Deep Research MCP Server

Name: Deep Research MCP Server
Author: pinkpixel-dev

@pinkpixel-dev

关于 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.

#information-retrieval #knowledge-base #web-crawling #search-api #data-aggregation #research-assistant #documentation-generation #ai-tools #llm #tavily #web-research #deep-research

基本信息

分类

数据与分析

许可证

MIT

运行时

node

传输方式

stdio

发布者

pinkpixel-dev

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "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"
      }
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

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