MCP.so
ログイン

Multi LLM Cross-Check MCP Server

@lior-ps

リポジトリ

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

基本情報

カテゴリ

AI とエージェント

ライセンス

MIT

ランタイム

python

トランスポート

stdio

公開者

lior-ps

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"
      ]
    }
  }
}

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 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

FAQ from Multi LLM Cross-Check MCP Server

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.

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.

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.

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.

What transport does the server use?

—

コメント

「AI とエージェント」の他のコンテンツ

Mcp Agent

lastmile-ai

Build effective agents using Model Context Protocol and simple workflow patterns

8.4K

Sequential Thinking Multi-Agent System (MAS)

FradSer

An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.

306

mcp-hfspace MCP Server 🤗

evalstate

MCP Server to Use HuggingFace spaces, easy configuration and Claude Desktop mode.

387

欢迎来到 智言平台

Shy2593666979

AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、HITL、Skill、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。

797

Perplexity Ask MCP Server

ppl-ai

The official MCP server implementation for the Perplexity API Platform

2.4K