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 · ★ 99
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ollama-mcp-bridge safe to install? View the security audit →
Provides an API layer in front of the Ollama API, seamlessly adding tools from multiple MCP servers so every Ollama request can access all connected tools transparently. Ollama MCP Bridge Table of Contents Features Requirements Installation Quick Start Or, install from PyPI with pip Or, run with Docker Compose ✨NEW [Or, run with
| Stars | 99 |
| Forks | 37 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74.4241694493076/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-18 |
| Created | 2025-06-13 |
| Platforms | cli, docker, mcp, python |
| Est. Tokens | ~19k |
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ollama-mcp-bridge is Extend the Ollama API with dynamic AI tool integration from multiple MCP (Model Context Protocol) servers. Fully compatible, transparent, and developer-friendly, ideal for building powerful local LLM . It is categorized as a MCP Server with 99 GitHub stars.
ollama-mcp-bridge is primarily written in Python. It covers topics such as ai, ai-agents, api.
You can find installation instructions and usage details in the ollama-mcp-bridge GitHub repository at github.com/jonigl/ollama-mcp-bridge. The project has 99 stars and 37 forks, indicating an active community.
ollama-mcp-bridge is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ollama-mcp-bridge on Agent Skills Hub include guaardvark, Context-Engine, MCPSharp. 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: