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Claude Code Gemini MCP

Claude Code Gemini MCP

by ShunL12324

Claude Code Gemini MCP

MCP Server that enables Claude Code to call Gemini models through OpenAI-compatible API.

Installation

git clone https://github.com/shun-sfoo/claude-code-gemini-mcp.git
cd claude-code-gemini-mcp
npm install
npm run build

Related MCP server: MCP Gemini Server

Configuration

Environment Variables

Variable

Description

Required

GEMINI_API_KEY

Your API key

Yes

GEMINI_BASE_URL

API endpoint (OpenAI-compatible)

Yes

GEMINI_MODEL

Model ID (default: gemini-3-pro-preview)

No

Claude Code Integration

Add to your project's .mcp.json:

{
  "mcpServers": {
    "gemini": {
      "command": "node",
      "args": ["/path/to/claude-code-gemini-mcp/dist/index.js"],
      "env": {
        "GEMINI_API_KEY": "your-api-key",
        "GEMINI_BASE_URL": "https://your-api-endpoint/v1",
        "GEMINI_MODEL": "gemini-3-pro-preview"
      }
    }
  }
}

Or add to global settings (~/.claude/settings.json).

Available Tools

gemini_think

Deep analysis and reasoning for complex problems.

Parameters:

  • problem (string, required) - The problem to analyze

  • context (string, optional) - Background information

  • thinkingStyle (string, optional) - One of: analytical, creative, critical, systematic

gemini_brainstorm

Generate multiple creative ideas with pros and cons.

Parameters:

  • topic (string, required) - The topic to brainstorm

  • constraints (string, optional) - Constraints or requirements

  • count (number, optional) - Number of ideas, 3-10 (default: 5)

gemini_review

Code, architecture, security, or performance review.

Parameters:

  • content (string, required) - Content to review

  • reviewType (string, required) - One of: code, architecture, security, performance

  • focus (string, optional) - Specific aspects to focus on

gemini_query

General purpose query with full control.

Parameters:

  • prompt (string, required) - The prompt to send

  • systemPrompt (string, optional) - System prompt

  • temperature (number, optional) - 0-2 (default: 0.7)

Testing

# Create .env file first
cp .env.example .env
# Edit .env with your credentials

# Run tests
npm test

Development

npm run dev    # Watch mode
npm run build  # Build
npm test       # Run tests

License

MIT

Available Tools

4 tools
gemini_brainstormC

Let Gemini brainstorm on a topic and generate multiple creative ideas or solutions with pros and cons

Inputs: topic (required), constraints, count.

gemini_queryC

Send any query to Gemini-3-pro-preview model

Inputs: prompt (required), systemPrompt, temperature.

gemini_reviewC

Let Gemini review code, architecture design, or technical solutions

Inputs: content (required), reviewType (required), focus.

gemini_thinkC

Let Gemini-3-pro-preview deeply analyze complex problems, perform reasoning and brainstorming

Inputs: problem (required), context, thinkingStyle.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    C2.9/5.0

    Scored across 4 tools

    Disambiguation2/5

    Multiple tools have overlapping purposes that could cause confusion. gemini_brainstorm and gemini_think both involve generating ideas and analysis, while gemini_query is a general-purpose tool that could potentially cover the same ground. The descriptions don't clearly differentiate when to use each tool versus the others.

    Naming Consistency5/5

    All tools follow a perfect gemini_verb pattern consistently throughout. The naming is completely predictable and follows the same structure for every tool, making it easy to understand the pattern.

    Tool Count3/5

    With only 4 tools, the server feels somewhat thin for a general-purpose AI assistant interface. While the count isn't extreme, it's borderline minimal for what appears to be a Gemini API wrapper that could benefit from more specialized operations.

    Completeness2/5

    There are significant gaps in the tool surface for a Gemini API wrapper. Missing are basic operations like summarization, translation, content generation, or structured output formatting. The tools focus narrowly on brainstorming/thinking/review while omitting many common LLM use cases that agents would expect.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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