What is Mcp Groq

MCP-GROQ is a project that implements a suite of AI tools built on the Machine Communication Protocol (MCP) framework, utilizing large language models through a client-server architecture for specialized functionalities in search, mathematics, and news retrieval.

Use cases

Use cases for MCP-GROQ include performing web searches with content extraction, executing mathematical queries in natural language, and aggregating the latest technology news for users or developers.

How to use

To use MCP-GROQ, install the required dependencies, set up a virtual environment, and configure the environment with your GROQ API key. Each tool can be run independently by executing its client script, which launches the corresponding server component.

Key features

Key features include a DuckDuckGo search engine for web search and content extraction, a mathematical operations engine for basic arithmetic with natural language processing, and a tech news aggregator that provides real-time technology news from reputable sources.

Where to use

MCP-GROQ can be used in various fields such as software development, data analysis, educational tools, and any application requiring advanced search capabilities or mathematical computations.

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:

# 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:

# 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:

# 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

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

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

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

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 team
• langchain-mcp-adapters framework
• Groq for LLM API access