Deep Research MCP Server
@pinkpixel-dev
About Deep Research MCP Server
A Model Context Protocol (MCP) compliant server designed for comprehensive web research. It uses Tavily's Search and Crawl APIs to gather detailed information on a given topic, then structures this data in a format perfect for LLMs to create high-quality markdown documents.
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"deep-research": {
"command": "npx",
"args": [
"-y",
"@pinkpixel/deep-research-mcp"
],
"env": {
"TAVILY_API_KEY": "tvly-YOUR_ACTUAL_API_KEY_HERE",
"DOCUMENTATION_PROMPT": "Your custom, detailed instructions for the LLM on how to generate markdown documents from the research data...",
"RESEARCH_OUTPUT_PATH": "/path/to/your/research/output/folder"
}
}
}
}Tools
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Overview
What is Deep Research MCP Server?
Deep Research MCP Server is a Model Context Protocol (MCP) compliant server that performs comprehensive web research using Tavily's Search and Crawl APIs. It gathers extensive information on a topic and outputs structured JSON optimized for Large Language Models (LLMs) to generate detailed markdown documents, and is intended for developers building MCP-based AI agent ecosystems.
How to use Deep Research MCP Server?
Install via npx @pinkpixel/deep-research-mcp or global npm install, then run with a Tavily API key set in the TAVILY_API_KEY environment variable. The server exposes the deep-research-tool for research requests and an optional write-research-file tool for saving results, configured through environment variables or tool arguments.
Key features of Deep Research MCP Server
- Multi-step research combining search with deep content crawling
- Structured JSON output optimized for LLM consumption
- Configurable documentation prompt via environment or tool argument
- Configurable output path for saving research documents
- Granular control over search and crawl timeouts and limits
- Optional secure file writing with path and line restrictions
- MCP compliant for integration into AI agent ecosystems
Use cases of Deep Research MCP Server
- Generate comprehensive markdown documentation on any topic via an LLM
- Automate web research and report generation in MCP-based AI agents
- Customize document output with a user-defined documentation prompt
- Perform deep content extraction from multiple web sources in a single request
- Securely save research findings directly to files through an LLM
FAQ from Deep Research MCP Server
What are the prerequisites to run the server?
Node.js 18.x or later and npm are required. A Tavily API key must be provided via the TAVILY_API_KEY environment variable.
How do I set a custom documentation prompt?
Set the DOCUMENTATION_PROMPT environment variable, or pass a documentation_prompt argument directly to the deep-research-tool call, which takes highest precedence.
Can I control where research files are saved?
Yes, set the RESEARCH_OUTPUT_PATH environment variable or pass an output_path argument to the tool. By default, files are saved to a timestamped folder in the user's Documents directory.
How do I enable the file-writing capability?
Set FILE_WRITE_ENABLED=true in the environment, and optionally restrict allowed directories with ALLOWED_WRITE_PATHS. The feature is disabled by default for security.
What security measures are in place for file writing?
Allowed directories are restricted via a comma-separated list, line limits per write operation prevent abuse, and paths are validated and sanitized. Only enable file writing in trusted environments.
Frequently asked questions
What are the prerequisites to run the server?
Node.js 18.x or later and npm are required. A Tavily API key must be provided via the `TAVILY_API_KEY` environment variable.
How do I set a custom documentation prompt?
Set the `DOCUMENTATION_PROMPT` environment variable, or pass a `documentation_prompt` argument directly to the `deep-research-tool` call, which takes highest precedence.
Can I control where research files are saved?
Yes, set the `RESEARCH_OUTPUT_PATH` environment variable or pass an `output_path` argument to the tool. By default, files are saved to a timestamped folder in the user's Documents directory.
How do I enable the file-writing capability?
Set `FILE_WRITE_ENABLED=true` in the environment, and optionally restrict allowed directories with `ALLOWED_WRITE_PATHS`. The feature is disabled by default for security.
What security measures are in place for file writing?
Allowed directories are restricted via a comma-separated list, line limits per write operation prevent abuse, and paths are validated and sanitized. Only enable file writing in trusted environments.
Basic information
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