Deep Research MCP

What is Deep Research MCP about?

Deep Research MCP provides a Model Context Protocol‑compliant server that collects detailed information on a given topic from the web, organizes it into a structured JSON format, and enables downstream large language models to produce high‑quality markdown documents.

How to use Deep Research MCP?

1. Install the project:

   git clone https://github.com/ali-kh7/deep-research-mcp.git
   cd deep-research-mcp
   npm install
   

2. Start the server (preferred shortcut):

   npx -y deep-research-mcp
   

   The server listens for HTTP requests on the default port.
3. Send a research request:

   POST /api/research
   Content-Type: application/json
   
   { "topic": "Artificial Intelligence" }
   

   The response contains structured data ready for markdown conversion.
4. Check server health with GET /api/status.

Key features of Deep Research MCP

• MCP compliance – seamless integration with tools that follow the Model Context Protocol.
• Tavily integration – leverages Search and Crawl APIs for deep web research.
• Structured output – data returned in a consistent JSON schema suitable for LLM consumption.
• Markdown generation helpers – built‑in functions to transform structured data into well‑formatted markdown.
• TypeScript codebase – modern, type‑safe development experience.

Use cases of Deep Research MCP

• Automated generation of research reports, whitepapers, or knowledge‑base articles.
• Feeding up‑to‑date web information into chatbots or AI assistants.
• Creating content pipelines where raw web data is transformed into publishable markdown.
• Supporting multi‑agent systems that require a reliable source of curated web knowledge.

FAQ from the Deep Research MCP

Q: Which API keys are required? A: You need a valid Tavily API key. Set it in the environment (e.g., TAVILY_API_KEY) before starting the server.

Q: Can I customize the output format? A: The server returns a standard JSON structure, but you can extend the provided markdown conversion utilities to fit your own templates.

Q: Is the server scalable? A: It runs on Node.js and can be containerized or deployed behind a load balancer for horizontal scaling.

Q: How do I contribute? A: Fork the repository, create a feature branch, commit your changes, and open a pull request as described in the README.

Deep Research MCP 🌐

Download Releases

Welcome to the Deep Research MCP repository! This project provides a server compliant with the Model Context Protocol (MCP). It is designed to facilitate comprehensive web research. By utilizing Tavily's Search and Crawl APIs, the server gathers detailed information on various topics and structures this data to support high-quality markdown document creation using large language models (LLMs).

Table of Contents

• Features
• Installation
• Usage
• API Documentation
• Contributing
• License
• Support

Features

• MCP Compliance: The server adheres to the Model Context Protocol, ensuring compatibility with various tools and services.
• Data Aggregation: Efficiently gathers and structures data from multiple sources.
• Markdown Generation: Converts gathered data into well-structured markdown documents.
• Web Crawling: Utilizes Tavily's Search and Crawl APIs for in-depth web research.
• Node.js and TypeScript: Built using modern technologies for better performance and maintainability.

Installation

To get started with Deep Research MCP, follow these steps:

1. Clone the repository:

   git clone https://github.com/ali-kh7/deep-research-mcp.git
   

2. Navigate to the project directory:

   cd deep-research-mcp
   

3. Install the dependencies:

   npm install
   

4. Run the server:

   npm start
   

You can also check the Releases section for downloadable files and specific versions.

Usage

Once the server is running, you can interact with it via the API. Here’s how to use it effectively:

1. Send a request to gather information:

   You can send a request to the server with a specific topic to gather data. The server will return structured information ready for markdown generation.

   Example request:

   POST /api/research
   Content-Type: application/json
   
   {
     "topic": "Artificial Intelligence"
   }
   

2. Receive structured data:

   The server responds with data in a structured format. This data can be used directly or transformed into markdown documents.

3. Generate markdown documents:

   The structured data can be converted into markdown using the provided functions in the API.

Example Markdown Output

# Artificial Intelligence

## Overview
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines.

## Applications
- Healthcare
- Finance
- Transportation

## Conclusion
AI is transforming industries and shaping the future.

API Documentation

For detailed API documentation, please refer to the docs folder in this repository. It contains information on all available endpoints, request formats, and response structures.

Endpoints

• POST /api/research: Gather information on a specific topic.
• GET /api/status: Check the server status.

Contributing

We welcome contributions to improve Deep Research MCP. If you want to contribute, please follow these steps:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature/YourFeatureName
   

3. Make your changes.

4. Commit your changes:

   git commit -m "Add your message here"
   

5. Push to the branch:

   git push origin feature/YourFeatureName
   

6. Open a Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

If you encounter any issues or have questions, please check the Releases section or open an issue in the repository.

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Thank you for checking out Deep Research MCP! We hope this tool enhances your web research capabilities. Happy coding!