Ollama MCP Server

The Ollama MCP Server bridges local LLM models managed by the Ollama runtime with any Model Context Protocol (MCP)‑compatible AI assistant, such as Claude Desktop or Cline. By exposing a lightweight HTTP interface that follows the MCP specification, it lets developers treat Ollama models as first‑class resources in their assistant workflows. This solves the common pain point of having to write custom adapters for each new model or platform: a single, well‑defined MCP server handles discovery, deployment, and interaction with any model available through Ollama.

At its core, the server provides a concise set of endpoints that mirror typical LLM operations. Clients can query to retrieve the list of locally available models, pull new ones via , and obtain detailed metadata through . The chat endpoint forwards user prompts to the selected Ollama model and streams back responses, allowing assistants to maintain conversational state without needing direct access to the Ollama API. The server also automatically manages its listening port, defaulting to 3456 but easily overridden with an environment variable. This eliminates the need for manual port configuration in complex deployment scenarios.

Key capabilities include:

• Model discovery and metadata: Exposes a clean, JSON‑based view of all Ollama models, enabling assistants to present choices or auto‑select the most suitable model for a task.
• Dynamic deployment: The pull endpoint allows on‑demand installation of new models, supporting use cases where an assistant needs a specialized model that is not yet present locally.
• Chat integration: By mapping the MCP chat flow to Ollama’s chat API, developers can offload language generation to local hardware while still leveraging the assistant’s higher‑level reasoning and tool‑calling logic.
• Environment flexibility: Configuration through environment variables (, ) makes the server adaptable to containerized or cloud‑native setups.

Real‑world scenarios that benefit from this server include:

• Privacy‑centric applications: Organizations can run a Claude Desktop instance that delegates heavy language generation to on‑premise Ollama models, ensuring data never leaves the local network.
• Hybrid AI pipelines: A developer can combine a Claude assistant’s prompt‑engineering strengths with Ollama’s fast, lightweight models for tasks like code completion or data summarization.
• Rapid prototyping: New model experiments can be pulled and tested immediately through the MCP interface, speeding iteration without redeploying the assistant.

Integrating the Ollama MCP Server into an existing AI workflow is straightforward: add its configuration to the target application’s MCP settings, then reference the server URL when creating or selecting a model. The assistant automatically discovers available models and can switch between them on the fly, all while remaining agnostic to the underlying LLM implementation.

What sets this server apart is its combination of simplicity and compliance. It offers a minimal, well‑documented API that requires no code changes in the assistant, while its AGPL‑3.0 license protects contributors by preventing unlicensed commercial use. This makes it an ideal choice for developers who need a reliable, open‑source bridge between local LLMs and modern AI assistants.