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RapidAPI-MCP

MCP.Pizza Chef: myownipgit

RapidAPI-MCP is an MCP Server implementation designed to interface seamlessly with the RapidAPI Global Patent API. It enables efficient retrieval and management of patent data, storing it securely in a SQLite database. The server supports advanced patent scoring metrics including pscore, cscore, lscore, and tscore, enhancing patent analysis capabilities. It also incorporates robust rate limiting and error handling to ensure reliable API interactions. Ideal for developers building AI workflows that require real-time patent data access and structured storage.

Use This MCP server To

Integrate patent data into AI workflows via RapidAPI Store and query patent information using SQLite Analyze patents with advanced scoring metrics Handle patent API requests with rate limiting Build AI agents accessing global patent databases

README

RapidAPI MCP Server

This repository contains an implementation of an MCP Server for interfacing with the RapidAPI Global Patent API and storing patent data in a SQLite database.

Features

  • RapidAPI Global Patent API integration
  • MCP Server implementation for handling patent requests
  • SQLite database integration for patent data storage
  • Advanced patent scoring system (pscore, cscore, lscore, tscore)
  • Rate limiting and error handling

Installation

Using Conda (Recommended)

  1. Clone the repository:
git clone https://github.com/myownipgit/RapidAPI-MCP.git
cd RapidAPI-MCP
  1. Create and activate conda environment:
# Create environment from yml file
conda env create -f environment.yml

# Activate environment
conda activate rapidapi-mcp

Alternatively, you can create the environment manually:

# Create new environment with Python 3.11
conda create -n rapidapi-mcp python=3.11

# Activate environment
conda activate rapidapi-mcp

# Install required packages
conda install -c conda-forge requests aiohttp python-dotenv pytest
pip install rapidapi-connect
  1. Set up environment variables:
cp .env.example .env
# Edit .env with your settings

Usage

  1. Initialize the MCP Server:
from patent_mcp.server import MCPPatentServer

mcp_server = MCPPatentServer()
  1. Handle patent search requests:
search_request = {
    'command': 'search',
    'params': {
        'query': 'quantum computing',
        'date_range': '2004-2024',
        'page': 1,
        'per_page': 100
    }
}

results = await mcp_server.handle_patent_request(search_request)

Testing

To run the tests, activate your conda environment and run:

# Run the connection test
python tests/test_connection.py

# Run all tests with pytest
python -m pytest tests/

Project Structure

  • patent_mcp/ - Main package directory
    • client.py - RapidAPI client implementation
    • server.py - MCP Server implementation
    • database.py - SQLite database operations
    • scoring.py - Patent scoring system
    • __init__.py - Package initialization
  • docs/ - Documentation
    • SCORING.md - Detailed scoring methodology
  • examples/ - Example scripts
  • tests/ - Unit tests

Requirements

  • Python 3.11 or higher
  • Required packages are listed in environment.yml

Scoring System

The system implements a comprehensive patent scoring methodology:

  • Patent Score (pscore): Overall patent strength
  • Citation Score (cscore): Citation impact analysis
  • Legal Score (lscore): Legal status evaluation
  • Technology Score (tscore): Technical complexity assessment

See SCORING.md for detailed information.

Configuration

The server uses the following environment variables:

  • RAPIDAPI_KEY: Your RapidAPI API key
  • DB_PATH: Path to SQLite database (optional, defaults to ./patents.db)
  • Additional configuration options in .env

Rate Limits

The RapidAPI service has the following limits:

  • 1000 requests per day
  • 500000 hard limit

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details

RapidAPI-MCP FAQ

How do I install RapidAPI-MCP using Conda?
Clone the repository, then create and activate the Conda environment using the provided environment.yml file.
What database does RapidAPI-MCP use for patent data storage?
It uses SQLite for efficient local storage of patent information.
Does RapidAPI-MCP handle API rate limiting?
Yes, it includes built-in rate limiting to manage request quotas and avoid API throttling.
Can RapidAPI-MCP process patent scoring?
Yes, it supports advanced scoring systems like pscore, cscore, lscore, and tscore for patent evaluation.
Is error handling implemented in RapidAPI-MCP?
Yes, the server has robust error handling to manage API failures and data issues gracefully.
What programming language is RapidAPI-MCP built with?
It is implemented in Python, compatible with Python 3.11 environments.
Can RapidAPI-MCP be integrated with multiple LLM providers?
Yes, it can be used with models from OpenAI, Anthropic Claude, and Google Gemini via MCP clients.
How do I activate the Conda environment for RapidAPI-MCP?
Use the command 'conda activate rapidapi-mcp' after environment creation.