"A server for local file operations on Windows that integrates with Ollama" matching MCP servers:

A server that connects Unity with local large language models through Ollama, enabling developers to automate workflows, manipulate assets, and control the Unity Editor programmatically without relying on cloud-based LLMs.

Enables AI agents to offload mechanical, high-token work to local Ollama models through MCP, with role-based model discovery, batch processing, and file-aware inputs.

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

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 bridge that integrates Ollama's local LLM capabilities into MCP-powered applications, enabling users to run, manage, and interact with AI models locally with full control and privacy.

A bridge that enables Claude Code to interact with local Ollama instances for text generation, multi-turn chat, and vision-based analysis. It supports model management tasks such as listing, pulling, and showing details, alongside generating text embeddings.

Enables interaction with locally running Ollama models through chat, generation, and model management operations. Supports listing, downloading, and deleting models while maintaining conversation history for interactive sessions.

A server that enables seamless integration between local Ollama LLM instances and MCP-compatible applications, providing advanced task decomposition, evaluation, and workflow management capabilities.

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

Enables a local-first autonomous coding agent that plans, edits, and verifies code using LM Studio, Ollama, or llama.cpp models, with hardware-aware parallel sub-agents and optional frontier-model planning.

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 Windows-only MCP server that gives AI models eyes and hands on a Windows machine, enabling file reading, screenshots, mouse/keyboard injection, and an auto-debug loop with a safety-focused watchdog for protected abort regions.

A generic OpenAI-compatible subagent MCP server that lets Claude Code delegate coding grunt-work to a local model via a tool-calling agent loop, with configurable concurrency and backend.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Enables any MCP client to delegate a scoped task to a local Ollama model that runs its own tool-calling loop for file operations and web search, with an allow-listed folder security model.

A local-first MCP memory server that gives AI coding agents persistent memory with hybrid semantic and keyword retrieval, working fully offline.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI-to-AI consultation for critical thinking and complex reasoning via OpenRouter, allowing one AI to delegate tasks to another AI model.