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Multi LLM Cross-Check MCP Server

@lior-ps

リポジトリ

基本情報

カテゴリ

AI とエージェント

ライセンス

MIT

トランスポート

stdio

作者

lior-ps

Multi LLM Cross-Check MCP Server について

A Model Control Protocol (MCP) server that allows cross-checking responses from multiple LLM providers simultaneously

#multi-llm#cross-check#mcp-server

Multi LLM Cross-Check 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-cross-check-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@lior-ps/multi-llm-cross-check-mcp-server",
        "--client",
        "claude"
      ]
    }
  }
}

Multi LLM Cross-Check 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。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Multi LLM Cross-Check MCP Server?

A Model Control Protocol (MCP) server that allows cross-checking responses from multiple LLM providers simultaneously. It integrates with Claude Desktop to provide a unified interface for querying OpenAI, Anthropic, Perplexity, and Google Gemini APIs.

How to use Multi LLM Cross-Check MCP Server?

Install via Smithery (npx -y @smithery/cli install @lior-ps/multi-llm-cross-check-mcp-server --client claude) or manually by cloning the repository, setting up a uv environment, and configuring API keys in claude_desktop_config.json. Once configured, the server starts automatically with Claude Desktop; use the cross_check tool by asking to "cross check with other LLMs" and providing a prompt.

Key features of Multi LLM Cross-Check MCP Server

  • Query multiple LLM providers in parallel
  • Supports OpenAI, Anthropic, Perplexity, and Google Gemini
  • Asynchronous parallel processing for faster responses
  • Easy integration with Claude Desktop via MCP
  • Independent error handling per provider

Use cases of Multi LLM Cross-Check MCP Server

  • Compare answers to the same prompt across different LLMs
  • Verify factual consistency by cross-checking responses
  • Test how different models handle the same query
  • Validate code snippets or reasoning outputs

よくある質問

1

What are the prerequisites?

Python 3.8 or higher, API keys for the desired LLM providers, and the uv package manager installed via `pip install uv`.

2

How do I configure the server?

Set environment variables for each provider’s API key (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `PERPLEXITY_API_KEY`, `GEMINI_API_KEY`) in the `claude_desktop_config.json` file.

3

What if I don’t have a key for a certain provider?

That provider will be skipped automatically; you only need keys for the services you want to use.

4

How does error handling work?

Each LLM’s response is fetched independently. API errors are caught and returned in the response, and errors with one provider do not affect the others.

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