Atomic Research MCP

A powerful web research pipeline MCP built using the Atomic Agents framework.

For a full breakdown of the code, please check out this article

For now, this requires an API key for Tavily and also an OpenAI API key . In the future, I plan to make this more configurable so you can use SearxNG instead of Tavily or use Groq or Anthropic instead of OpenAI, thanks to the Atomic Agents framework this is all easy and possible, it just requires a bit of time and I wanted to get the initial project out there. Feel free to contribute, though!

Support

Do you like this project? Please consider a small donation, it means the world to me!

Overview

This project implements an advanced web research pipeline that leverages the Model Context Protocol (MCP) and Atomic Agents to provide comprehensive answers to research questions. The pipeline automates the entire research process:

Generating optimized search queries
Performing web searches using Tavily
Scraping and processing relevant web pages
Synthesizing information into coherent answers

Architecture

The system follows a modular architecture based on the MCP client-server model:

Core Components

MCP Server: Implements the Model Context Protocol to expose tools and manage client connections
Web Search Pipeline: Orchestrates the research workflow through multiple stages
Atomic Agents: Specialized AI agents for query generation and question answering
Tools: Reusable components for web search (Tavily) and web page scraping

Pipeline Flow

User Question → Query Generation → Web Search → Content Scraping → Answer Synthesis → Formatted Response

Technologies

Model Context Protocol (MCP): An open standard for AI applications to access contextual information
Atomic Agents: A modular framework for building AI agents with well-defined input/output schemas
Tavily API: A specialized search engine for AI applications
OpenAI: Powers the underlying language models for query generation and answer synthesis
Python 3.12+: The foundation of the application

Setup

Prerequisites

Python 3.12 or higher
Tavily API key
OpenAI API key

Installation

Clone the repository:

git clone https://github.com/yourusername/agentic-research-mcp.git
cd agentic-research-mcp

Install dependencies:
```
pip install -e .
```

Set up environment variables:

# Create a .env file in the project root
echo "TAVILY_API_KEY=your_tavily_api_key" > .env
echo "OPENAI_API_KEY=your_openai_api_key" >> .env

Usage

Running the Server

Start the MCP server:

python -m atomic_research_mcp.server

[rootfolder]\atomic-reseearch-mcp\.venv\Scripts\atomic-research

OR configure it in, for example, Cursor

Testing with the Client

Run the test client to verify functionality:

python test_client.py

Example Output

The system returns comprehensive research results including:

Generated search queries
Top search results with relevance scores
A detailed answer synthesized from multiple sources
References to source materials
Suggested follow-up questions

Project Structure

atomic_research_mcp/
├── agents/
│   ├── query_agent.py    # Generates optimized search queries
│   └── qa_agent.py       # Synthesizes answers from scraped content
├── tools/
│   ├── tavily_search.py  # Interface to Tavily search API
│   └── webpage_scraper.py # Extracts and processes web content
├── server.py             # MCP server implementation
└── config.py             # Configuration settings

Configuration

The system can be configured through environment variables:

TAVILY_API_KEY: Required for web search functionality
OPENAI_API_KEY: Required for AI agent operations
OPENAI_MODEL: Optional, defaults to "gpt-4o-mini"

Contributing

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

License

MIT License