No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by jonigl · MCP Server · ★ 823
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-client-for-ollama safe to install? View the security audit →
A simple yet powerful Python client for interacting with Model Context Protocol (MCP) servers using Ollama, allowing local LLMs to use tools. MCP Client for Ollama (ollmcp) 🎥 Watch this demo as an Asciinema recording Table of Contents Overview Features Requirements Quick Start Usage [Command-line Arguments](#command-line-argu
| Stars | 823 |
| Forks | 124 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.4102904701637/100 |
| Open Issues | 17 |
| Last Updated | 2026-09-14 |
| Created | 2025-04-23 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~23k |
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mcp-client-for-ollama is 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, huma. It is categorized as a MCP Server with 823 GitHub stars.
mcp-client-for-ollama is primarily written in Python. It covers topics such as agentic-ai, ai, command-line-tool.
You can find installation instructions and usage details in the mcp-client-for-ollama GitHub repository at github.com/jonigl/mcp-client-for-ollama. The project has 823 stars and 124 forks, indicating an active community.
mcp-client-for-ollama is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-client-for-ollama on Agent Skills Hub include toolhive-studio, daymon, volcano-agent-sdk. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: