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IntraIntel.ai - Multi-LLM Agent Coding Challenge MCP Server

@Kush614

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

Kush614

IntraIntel.ai - Multi-LLM Agent Coding Challenge MCP Server について

概要はまだありません

#multi-llm#medical-agent#mcp-server

IntraIntel.ai - Multi-LLM Agent Coding Challenge MCP Server の設定

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": {
    "Multi-LLM-Medical-Agent-using-MCP-Server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

IntraIntel.ai - Multi-LLM Agent Coding Challenge MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

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 IntraIntel.ai - Multi-LLM Agent Coding Challenge?

It is a Python-based multi-agent system that answers medical questions by orchestrating multiple free LLMs from the Hugging Face Inference API. It uses a custom HTTP-based Model Context Protocol (MCP) to interact with separate tool servers for web search (DuckDuckGo) and PubMed search, and implements a Retrieval-Augmented Generation (RAG) pipeline with optional query refinement and snippet summarization.

How to use IntraIntel.ai - Multi-LLM Agent Coding Challenge?

Clone the project, create a Python virtual environment, install dependencies (FastAPI, uvicorn, httpx, duckduckgo-search, biopython, python-dotenv), and set HF_API_TOKEN and NCBI_EMAIL in a .env file. Accept the license terms for mistralai/Mistral-7B-Instruct-v0.3 on Hugging Face. Start the Web Search MCP server on port 8001 and the PubMed MCP server on port 8002 in separate terminals. Then run python agent/main_agent.py to process a predefined set of medical questions.

Key features of IntraIntel.ai - Multi-LLM Agent Coding Challenge

  • Multi-step RAG pipeline using up to three distinct LLMs.
  • Optional query refinement and snippet summarization steps.
  • Separate synthesized answers from web search and PubMed with source links.
  • Asynchronous HTTP communication via FastAPI and Uvicorn.
  • API-key-free web search via DuckDuckGo and free Hugging Face models.

Use cases of IntraIntel.ai - Multi-LLM Agent Coding Challenge

  • Answering medical questions by combining general web and PubMed literature.
  • Comparing synthesized answers from two independent information sources.
  • Automating evidence-based research for clinical or educational queries.
  • Demonstrating an agentic RAG workflow with multiple free LLMs.

よくある質問

1

Which LLMs are used?

The system uses `mistralai/Mistral-7B-Instruct-v0.3` for query refinement and answer synthesis, and `sshleifer/distilbart-cnn-6-6` for snippet summarization, both accessed via the free Hugging Face Inference API.

2

What are the main dependencies?

FastAPI, uvicorn, httpx, duckduckgo-search, biopython, and python-dotenv.

3

How do the MCP servers communicate?

They use a simple HTTP-based JSON protocol: a POST request to `/execute` with `{"query": "..."}` returns a JSON response containing source, status, and results (title, snippet, URL or PubMed ID).

4

What authentication is required?

A Hugging Face API token (`HF_API_TOKEN`) with read permissions, and an email address (`NCBI_EMAIL`) for polite access to NCBI’s Entrez system.

5

Are there performance considerations?

The first call to a Hugging Face model may take 20–90 seconds as the model loads; subsequent calls are faster. The script includes pauses to respect API rate limits.

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