000
GEMINI_API_KEY=your_gemini_api_key
---
🔄 Running the Application
Start the MCP Server
$ uvicorn mcp_server:app --host 127.0.0.1 --port 8000 --reload
Run the Main Application
$ python main.py
---
📌 API Endpoints
| Method | Endpoint | Description |
|--------|----------------|---------------------------------|
| GET | /read-text-from-file?file_path=path.txt | Read file contents |
| POST | /invoke | Call MCP function |
---
🛠️ Future Enhancements
✅ Implement WebSockets for real-time file updates ✅ Add support for cloud storage (Google Cloud Storage, AWS S3) ✅ Improve error handling & logging---
📌 Contributing
Feel free to open issues or pull requests to improve the project!---
🚀 Happy Coding! 🎯
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