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Deep Research MCP Server

@pinkpixel-dev

代码仓库主页

基本信息

分类

数据与分析

许可证

MIT

传输方式

stdio

作者

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

Deep Research MCP Server 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": "YOUR_TAVILY_API_KEY",
        "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"
      }
    }
  }
}

Deep Research MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

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

常见问题

1

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.

2

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.

3

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.

4

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.

5

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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