MCP-Server-Research-Tool

Name: MCP-Server-Research-Tool
Author: atimmeny27

@atimmeny27

MCP-Server-Research-Tool について

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

基本情報

カテゴリ

データと分析

ランタイム

python

トランスポート

stdio

公開者

atimmeny27

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

FAQ from MCP-Server-Research-Tool

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.

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.

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.

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.

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.

「データと分析」の他のコンテンツ

🪐✨ Jupyter MCP Server

datalayer

🪐 🔧 Model Context Protocol (MCP) Server for Jupyter.

1.2K

mcp-simple-arxiv

andybrandt

Tool to work with arXiv, provide LLM with ability to search and read papers from there

199

HubSpot MCP Server

peakmojo

A Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data, providing built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.

128