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MCP-Server-Research-Tool

@atimmeny27

Repository

About 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

Basic information

Category

Data & Analytics

Runtime

python

Transports

stdio

Publisher

atimmeny27

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Comments

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