Question 1

What is the Deep Research MCP Server?

Accepted Answer

It's a Model Context Protocol (MCP) compliant server designed for comprehensive web research. It uses Tavily's Search and Crawl APIs to gather detailed information and structures it for LLMs to create high-quality markdown documents.

Question 2

What is MCP?

Accepted Answer

MCP is an open protocol that standardizes how applications provide context to LLMs.

Question 3

What are the prerequisites for using the Deep Research MCP Server?

Accepted Answer

You need Node.js (version 18.x or later recommended) and npm (comes with Node.js) or Yarn. Also, you need a Tavily API key.

Question 4

How do I install the Deep Research MCP Server?

Accepted Answer

You can install it via Smithery, using NPX, global installation with npm, or by cloning the repository for local project integration.

Question 5

How do I configure the Tavily API key?

Accepted Answer

Set the `TAVILY_API_KEY` environment variable in a `.env` file, directly in the command line, or in your system environment variables.

Question 6

Can I customize the documentation prompt for the LLM?

Accepted Answer

Yes, you can override the default prompt by setting the `DOCUMENTATION_PROMPT` environment variable or passing a `documentation_prompt` argument directly to the tool.

Question 7

How do I specify where the research documents and images should be saved?

Accepted Answer

You can configure the output path using the `output_path` parameter, the `RESEARCH_OUTPUT_PATH` environment variable, or rely on the default path in the user's Documents folder.

Question 8

What are the available search parameters?

Accepted Answer

Search parameters include `search_depth`, `topic`, `days`, `time_range`, `max_search_results`, and more. These parameters allow you to fine-tune the Tavily Search API.

Question 9

What are the available crawl parameters?

Accepted Answer

Crawl parameters include `crawl_max_depth`, `crawl_max_breadth`, `crawl_limit`, `crawl_instructions`, and more. These parameters allow you to control the Tavily Crawl API.

Question 10

How does the server work?

Accepted Answer

The server receives a `CallToolRequest` from an LLM or AI agent, performs a Tavily Search, uses Tavily Crawl to extract content, aggregates the information, and returns a JSON string with structured data for the LLM to generate a markdown document.

Question 11

What is the output structure of the `deep-research-tool`?

Accepted Answer

The tool returns a JSON string with fields like `documentation_instructions`, `original_query`, `search_summary`, `research_data`, and `output_path`.

Question 12

How do I troubleshoot API key errors?

Accepted Answer

Ensure that the `TAVILY_API_KEY` is correctly set and valid.

Question 13

What should I do if I encounter SDK issues?

Accepted Answer

Make sure that `@modelcontextprotocol/sdk` and `@tavily/core` are installed and up-to-date.

Question 14

What if I get no output or errors?

Accepted Answer

Check the server console logs for any error messages. Increase verbosity if needed for debugging.

Question 15

Is the file writing feature secure?

Accepted Answer

The file writing feature is disabled by default for security. When enabled, it includes directory restrictions and line limits to prevent abuse.  Only enable it in trusted environments.

Question 16

How does the Deep Research MCP Server relate to UBOS?

Accepted Answer

UBOS is a full-stack AI Agent Development Platform that allows you to orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents with your LLM model, and create Multi-Agent Systems. The Deep Research MCP Server can be seamlessly integrated into UBOS workflows to enhance the research capabilities of AI agents.

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