LLM Gateway MCP Server
@Dicklesworthstone
Comprehensive MCP server exposing dozens of capabilities to AI agents: multi-provider LLM delegation, browser automation, document processing, vector ops, and cognitive memory systems
概要
What is LLM Gateway MCP Server?
LLM Gateway MCP Server is a comprehensive MCP-native system that serves as a complete AI agent operating system. It exposes dozens of powerful capabilities through the Model Context Protocol, enabling advanced AI agents to access a rich ecosystem of tools, cognitive systems, and specialized services.
How to use LLM Gateway MCP Server?
Install the Python package (Python 3.13+ required), configure provider API keys or a local model endpoint, then connect your AI agent (e.g., Claude) to the server via MCP. The server’s tools are automatically discovered and callable by the agent.
Key features of LLM Gateway MCP Server
- Native MCP server with standardized tool framework and discovery.
- Intelligent task delegation across multiple LLM providers.
- Multi-level caching (exact, semantic, task-aware) with disk persistence.
- Comprehensive document processing: chunking, summarization, entity extraction.
- Secure filesystem operations with path validation and search capabilities.
- Browser automation, OCR, vector search, RAG, and dynamic API integration.
Use cases of LLM Gateway MCP Server
- Augment an AI agent with web browsing, database queries, and file operations for complex research tasks.
- Delegate costly summarization and extraction to a free local model via the
localprovider. - Process and analyze large documents in parallel, generating structured data or summaries.
- Automate multi-step workflows combining CLI tools, Excel manipulation, and entity graph analysis.
- Optimize API costs by routing routine tasks to cheaper models or non-LLM tools.
FAQ from LLM Gateway MCP Server
What LLM providers are supported?
OpenAI, Anthropic (Claude), Google (Gemini), xAI (Grok), DeepSeek, OpenRouter, and any OpenAI-compatible local server (Ollama, llama.cpp, vLLM, etc.) via the configurable local provider.
How does cost optimization work?
The server routes appropriate tasks to cheaper models (e.g., $0.01/1K tokens vs $0.15/1K tokens), uses advanced caching to avoid redundant API calls, and handles routine processing with specialized non-LLM tools.
What are the runtime requirements?
Python 3.13 or later. Local inference requires a compatible local server (e.g., Ollama) configured via the local provider; no GPU is strictly needed if using cloud providers.
Does the server support persistent memory?
Yes. The cognitive memory system provides persistent state across operations, with vector storage and retrieval-augmented generation for maintaining context.
Is the server free to use for local inference?
Yes. The local provider is cost-accounted at $0,
