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MCP Server Research Tool

@atimmeny27

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

atimmeny27

关于 MCP Server Research Tool

Uses an MCP server, API key, and claude to search thousands of sources and compile any topic into neatly formatted notes in markdown.

#mathgpt#math-solver#math-assistant

MCP Server Research Tool 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "MCP-Server-Research-Tool": {
      "command": "python3",
      "args": [
        "-m",
        "ensurepip",
        "--upgrade"
      ]
    }
  }
}

MCP Server Research Tool 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。

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 MCP-Server-Research-Tool?

MCP-Server-Research-Tool is a long-form research assistant powered by LLMs. It gathers information from primary sources, podcasts, academic PDFs, textbooks, YouTube, and more, then outputs structured markdown files with a detailed topic summary and source list.

How to use MCP-Server-Research-Tool?

Clone the repository, run the installation commands, set your OpenRouter API key (exported in .zshrc or .env), then launch with ./start.sh or manually with python MCP_assistant.py "Topic" and MCP_DURATION=long python send_to_claude.py. Enter a specific research topic and wait up to two minutes for the results.

Key features of MCP-Server-Research-Tool

  • Gathers information from primary sources, podcasts, and academic PDFs
  • Outputs structured markdown with summary and source list
  • Powered by LLMs via OpenRouter API (free key required)
  • Configure research prompt by editing send_to_claude.py lines 18-220
  • Supports manual launch or automated start script
  • Designed for macOS, can be added as a Desktop tool via Automator

Use cases of MCP-Server-Research-Tool

  • Conducting deep, realistic research on a specific historical or technological topic
  • Compiling structured notes from multiple media types (audio, video, text)
  • Creating markdown notes for use in editors like Obsidian
  • Learning about a subject by simulating LLM-driven multi-source investigation

常见问题

1

What API key is required?

You need a free API key from OpenRouter (or optionally from OpenAI/Anthropic). The key is set as environment variable `OPENROUTER_API_KEY` in `~/.zshrc` or a local `.env` file.

2

How long does research take?

The tool waits up to 2 minutes for the LLM to research the topic. You will see a message like “🔐 Using key: sk-or-v1-2 …” when it is actively researching.

3

What is the output?

The output is a markdown (.md) file containing a detailed summary and a list of sources used. It is best opened in a markdown-friendly editor such as Obsidian.

4

Can I edit the research prompt?

Yes. The instructions sent to Claude are in `send_to_claude.py` around lines 18-220 as a long f-string. You can modify that to change the research behavior.

5

Are there any known limitations?

Wikipedia’s API may be overloaded, causing temporary errors. Spelling errors or niche topics may confuse the LLM. For best results, use a specific, detailed research topic.

评论

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