MCP Client for Ollama (ollmcp)

Name: MCP Client for Ollama (ollmcp)
Author: jonigl

@jonigl

关于 MCP Client for Ollama (ollmcp)

Harness the power of local LLMs with this TUI MCP Client for Ollama. Featuring all core MCP primitives (tools, prompts, resources), agent mode, multi-server, model switching, streaming responses, human-in-the-loop, thinking mode, model params config, system prompts, and saved pre

#open-source #ai #mcp #sse #model-selection #stdio #command-line-tool #terminal-based #pypi-package #tool-management #chat-interface #llm #generative-ai #local-llm #ollama #model-context-protocol #mcp-server #mcp-client

基本信息

分类

AI 与智能体

许可证

MIT

运行时

python

传输方式

stdio

发布者

jonigl

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-client-for-ollama": {
      "command": "uv",
      "args": [
        "tool",
        "install",
        "--upgrade",
        "ollmcp"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Client for Ollama (ollmcp)?

MCP Client for Ollama (ollmcp) is a Python terminal application that connects local Ollama LLMs to one or more Model Context Protocol (MCP) servers. It supports core MCP primitives—tools, prompts, and resources—and is designed for developers building, testing, or exploring MCP-powered workflows with local AI models.

How to use MCP Client for Ollama (ollmcp)?

Install the package via uv tool install --upgrade ollmcp or pip install --upgrade ollmcp. Add an MCP server with ollmcp mcp add <name> -- <command>, then launch the client by running ollmcp. Use /help inside the interactive session for available commands.

Key features of MCP Client for Ollama (ollmcp)

Agent Mode with iterative tool execution and configurable loop limits
Multi-Server Support and multiple transport types (STDIO, SSE, Streamable HTTP)
Human-in-the-Loop (HIL) tool execution for safety
Rich terminal interface with streaming responses and fuzzy autocomplete
Dynamic model switching and advanced model parameter configuration
Server hot-reloading and conversation history management
Support for Ollama (default) and OpenAI-compatible LLM providers

Use cases of MCP Client for Ollama (ollmcp)

Connect local Ollama models to existing MCP tool servers for automated tasks
Prototype and debug MCP servers during development with hot-reloading
Run multi-step agent workflows with strict human approval on tool calls
Experiment with different LLM providers and model parameters in a single session

FAQ from MCP Client for Ollama (ollmcp)

What are the runtime requirements?

Python 3.11 or newer, Ollama running locally, and the UV package manager (recommended). pip installation is also supported.

How do I fix a "Could not find a version that satisfies the requirement ollmcp" error?

This usually means your Python version is older than 3.11. Use uv tool install --upgrade ollmcp (uv auto-selects a compatible Python) or install with a Python 3.11+ interpreter.

Can I use cloud-hosted models instead of a local Ollama instance?

Yes. Ollama Cloud is supported, and you can also use OpenAI-compatible providers such as OpenAI, OpenRouter, and DeepSeek.

What types of MCP servers does ollmcp connect to?

It supports STDIO, SSE, and Streamable HTTP transport types. It can connect to multiple servers simultaneously and automatically discovers existing Claude MCP configurations.

How does tool safety work?

Human-in-the-Loop (HIL) mode lets you review and approve tool executions before they run. You can also enable/disable specific tools or entire servers during a chat session.

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