Notebook Intelligence (NBI) is an AI coding assistant and extensible AI framework designed for JupyterLab. It supports models from GitHub Copilot and other LLM providers, including local Ollama models. It significantly enhances productivity through features such as code generation, auto - completion, and chat interfaces, and supports the integration of Model Context Protocol (MCP) services.

The StrawHat AI development repository provides a complete solution for building local and online AI development environments, including various tools such as the Ollama backend, Open - WebUI frontend, and Claude desktop proxy, supporting rapid deployment and development of AI applications.

An interactive chat assistant that combines the capabilities of the Ollama large - model and PostgreSQL database access, supporting natural language querying of databases and generating SQL queries.

This project is a deep research server based on the Ollama local large - model, providing automated research tools through the MCP protocol, capable of iterative search, analysis, and summarization of complex topics.

This project is an experimental Model Context Protocol (MCP) server for interacting with the Algolia API. It allows users to perform operations such as searching, adding data, and updating index configurations through the MCP protocol. The project provides installation guides, debugging methods, and instructions for integrating with Ollama, but clearly states that it does not provide official support.

A deep research MCP service based on Ollama, realizing automated web search and knowledge synthesis through local LLM models

The Multi-Model Advisor is a multi - model consulting server based on Ollama, which provides more comprehensive answers to questions by integrating the viewpoints of multiple AI models.

This project is a Python adapter that exposes the tools of MCPO (MCP-to-OpenAPI proxy server) as Ollama-compatible functions, supporting connection to existing MCPO instances or starting a local service.

Ollama Chat with MCP is a project that demonstrates how to integrate a local language model with real-time web search functionality through the Model Context Protocol (MCP). It includes an MCP web search server, a terminal client, and a Gradio-based web front-end, enabling locally run LLMs to access external tools and data sources.

OtterBridge is a lightweight MCP server used to connect applications with multiple large language model providers, supporting models such as Ollama, with a simple and flexible design.

A Model Control Protocol server designed for Claude Desktop to communicate with the Ollama LLM server

The Deepseek Thinker MCP Server is an MCP service that provides Deepseek inference content, supporting both the OpenAI API and the local Ollama mode, and can be integrated into AI clients.

MCP LLM Bridge is a bridge tool to connect the Model Context Protocol (MCP) server with OpenAI-compatible LLMs (such as Ollama).

File Context MCP is a TypeScript-based application that provides an API to query large language models (LLMs) through local file content. It supports multiple LLM providers (Ollama and Together.ai) and can process multiple file types to generate context-aware responses.

A Model Context Protocol server for integrating Ollama with MCP clients such as Claude Desktop

The Ollama MCP Server is a bridge tool that connects the local large language models of Ollama and the Model Context Protocol (MCP). It provides complete API integration, model management, and execution functions, and supports OpenAI - compatible chat interfaces and visual multimodal models.

Ollama MCP Server is a protocol server that connects local Ollama LLM models with MCP - compatible applications, providing functions such as model management and conversation interaction.

Homelab MCP is a collection of Model Context Protocol servers used to manage and monitor home lab infrastructure through Claude Desktop. It supports multiple services such as Docker, Ollama, Pi-hole, Unifi, and UPS, and offers both unified and independent deployment modes.

This project implements the integration of the TypeScript MCP proxy with Ollama. Through a unified interface, AI models can call multiple tools, supporting functions such as file operations and web searches.

The MCP Document Indexer is a Python - based local document indexing and search server that uses the LanceDB vector database and a local LLM (through Ollama) to implement real - time monitoring, multi - format document processing, and semantic search, and provides tools for AI assistants such as Claude through the Model Context Protocol (MCP).