io.github.daedalus/mcp-llm-gateway
MCP-compatible LLM gateway that proxies completion requests.
Open source Open in the app JSON README (API)
About
MCP-compatible LLM gateway that proxies completion requests.
Details
- Kind
- MCP servers
- Topic
- AI, RAG & memory
- Publisher
- daedalus
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.0
- Last push
- 2026-04-21T19:26:56Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:02:39
- Updated
- 2026-08-29 03:02:39
- Origin id
io.github.daedalus/mcp-llm-gateway
README
# MCP LLM Gateway > MCP-compatible LLM gateway that proxies completion requests to downstream OpenAI-compatible providers. [](https://pypi.org/project/mcp-llm-gateway/) [](https://pypi.org/project/mcp-llm-gateway/) [](https://github.com/astral-sh/ruff) mcp-name: io.github.daedalus/mcp-llm-gateway ## Install ```bash pip install mcp-llm-gateway ``` ## Usage ### Configuration Set the following environment variables: - `DOWNSTREAM_URL`: Base URL for the OpenAI-compatible downstream API (required) - `DEFAULT_MODEL`: Default model to use for completions (required) - `MODEL_LIST_URL`: URL to fetch available models from (optional, defaults to models.dev) - `API_KEY`: Optional API key for downstream (passthrough) - `TIMEOUT`: Request timeout in seconds (optional, default: 60) ### MCP Server Run the MCP server with stdio transport: ```bash mcp-llm-gateway ``` ### MCP Tools The server exposes the following tools: - `list_models()`: List all available models from the remote endpoint - `complete(prompt, model, max_tokens, temperature)`: Send a completion request to the downstream LLM provider ### MCP Resources - `models://list`: Returns the list of available models - `config://info`: Returns current gateway configuration ## Development ```bash git clone https://github.com/daedalus/mcp-llm-gateway.git cd mcp-llm-gateway pip install -e ".[test]" # run tests pytest # format ruff format src/ tests/ # lint ruff check src/ tests/ # type check mypy src/ ``` ## API ### core.models - `Model`: Dataclass representing an available LLM model - `CompletionRequest`: Dataclass for completion request payloads - `GatewayConfig`: Dataclass for gateway configuration ### adapters.http - `HTTPAdapter`: HTTP client for downstream API communication - `ModelListAdapter`: Adapter for fetching model list from remote endpoints ### services.gateway - `ModelService`: Service for managing model discovery and caching - `CompletionService`: Service for handling completion requests - `ConfigService`: Service for managing gateway configuration