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Otter Bridge MCP Server

@typangaa

Repository

Basic information

Category

Developer Tools

Transports

stdio

Author

typangaa

Submitted by

Alex Pang

About Otter Bridge MCP Server

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

#otterbridge#llm-connector#server

Otter Bridge MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "otterbridge": {
      "command": "uv",
      "args": [
        "run",
        "server.py"
      ]
    }
  }
}

Otter Bridge MCP Server tools

2

Send messages to LLMs and get AI-generated responses

Retrieve information about available language models

Overview

What is Otter Bridge?

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.

How to use Otter Bridge?

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).

Key features of Otter Bridge

  • Provider-agnostic design (currently Ollama, ChatGPT and Claude planned)
  • Simple, composable architecture inspired by effective agent patterns
  • Lightweight server built with FastMCP
  • Easy model management and retrieval of model capabilities
  • Configurable via environment variables (OLLAMA_BASE_URL, DEFAULT_MODEL)

Use cases of Otter Bridge

  • Integrate a local LLM (Ollama) into any MCP-compatible application
  • Build custom agent workflows that switch between providers as they become available
  • Prototype LLM-powered features using a fast, local server before moving to cloud APIs
  • Seamlessly connect Claude Desktop with Ollama models for on-premise AI assistance

Frequently asked questions

1

What LLM providers does Otter Bridge support?

Currently, Ollama is supported. Support for ChatGPT is planned for Q2 2025 and Claude for Q3 2025.

2

What are the prerequisites for running Otter Bridge?

You need Ollama installed and running on the default port, and the `uv` Python package manager installed.

3

How do I integrate Otter Bridge with Claude Desktop?

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"]}}`.

4

What environment variables can I configure?

- `OLLAMA_BASE_URL`: URL of the Ollama server (default: http://localhost:11434) - `DEFAULT_MODEL`: Default model to use (default: llama3.3)

5

What tools does Otter Bridge expose?

It exposes two tools: `chat` for sending

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