What is Atomic Research Mcp

Atomic Research MCP is a powerful web research pipeline built using the Atomic Agents framework, designed to automate the research process and provide comprehensive answers to research questions.

Use cases

Use cases include generating research papers, conducting market research, creating content for blogs or articles, and answering complex questions by synthesizing information from multiple web sources.

How to use

To use atomic-research-mcp, you need to obtain API keys for Tavily and OpenAI. Once you have the keys, you can set up the pipeline to generate optimized search queries, perform web searches, scrape relevant content, and synthesize the information into coherent answers.

Key features

Key features include optimized query generation, automated web searches using Tavily, content scraping, and synthesis of information into coherent answers. The system is modular and leverages the Model Context Protocol (MCP) for managing client connections.

Where to use

Atomic Research MCP can be used in various fields such as academic research, market analysis, content creation, and any domain that requires comprehensive web-based research.

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:

1. Generating optimized search queries
2. Performing web searches using Tavily
3. Scraping and processing relevant web pages
4. 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

1. Clone the repository:

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

2. Install dependencies:

   pip install -e .
   

3. 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

OR

[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