About Multi LLM Cross-Check MCP Server
An MCP server for users of Claude Desktop and other MCP clients to query multiple LLM providers in parallel and compare their responses. Providers without configured API keys are skipped, and one provider’s error does not prevent others from returning results.
Key features
- 01Parallel queries to multiple LLM providers
- 02Supports OpenAI, Anthropic, Perplexity AI, and Google
- 03Asynchronous request processing
- 04Separate responses and errors per provider
Use cases
- 01Compare several LLM responses to one question
- 02Run cross-checks in Claude Desktop conversations
MCP support
Exposes a `cross_check` tool through MCP for clients to submit a prompt and receive responses from configured providers. The README describes integration with Claude Desktop.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMulti LLM Cross-Check MCP Server
A Model Control Protocol (MCP) server that allows cross-checking responses from multiple LLM providers simultaneously. This server integrates with Claude Desktop as an MCP server to provide a unified interface for querying different LLM APIs.
Features
- Query multiple LLM providers in parallel
- Currently supports:
- OpenAI (ChatGPT)
- Anthropic (Claude)
- Perplexity AI
- Google (Gemini)
- Asynchronous parallel processing for faster responses
- Easy integration with Claude Desktop
Prerequisites
- Python 3.8 or higher
- API keys for the LLM providers you want to use
- uv package manager (install with
pip install uv)
Installation
Installing via Smithery
To install Multi LLM Cross-Check Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @lior-ps/multi-llm-cross-check-mcp-server --client claude
Manual Installation
- Clone this repository:
git clone https://github.com/lior-ps/multi-llm-cross-check-mcp-server.git
cd multi-llm-cross-check-mcp-server
- Initialize uv environment and install requirements:
uv venv
uv pip install -r requirements.txt
-
Configure in Claude Desktop: Create a file named
claude_desktop_config.jsonin your Claude Desktop configuration directory with the following content:{ "mcp_servers": [ { "command": "uv", "args": [ "--directory", "/multi-llm-cross-check-mcp-server", "run", "main.py" ], "env": { "OPENAI_API_KEY": "your_openai_key", // Get from https://platform.openai.com/api-keys "ANTHROPIC_API_KEY": "your_anthropic_key", // Get from https://console.anthropic.com/account/keys "PERPLEXITY_API_KEY": "your_perplexity_key", // Get from https://www.perplexity.ai/settings/api "GEMINI_API_KEY": "your_gemini_key" // Get from https://makersuite.google.com/app/apikey } } ] }Notes:
- You only need to add the API keys for the LLM providers you want to use. The server will skip any providers without configured API keys.
- You may need to put the full path to the uv executable in the command field. You can get this by running
which uvon MacOS/Linux orwhere uvon Windows.
Using the MCP Server
Once configured:
- The server will automatically start when you open Claude Desktop
- You can use the
cross_checktool in your conversations by asking to "cross check with other LLMs" - Provide a prompt, and it will return responses from all configured LLM providers
API Response Format
The server returns a dictionary with responses from each LLM provider:
{
"ChatGPT": { ... },
"Claude": { ... },
"Perplexity": { ... },
"Gemini": { ... }
}
Error Handling
- If an API key is not provided for a specific LLM, that provider will be skipped
- API errors are caught and returned in the response
- Each LLM's response is independent, so errors with one provider won't affect others
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
