ollama-mcp

🥈Silver

Ollama MCP Server enables local LLM access for AI assistants. Operations teams benefit from integrating local models with AI agents. Connects to Claude, Cursor, and other AI tools for secure, private AI operations.

148210Updated 3d ago

Intermediate30min to implementautomation

Saves ~50 min per use

Quick InstallView Source

git clone https://github.com/rawveg/ollama-mcp.git

Works with:

Claude

Overview

About This Skill

How to Use

["Install and configure the ollama-mcp server on your local machine. Follow the [official Ollama MCP documentation](https://github.com/ollama/ollama-mcp) to set up the server and ensure it’s running.","In your AI assistant (e.g., Claude, Cursor), enable the ollama-mcp server as a tool. For Claude, add it to your `claude_desktop_config.json` under the `mcpServers` section.","Use the prompt template to craft your request. Replace [TASK] with your specific goal (e.g., 'analyze,' 'summarize,' or 'generate'). Include [PLACEHOLDERS] like file paths, models, or context.","Execute the command or query in your AI assistant. Monitor the output for accuracy and refine your prompt if needed. For complex tasks, break them into smaller steps.","For iterative workflows, save frequently used prompts as templates in your AI assistant’s custom instructions or a notes file to streamline future use."]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/rawveg/ollama-mcp

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use the ollama-mcp server to [TASK] with a local LLM. Example: 'Summarize the contents of [FILE_PATH] using the ollama-mcp server with the mistral model. Provide a 3-bullet-point summary and highlight any action items.'

Example Output

### Summary of /workspace/docs/quarterly_report.md (Mistral Model via Ollama MCP)

**Key Findings:**
- Revenue grew 12% YoY to $14.2M, driven by strong enterprise adoption in the EMEA region (up 18% vs. Q3).
- Customer churn improved to 3.2% (down from 4.1% last quarter), with enterprise accounts showing the highest retention.
- Product usage data reveals a 22% increase in daily active users for the AI assistant feature, indicating high engagement.

**Action Items:**
- Schedule a follow-up with the EMEA sales team to discuss enterprise adoption strategies for Q1.
- Review churn reduction initiatives with the customer success team, focusing on enterprise accounts.
- Prepare a demo for the product team highlighting the AI assistant feature’s growth and user feedback.

*Model used: mistral (via ollama-mcp server)*
*File processed: /workspace/docs/quarterly_report.md*