Ollama MCP Server

Overview

The Ollama‑MCP‑Server bridges local Ollama large language model (LLM) instances with any MCP‑compatible AI assistant, enabling a fully integrated workflow for task decomposition, evaluation, and model execution. By exposing a rich set of resources—, , and —the server gives assistants a standardized way to create, manage, and interrogate work items while keeping the underlying LLM logic encapsulated.

At its core, the server solves a common pain point for developers: orchestrating complex, multi‑step reasoning with a local LLM without having to write custom adapters for each assistant. It offers high‑level prompts such as decompose-task and evaluate-result, which translate natural language problem descriptions into structured sub‑tasks or quality metrics. Corresponding tools like add-task, decompose-task, and evaluate-result provide the executable handlers that actually invoke Ollama, apply caching, and return results in a consistent MIME format. This separation of prompt (schema) and tool (handler) mirrors the design principles of modern AI workflows, making it easier to plug new capabilities or swap out models.

Key features include:

• Task orchestration: Create, decompose, and track tasks with priority, deadlines, and tags.
• Result evaluation: Analyze outputs against custom criteria and receive actionable feedback.
• Model management: Run any Ollama model, with a flexible priority system that respects tool parameters, MCP config files, environment variables, and defaults.
• Performance optimization: Connection pooling and an LRU cache reduce latency and server load, while detailed error messages aid rapid debugging.
• Extensibility: Developers can add new prompts and tools by defining schemas, enabling rapid expansion of the assistant’s capabilities.

Real‑world use cases include:

• Software engineering pipelines where an AI assistant drafts code, decomposes it into testable units, and evaluates the quality of each module.
• Data science workflows that require iterative model tuning; tasks can be broken into data preparation, feature engineering, and evaluation steps.
• Customer support automation where a bot creates ticket‑handling tasks, delegates subtasks to human agents, and evaluates response quality.

Integration is seamless: an MCP‑compatible client (e.g., Claude Desktop) sends a prompt to the server, receives a structured response, and may trigger subsequent tool calls. The server’s standardized URIs allow the client to reference tasks or results directly, enabling stateful interactions across sessions. With its robust error handling and clear logging, developers can quickly diagnose issues such as missing models or task‑not‑found errors. Overall, the Ollama‑MCP‑Server empowers AI assistants to harness local LLM power in a scalable, maintainable, and developer‑friendly manner.