OtterBridge

OtterBridge is a lightweight, provider‑agnostic MCP server designed to bridge the gap between applications and large language model (LLM) backends. By exposing a clean, composable interface, it lets developers integrate LLM capabilities into their workflows without wrestling with provider‑specific APIs. The server is built on FastMCP, ensuring reliable performance while keeping the codebase minimal and easy to extend.

The core problem OtterBridge solves is infrastructure friction: developers often need to write custom adapters for each LLM provider they wish to use, leading to duplicated effort and inconsistent interfaces. OtterBridge abstracts these differences behind a single set of MCP resources, tools, and prompts. At present it supports Ollama locally but is architected to add ChatGPT, Claude, and future providers with minimal changes. This means a single tool call can route to any underlying model, simplifying agent design and reducing maintenance overhead.

Key capabilities include:

• Provider‑agnostic tooling – the tool forwards user messages to the chosen LLM, while queries the backend for available models and their metadata.
• Model management – developers can discover which models are accessible, how many tokens they support, and other runtime characteristics without leaving the MCP ecosystem.
• Composable architecture – built following Anthropic’s best‑practice guidelines, OtterBridge can be chained with other MCP services or integrated into larger agent frameworks.
• Lightweight deployment – a single Python file () plus minimal dependencies keeps the footprint small, making it suitable for local machines or containerized environments.

Real‑world scenarios that benefit from OtterBridge include:

• Rapid prototyping – quickly switch between local Ollama models and cloud providers while testing agent behavior.
• Hybrid workflows – use a local model for low‑latency tasks and fall back to a cloud provider for more complex reasoning, all through the same MCP interface.
• Educational tools – students can experiment with different LLMs without configuring multiple SDKs, focusing on the logic of their agents instead.

In practice, an AI assistant such as Claude Desktop can add OtterBridge to its configuration; the MCP client will automatically start the server when needed, and all tool calls are routed through the unified interface. This seamless integration removes manual setup steps, reduces configuration errors, and allows developers to concentrate on building higher‑level logic rather than plumbing.

Overall, OtterBridge offers a pragmatic solution for developers who need consistent, low‑overhead access to multiple LLM backends within the MCP ecosystem, delivering both flexibility and simplicity.