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JamesANZ/cross-llm-mcp

by Various

A Model Context Protocol (MCP) server that provides access to multiple Large Language Model (LLM) APIs including ChatGPT, Claude, Gemini, Mistral, Kimi K2, and DeepSeek.

J

MCP

JamesANZ/cross-llm-mcp

Added 1 June 2026

#chatgpt #claude #deepseek #gemini #grok #kimi-k2 #llms #mcp-server

Overview

A Model Context Protocol (MCP) server that provides a unified interface to multiple LLM APIs including ChatGPT, Claude, Gemini, Mistral, Kimi K2, and DeepSeek. It allows developers to switch between models or route requests through a single MCP endpoint without changing client code.

Best for

Best for
Developers who need a unified MCP-based gateway to multiple LLM APIs

Use cases

  • Unifying access to multiple LLM providers behind one MCP endpoint
  • Seamlessly switching between different models during development or testing
  • Building applications that can leverage diverse AI capabilities through a standardized protocol

Notes

A Model Context Protocol (MCP) server that provides a unified interface to multiple LLM APIs including ChatGPT, Claude, Gemini, Mistral, Kimi K2, and DeepSeek. It allows developers to switch between models or route requests through a single MCP endpoint without changing client code.

15 stars on GitHub. Last updated 2026-04-17. Licensed MIT.

Use cases

  • Unifying access to multiple LLM providers behind one MCP endpoint
  • Seamlessly switching between different models during development or testing
  • Building applications that can leverage diverse AI capabilities through a standardized protocol

Pros

  • Supports a broad range of major LLM providers
  • Standardized MCP interface simplifies integration
  • Open source with a clear TypeScript codebase for customization

Cons

  • Low community adoption (15 stars) may indicate limited support and documentation
  • Requires managing multiple API keys and provider configurations
  • MCP protocol is less commonly used than direct API calls, adding potential complexity

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Supports a broad range of major LLM providers
  • Standardized MCP interface simplifies integration
  • Open source with a clear TypeScript codebase for customization

Cons

  • Low community adoption (15 stars) may indicate limited support and documentation
  • Requires managing multiple API keys and provider configurations
  • MCP protocol is less commonly used than direct API calls, adding potential complexity