How do I use one-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@one-mcp Search for any tools related to financial data or stock prices" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🧠 one-mcp

🚀 Overview

one-mcp is a lightweight MCP (Model Context Protocol) server built using FastAPI that enables intelligent tool management and semantic search for APIs. It allows you to upload, manage, and query API tools using natural language — powered by modern embedding models via sentence-transformers.

The server supports multiple transport modes (stdio, HTTP, or both) and provides both a REST API and MCP tool interface for maximum flexibility.

✨ Features

🔍 Semantic Search: Find relevant API tools based on descriptive queries using sentence-transformers embeddings.
📤 Upload Tools: Add new API tools via JSON body or file upload.
🗑️ Delete Tools: Remove specific tools by name (supports batch deletion).
🧾 Tool Statistics: Get insights on stored tools including count, model, and storage path.
🧹 Tool Management: Clear, inspect, or modify your tool store easily.
⚡ FastAPI Backend: High-performance, async-ready backend server.
🤝 MCP Compatibility: Dual interface - REST API and MCP tools for seamless integration.
🔄 Dual Transport: Support for stdio and HTTP transports simultaneously.
💾 Persistent Storage: Tools and embeddings saved to disk with automatic loading.
📊 Structured Logging: Comprehensive logging with rotating file handlers.

🧩 Project Structure

one-mcp/ ├── server.py # Main application entry point with server orchestration ├── mcp_server.py # MCP server class with multi-transport support ├── api.py # FastAPI routes and REST endpoints ├── mcp_tools.py # MCP tool definitions and handlers ├── models.py # Pydantic models for request/response validation ├── tools_store.py # Persistent tool storage with embeddings ├── config.py # Server configuration and argument parsing ├── logging_setup.py # Centralized logging configuration ├── test_specs.json # Sample tool dataset for testing ├── CURLS.md # Example cURL commands for testing API endpoints ├── MCP_TOOLS.md # MCP tools documentation ├── requirements.txt # Project dependencies ├── Dockerfile # Docker containerization (CPU-based dependencies) └── README.md # Project documentation (this file)

⚙️ Installation

1. Clone the Repository

git clone https://github.com/freakynit/one-mcp.git cd one-mcp

2. Set Up Virtual Environment

python -m venv venv source venv/bin/activate # macOS/Linux venv\Scripts\activate # Windows

3. Install Dependencies

pip install -r requirements.txt

Dependencies include:

fastapi>=0.104.0 uvicorn>=0.24.0 fastmcp>=0.2.0 python-multipart>=0.0.6 torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 sentence-transformers>=2.2.0 scikit-learn>=1.3.0 numpy>=1.24.0

🧠 Running the Server

Note: The first time you run the server, it will download the all-MiniLM-L6-v2 model from sentence-transformers. This may take a few seconds depending on your internet connection.

Start with Dual Transport (stdio + HTTP)

python server.py --transport stdio,http --port 8003

This enables both MCP stdio communication and HTTP REST API access.

HTTP-only Mode

python server.py --transport http --port 8003

Stdio-only Mode (for MCP clients)

python server.py --transport stdio

Using Uvicorn Directly

uvicorn server:app --host 0.0.0.0 --port 8003

Configuration Options

--transport: Transport mode (stdio, http, or stdio,http) - default: stdio
--port: HTTP port number - default: 8000
--host: Host to bind to - default: 0.0.0.0
--storage_path: Path to store tool embeddings - default: tool_embeddings.json

By default, the server starts at: 👉 http://localhost:8003 (when HTTP transport is enabled)

The server automatically:

Creates a logs/ directory for application logs
Loads existing tools from tool_embeddings.json on startup
Saves tools to disk after any modification

🧪 Testing the API

The server provides two interfaces:

REST API: Available at /api/* endpoints (see CURLS.md for examples)
MCP Tools: Available via MCP protocol (see MCP_TOOLS.md for documentation)

REST API Endpoints

All endpoints return structured JSON responses with appropriate status codes.

Check Server Status

curl http://localhost:8003/api/status

Upload Tools via JSON

curl -X POST http://localhost:8003/api/tools/upload-json \ -H "Content-Type: application/json" \ -d '{"tools": [{"type": "function", "name": "get_weather", "description": "Get the current weather for a specific city.", "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The name of the city to get weather for."}}}}]}'

Upload Tools via File

curl -X POST http://localhost:8003/api/tools/upload-file \ -F "file=@test_tools.json;type=application/json"

Search for Similar Tools

curl -X POST http://localhost:8003/api/tools/search \ -H "Content-Type: application/json" \ -d '{"query": "weather forecast for a city", "k": 3}'

Get Statistics

curl http://localhost:8003/api/tools/stats

Delete Specific Tools

curl -X DELETE http://localhost:8003/api/tools/delete \ -H "Content-Type: application/json" \ -d '{"tool_names": ["get_weather", "get_news_headlines"]}'

Clear All Tools

curl -X DELETE http://localhost:8003/api/tools/clear

MCP Access

The MCP endpoint is mounted at /mcp for HTTP streaming mode:

curl http://localhost:8003/mcp

For full MCP tool documentation, see MCP_TOOLS.md.

For more comprehensive testing examples, see CURLS.md.

🧰 Example MCP Configuration

To integrate with an MCP client (like Claude Desktop):

{ "mcpServers": { "one-mcp-server": { "command": "python", "args": [ "/absolute/path/to/server.py", "--transport", "stdio", "--storage_path", "tool_embeddings.json" ] } } }

For dual transport mode (stdio for MCP + HTTP for REST API):

{ "mcpServers": { "one-mcp-server": { "command": "python", "args": [ "/absolute/path/to/server.py", "--transport", "stdio,http", "--port", "8004", "--storage_path", "tool_embeddings.json" ] } } }

🏗️ Architecture

Components

server.py: Entry point that initializes the app and starts the MCP server
mcp_server.py: Handles multi-transport server orchestration (stdio/HTTP/dual)
api.py: FastAPI application factory and REST endpoint definitions
mcp_tools.py: MCP tool decorators and function implementations
tools_store.py: Singleton store for tool embeddings with search capability
models.py: Pydantic models for type safety and validation
config.py: Configuration management and CLI argument parsing
logging_setup.py: Centralized logging with rotating file handlers

How It Works

Tool Storage: Tools are stored with their embeddings using sentence-transformers
Semantic Search: Query embeddings are compared using cosine similarity
Persistence: Tools automatically saved to tool_embeddings.json
Dual Interface: Same functionality available via REST API and MCP tools
Multi-Transport: Server can run stdio (for MCP clients) and HTTP simultaneously

Dev

Create zip: zip -r one-mcp.zip . -x "*.git/*" -x ".env" -x ".DS_Store" -x ".dockerignore" -x ".gitignore"

🧑‍💻 Contributing

Contributions are welcome! To contribute:

Fork the repository
Create a new feature branch (git checkout -b feature/my-feature)
Commit your changes (git commit -m "Add my feature")
Push to your fork (git push origin feature/my-feature)
Submit a Pull Request

Before submitting, ensure:

Code passes linting and basic tests.
You’ve updated documentation if needed.

📜 License

This project is licensed under the MIT License — see the LICENSE file for details.

💬 Support

If you encounter any issues or have feature requests:

Open an issue on GitHub
Or contact @freakynit directly.

one-mcp