What LLM providers does Otter Bridge support?
Currently, Ollama is supported. Support for ChatGPT is planned for Q2 2025 and Claude for Q3 2025.
@typangaa
基本信息
OtterBridge is a lightweight, flexible server for connecting applications to various Large Language Model providers. Following the principles of simplicity and composability outlined in Anthropic's guide to building effective agents, OtterBridge provides a clean interface to LLMs
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"otterbridge": {
"command": "uv",
"args": [
"run",
"server.py"
]
}
}
}Send messages to LLMs and get AI-generated responses
Retrieve information about available language models
Otter Bridge is a lightweight, flexible MCP server that connects applications to various Large Language Model providers. It provides a clean, composable interface for interacting with LLMs, currently supporting Ollama with planned expansion to ChatGPT and Claude. Designed for developers building agent workflows, it follows Anthropic’s principles of simplicity and composability.
Install prerequisites (Ollama and uv), clone the repository, install dependencies with uv add -r requirements.txt, copy .env.example to .env, and configure environment variables. Start the server manually with uv run server.py or automatically via MCP clients like Claude Desktop (add the server to the desktop configuration). Two tools are available: chat (send messages to LLMs) and list_models (retrieve available model information).
OLLAMA_BASE_URL, DEFAULT_MODEL)Currently, Ollama is supported. Support for ChatGPT is planned for Q2 2025 and Claude for Q3 2025.
You need Ollama installed and running on the default port, and the `uv` Python package manager installed.
Add the following configuration to your Claude Desktop config file (adjust the path to match your local installation): `{"otterbridge": {"command": "uv", "args": ["--directory", "<path-to-otterbridge>", "run", "server.py"]}}`.
- `OLLAMA_BASE_URL`: URL of the Ollama server (default: http://localhost:11434) - `DEFAULT_MODEL`: Default model to use (default: llama3.3)
It exposes two tools: `chat` for sending

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