iptv000iptv/mcp-server
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The Model Context Protocol (MCP) Server is a modular, FastAPI-based server designed for agent-based systems and orchestration tools, supporting local JSON and Excel file reading.
🧠 Model Context Protocol (MCP) Server
This project is a modular, FastAPI-based MCP server designed for use in agent-based systems and orchestration tools like Flowsie AI and n8n. It currently supports reading local JSON and Excel files and returns the data in JSON format.
📁 Project Structure
mcp_project/
├── app/
│ ├── configs/ # App configuration settings
│ ├── local_data/ # Folder to store local JSON/Excel files
│ ├── models/ # (Reserved for data models)
│ ├── routers/ # FastAPI route handlers
│ ├── services/ # Business logic (file readers, connectors)
│ ├── utils/ # Utility functions (logging, parsing)
│ └── main.py # FastAPI app entry point
├── Dockerfile # Docker setup
└── requirements.txt # Python dependencies
🚀 Getting Started
1. Install dependencies
Make sure you have Python 3.10+ installed:
pip install -r requirements.txt
2. Run the app locally
uvicorn app.main:app --reload --port 8000
Server will be available at: http://localhost:8000/docs
3. Example Usage
Endpoint: GET /local/read
Query Parameter:
filename: Name of the file insideapp/local_data/(e.g.,sample.json,data.xlsx)
curl -X 'GET' 'http://localhost:8000/local/read?filename=sample.json' -H 'accept: application/json'
🐳 Docker Usage
Build Docker Image
docker build -t mcp-server .
Run the Container
docker run -p 8000:8000 mcp-server
Then access the server at: http://localhost:8000/docs
📦 File Support
The local file reader MCP server supports:
.json— Standard structured JSON.xlsx/.xls— Excel files (auto-converted to JSON)
Returned format is always a JSON list or dict.
🛠 Future Modules (Suggested)
design_system_connector: For querying design metadatagmail_connector: Secure access to Gmail via OAuthcontext_session_handler: Track and manage agent sessions
📄 License
MIT License — use freely and modify as needed.
✨ Author
This project was generated via [ChatGPT-4] and customized for real-world MCP use cases.