Multi LLM Cross-Check MCP Server

@lior-ps

Repository

Basic information

About Multi LLM Cross-Check MCP Server

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 config

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

{
  "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 tools

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Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

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

Frequently asked questions

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.

Comments

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