LLM Wrapper MCP Server
@matdev83
About LLM Wrapper MCP Server
Allow any MCP-capable LLM agent to communicate with or delegate tasks to any other LLM available through the OpenRouter.ai API.
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"llm-wrapper-mcp-server": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}Tools
No tools detected
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Overview
What is LLM Wrapper MCP Server?
LLM Wrapper MCP Server is a Model Context Protocol (MCP) STDIO server that allows any MCP-capable LLM agent to communicate with or delegate tasks to any other LLM available through the OpenRouter.ai API. It’s built for developers who need a standardized interface to integrate multiple LLM backends into their applications.
How to use LLM Wrapper MCP Server?
Install the package via pip install llm-wrapper-mcp-server, create a .env file with your OPENROUTER_API_KEY, then run the server with python -m llm_wrapper_mcp_server [OPTIONS] (e.g., --model your-org/your-model). The server communicates over stdin/stdout using JSON‑RPC messages and exposes an llm_call tool that accepts a prompt (required) and optionally a model override.
Key features of LLM Wrapper MCP Server
- Implements the MCP specification for standardized LLM interactions.
- STDIO‑based server handling LLM requests and responses.
- Supports advanced tool calls and result processing via MCP.
- Configurable API base URL and model (OpenRouter by default).
- Designed for extensibility to integrate new LLM backends.
- Integrates with
llm-accountingfor logging, rate limiting, and audit.
Use cases of LLM Wrapper MCP Server
- Allow any MCP‑capable agent to delegate tasks to OpenRouter’s LLM models.
- Monitor and audit remote LLM usage, inference costs, and query/response payloads.
- Seamlessly swap or update LLM backends without changing agent code.
- Run a local MCP bridge that logs all LLM calls for debugging or compliance.
FAQ from LLM Wrapper MCP Server
What does LLM Wrapper MCP Server do that a direct API call doesn’t?
It wraps the OpenRouter API into the MCP protocol, enabling any MCP‑compatible agent to use OpenRouter models through a standardized interface (stdin/stdout JSON‑RPC) and adds integrated logging/auditing via llm-accounting.
How do I configure the server?
You must set the OPENROUTER_API_KEY environment variable (in a .env file). The API base URL and model can be overridden via CLI arguments (e.g., --model or --llm-api-base-url) or environment variables.
Does the server open a network port?
No. It is an STDIO server that communicates exclusively via standard input and output; it does not open a network port for MCP communication.
What models can I use?
Any model available through OpenRouter.ai. The default model is perplexity/llama-3.1-sonar-small-128k-online, but you can specify any OpenRouter model via the --model CLI argument or per‑call in the llm_call tool’s arguments.
Is logging and auditing included?
Yes. The server integrates with llm-accounting for robust logging, rate limiting, and audit functionality, enabling monitoring of remote LLM usage, inference costs, and inspection of queries/responses.
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