gemini-mcp-tool: MCP server that enables AI assistants to interact with Google Gemini CLI, leveraging Gemini's massive token

by jamubc · MCP Server · ★ 2.3k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install gemini-mcp-tool

claude mcp add gemini-cli -- npx -y gemini-mcp-tool

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 77/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aiclaudeclicodebase-analysisfile-analysisgeminimcpmodel-context-protocolnpmtypescript

Quick Facts

Stars2,284
Forks201
LanguageTypeScript
CategoryMCP Server
Quality Score77/100
Open Issues28
Last Updated2026-07-21
Created2025-06-29
Platformsclaude-code, cli, gemini, mcp, node
Est. Tokens~15k

About gemini-mcp-tool

Gemini MCP Tool 📚 View Full Documentation - Search me!, Examples, FAQ, Troubleshooting, Best Practices This is a simple Model Context Protocol (MCP) server that allows AI assistants to interact with the Gemini CLI. It enables the AI to leverage the power of Gemini's massive token window for large analysis, especially with large files and codebases using the syntax for direction.

Compatible Skills

These tools work well together with gemini-mcp-tool for enhanced workflows:

  • ax — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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