MCP-GROQ

# MCP Tools Project ## Overview This project implements a suite of AI tools built on the Machine Communication Protocol (MCP) framework. Each tool leverages large language models through a client-server architecture to provide specialized functionality for search, mathematics, and news retrieval operations. ## Features - **DuckDuckGo Search Engine**: Web search with content extraction capabilities - **Mathematical Operations Engine**: Basic arithmetic with natural language processing - **Tech News Aggregator**: Real-time technology news from reputable sources ## Requirements - Python 3.8+ - UV package manager - Groq API key ## Installation ### Setting up UV If you don't have UV installed, install it first: ```bash # Install UV using curl curl -sSf https://install.ultraviolet.rs | sh # Or with pip pip install uv ``` ### Installing Project Dependencies Clone the repository and install dependencies using the existing pyproject.toml: ```bash # Clone the repository git clone https://github.com/rahulsamant37/mcp-tools.git cd mcp-tools # Create and activate a virtual environment uv venv # On Windows: .venv\Scripts\activate # On macOS/Linux: source .venv/bin/activate # Install dependencies from pyproject.toml uv pip sync ``` If you need to install dependencies without an existing pyproject.toml: ```bash # Install directly (will update pyproject.toml and uv.lock) uv pip install mcp langchain-mcp-adapters langchain-groq langgraph httpx beautifulsoup4 python-dotenv ``` ### Environment Configuration Create a `.env` file in the project root: ``` GROQ_API_KEY=your_groq_api_key_here ``` ## Usage Guide Each tool can be tested independently by running its client script, which automatically launches the corresponding server component. ### DuckDuckGo Search Tool ```bash python duckduckgo_client.py ``` This tool provides: - Web search functionality via DuckDuckGo - Content extraction from websites - Rate-limited requests to prevent IP blocking - Formatted search results optimized for LLM consumption ### Math Calculation Tool ```bash python math_client.py ``` This tool enables: - Basic arithmetic operations (addition, multiplication) - Natural language processing of mathematical expressions - Integration with ReAct agents for complex problem solving ### Tech News Retrieval Tool ```bash python weather_client.py ``` This tool delivers: - Latest articles from Ars Technica - Content parsing and summarization - Structured data output for LLM processing ## Technical Architecture The project implements a microservices architecture using MCP: ### Server Layer - Implements domain-specific functionality - Exposes capabilities through standardized MCP interfaces - Handles rate limiting and error management - Processes raw data into LLM-friendly formats ### Client Layer - Establishes connections to server components - Creates LangChain-compatible tool interfaces - Integrates with ReAct agents for reasoning - Manages conversation context and state ### LLM Integration - Leverages Groq's Qwen-2.5-32b model for reasoning - Implements ReAct (Reasoning + Acting) methodology - Supports asynchronous operations for improved performance ## Troubleshooting | Issue | Solution | |-------|----------| | Connection errors | Check that no other processes are using required ports | | Authentication failures | Verify Groq API key in .env file | | Rate limiting | Implement exponential backoff between requests | | Timeout errors | Increase timeout values in httpx client configurations | | Dependency issues | Run `uv pip list` to verify installations | | UV sync errors | Check if pyproject.toml exists and is valid | ## Contributing To extend this project with new tools: 1. Create a server file implementing your tool's functionality 2. Expose methods using the `@mcp.tool()` decorator 3. Develop a client file that establishes connections and loads tools 4. Integrate with the ReAct agent framework ## License This project is licensed under the GNU License - see the LICENSE file for details. ## Acknowledgments - [Machine Communication Protocol](https://github.com/llm-protocol/mcp) team - [langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters) framework - [Groq](https://groq.com/) for LLM API access