MCP LLM Bridge

Ollama MCP Bridge – Bringing Local LLMs to the Model Context Protocol

The Ollama MCP Bridge solves a common bottleneck for developers who want to run powerful open‑source language models locally while still enjoying the rich ecosystem of tools that Claude and other MCP‑enabled assistants provide. By translating the model’s natural language output into JSON‑RPC calls that MCP servers understand, it enables any Ollama‑compatible model to perform filesystem operations, web searches, GitHub interactions, Google Drive and Gmail tasks, memory management, and even image generation with Flux—all without leaving the local environment.

At its core, the bridge is a lightweight TypeScript service that orchestrates three responsibilities: it runs the LLM via Ollama, connects to one or more MCP servers, and routes tool calls based on the model’s structured output. Developers can configure a single file to specify which MCP servers are available, what directories the filesystem server may access, and how the LLM should be invoked. The bridge then exposes a simple REPL‑style interface where users can type prompts, list available tools, or exit. When a prompt contains an action such as “search the web for…”, the model emits a structured JSON object; the bridge validates this payload, forwards it to the appropriate MCP (e.g., Brave Search), and relays the results back to the user in natural language.

Key features that make this bridge valuable include dynamic tool routing—the ability to handle multiple MCPs in parallel; structured output validation, which ensures that only well‑formed tool calls are executed; and automatic tool detection that parses user intent to choose the right MCP without manual specification. Robust process management guarantees that the Ollama model stays responsive, while detailed logging and error handling provide transparency for debugging. The bridge also supports advanced use cases such as creating project directories, querying GitHub repositories, sending emails through Gmail, or generating images with Flux—all from a single prompt.

In real‑world scenarios, the bridge empowers developers to build fully autonomous local assistants that can read and write code, search the web for documentation, manage cloud resources, or generate visual assets—all while keeping data on their own machines. Teams that prioritize privacy, low latency, or offline capability will find the Ollama MCP Bridge a compelling addition to their AI workflow. Its straightforward configuration, combined with the extensibility of MCP servers, allows rapid prototyping and scaling from simple scripts to complex, multi‑tool pipelines.