Frequently Asked Questions about Deep Research MCP

Q: What is Deep Research MCP?

A: Deep Research MCP is a Model Context Protocol (MCP) compliant server designed for comprehensive web research. It utilizes Tavily’s Search and Crawl APIs to gather and structure information, making it ideal for Large Language Models (LLMs) to create high-quality markdown documents.

Q: How does Deep Research MCP work?

A: It works by receiving a topic or query, using Tavily APIs to collect relevant web data, structuring that data into a standardized format, converting it to markdown, and then providing the markdown document as output.

Q: What are the main features of Deep Research MCP?

A: Key features include MCP compliance, efficient data aggregation, markdown generation, in-depth web crawling using Tavily APIs, and a modern technology stack (Node.js and TypeScript).

Q: What is the Model Context Protocol (MCP)?

A: MCP is an open protocol that standardizes how applications provide context to LLMs, ensuring seamless integration and interoperability.

Q: What are Tavily’s Search and Crawl APIs?

A: Tavily provides APIs for searching and crawling the web, allowing Deep Research MCP to gather detailed and up-to-date information on various topics.

Q: How can I install Deep Research MCP?

A: You can install it by cloning the repository from GitHub, navigating to the project directory, installing the dependencies using npm install, and then running the server with npm start.

Q: How do I use Deep Research MCP once it’s installed?

A: Once the server is running, you can send a POST request to the /api/research endpoint with a JSON payload containing the topic you want to research. The server will return structured data ready for markdown generation.

Q: Can I contribute to Deep Research MCP?

A: Yes, contributions are welcome! You can fork the repository, create a new branch for your feature, commit your changes, and then open a pull request.

Q: What license is Deep Research MCP released under?

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

Q: How can Deep Research MCP be integrated with UBOS?

A: Deep Research MCP can be integrated with UBOS to enable AI agents to perform complex research tasks autonomously. UBOS helps orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents with your LLM model and Multi-Agent Systems. The data gathered by Deep Research MCP can be used to inform and enhance the decision-making process of AI agents within the UBOS platform.