{"repo":"pinkpixel-dev/deep-research-mcp","free":true,"listed":false,"github":"https://github.com/pinkpixel-dev/deep-research-mcp","clone":"git clone https://github.com/pinkpixel-dev/deep-research-mcp.git","description":"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.","language":"JavaScript","stars":27,"topics":["ai-tools","data-aggregation","deep-research","documentation-generation","information-retrieval","knowledge-base","llm","mcp","mcp-server","model-context-protocol"],"license":"Apache-2.0","category":"mcp-servers","readme_excerpt":"Deep Research MCP Server The Deep Research MCP Server is a Model Context Protocol (MCP) compliant server designed to perform comprehensive web research. It leverages Tavily's powerful Search and new Crawl APIs to gather extensive, up-to-date information on a given topic. The server then aggregates this data along with documentation generation instructions into a structured JSON output, perfectly tailored for Large Language Models (LLMs) to create detailed and high-quality markdown documents. Features Multi-Step Research: Combines Tavily's AI-powered web search with deep content crawling for thorough information gathering. Structured JSON Output: Provides well-organized data (original query, search summary, detailed findings per source, and documentation instructions) optimized for LLM consumption. Configurable Documentation Prompt: Includes a comprehensive default prompt for generating high-quality technical documentation. This prompt can be: Overridden by setting the DOCUMENTATION PROMPT environment variable. Further overridden by passing a documentation prompt argument directly to the tool. Configurable Output Path: Specify where research documents and images should be saved through: Environment variable configuration JSON configuration Direct parameter in tool calls Granular Control: Offers a wide range of parameters to fine-tune both the search and crawl processes. MCP Compliant: Designed to integrate seamlessly into MCP-based AI agent ecosystems. Prerequisites Node.js (v","default_branch":null,"files":null,"tree":[],"storefront":"/r/pinkpixel-dev","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/pinkpixel-dev/deep-research-mcp/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}