mcp-gemini-search — security grade SAFE, quality 71/100

Security audit verdict: SAFE · quality 71/100

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 arjunprabhulal · MCP Server · ★ 55

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

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About mcp-gemini-search

Gemini Function Calling + Model Context Protocol(MCP) Flight Search This project demonstrates how to use Google's Gemini 2.5 Pro with function calling capabilities to interact with the tool via Model Context Protocol (MCP). This client implementation shows how to: Connect to a local MCP server process () using stdio communication Use natural language prompts with Gemini 2.5 Pro to search for flights (e.g., "Find flights from Atlanta to Las Vegas on 2025-05-05") Let Gemini automatically determine the correct function parameters from the natural language input Execute the flight search using the MCP tool Display formatted results from the search Features Natural language flight search using Gemini 2.5 Pro Automatic parameter extraction via function calling Integration with tool via stdio Formatted JSON output of flight results Environment-based configuration for API keys Prerequisites Before running this client, you'll need: Python 3.7+ A Google AI Studio API key for Gemini A SerpAPI key (used by the flight search tool) The package installed Dependencies This project relies on several Python packages: google-generativeai: Google's official Python library for accessing Gemini 2.5 Pro...

aifunction-callinggeminillmmachine-learningmcpmcp-servermodel-context-protocolserpapistdio

Quick Facts

Stars55
Forks20
LanguagePython
CategoryMCP Server
Quality Score71.0369151474749/100
Open Issues1
Last Updated2026-08-12
Created2025-04-04
Platformsgemini, mcp, python
Est. Tokens~14k

Compatible Skills

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

  • empower-functions — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (57%)
  • Eridanus — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (56%)
  • openai-function-calling — semantic(0.30)+complementary+same_lang+similar_pop+shared_platform (56%)

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Frequently Asked Questions

What is mcp-gemini-search?

mcp-gemini-search is Model Context Protocol (MCP) with Gemini 2.5 Pro. Convert conversational queries into flight searches using Gemini's function calling capabilities and MCP's flight search tools. It is categorized as a MCP Server with 55 GitHub stars.

What programming language is mcp-gemini-search written in?

mcp-gemini-search is primarily written in Python. It covers topics such as ai, function-calling, gemini.

How do I install or use mcp-gemini-search?

You can find installation instructions and usage details in the mcp-gemini-search GitHub repository at github.com/arjunprabhulal/mcp-gemini-search. The project has 55 stars and 20 forks, indicating an active community.

What are the best alternatives to mcp-gemini-search?

The top alternatives to mcp-gemini-search on Agent Skills Hub include vurb.ts, decipher-research-agent, MetaSearchMCP. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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