"Ollama" matching MCP servers:

Hardened MCP bridge to the full Ollama ecosystem — local and cloud models. 6 tools covering health, model management, chat, and generation with SSRF mitigation, singleton HTTP client, and structured error handling. Two deps, one file.

Integrates Ollama's local AI models with MCP clients, enabling listing models, viewing model details, and asking questions to models.

Offloads cheap work from cloud LLM agents to a local Ollama model, reducing costs and keeping frontier models focused on complex tasks.

MCP server that lets Claude Code offload simple tasks like code explanation, writing tests, and adding comments to a local Ollama model, saving Claude API tokens.

An MCP server that lets Claude Code delegate coding tasks such as code generation, review, refactoring, and test writing to a local Ollama instance running a Qwen3 model.

A minimal local vision bridge for text-only VS Code Copilot, enabling it to see images by sending them to a local Ollama vision model and returning text descriptions. It provides MCP tools for image description, listing, OCR, and status checks.

Exposes a local Ollama fleet as MCP tools for Claude Code, enabling model listing, generation, fleet health checks, parallel comparisons, smart host routing, and model pulling across hosts.

A Python MCP server that gives vision capabilities to text-only LLMs by exposing an analyze_image tool that sends local images to a vision-capable Ollama model and returns textual descriptions.

MCP server enabling LLM clients without vision capability to process images by delegating to local Ollama vision models. Supports describing images, OCR, asking questions, and processing clipboard images.

MCP Ollama server integrates Ollama models with MCP clients, allowing users to list models, get detailed information, and interact with them through questions.

Enables AI agents to interact with local Ollama models for text generation and tool calling with prompt injection protection.

Enables AI assistants to perform semantic search and retrieval across codebases stored in Qdrant, using Ollama models for GPU-accelerated embeddings.

Enables complete local Ollama management including listing models, chatting with local LLMs, starting/stopping the server, and getting intelligent model recommendations for specific tasks through natural language commands.

A comprehensive MCP proxy server that bridges MCP clients with Ollama local language models, providing advanced features like RAG integration, context management, caching, and production-ready security.

Exposes local Ollama instances as tools for Claude Code, allowing users to offload code generation, text drafting, and embedding tasks to local GPUs. It supports multi-turn conversations and model management through the Model Context Protocol.

A Python MCP server that exposes local Ollama models as tools for AI assistants, enabling chat, generation, embeddings, and model management without cloud APIs.

Enables local image analysis and document processing through MCP workers (Vision and Document) using Ollama models, with cloud orchestration for mixed hierarchical tasks. Supports tools for image understanding, requirement extraction, test case generation, and GPU memory lifecycle management.

A lightweight MCP server that exposes Ollama Cloud models as tools for chat, web search, and web fetching, with dynamic model switching.

A local MCP server that gives Ollama language models sandboxed file management tools—read, write, list, and recoverable delete—within configurable workspace directories, keeping everything local and secure.

Enables text-only AI models to analyze images via local Ollama multimodal models. Supports image analysis, OCR, and multi-image comparison entirely offline.