Claude Code Gemini MCP
Enables calling Gemini models through an OpenAI-compatible API, providing tools for deep analysis, brainstorming, code review, and general queries using Gemini's language models.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Claude Code Gemini MCP brainstorm 5 ideas for a new productivity app with gamification elements"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 buildRelated MCP server: MCP Gemini Server
Configuration
Environment Variables
Variable | Description | Required |
| Your API key | Yes |
| API endpoint (OpenAI-compatible) | Yes |
| Model ID (default: | 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 analyzecontext(string, optional) - Background informationthinkingStyle(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 brainstormconstraints(string, optional) - Constraints or requirementscount(number, optional) - Number of ideas, 3-10 (default: 5)
gemini_review
Code, architecture, security, or performance review.
Parameters:
content(string, required) - Content to reviewreviewType(string, required) - One of:code,architecture,security,performancefocus(string, optional) - Specific aspects to focus on
gemini_query
General purpose query with full control.
Parameters:
prompt(string, required) - The prompt to sendsystemPrompt(string, optional) - System prompttemperature(number, optional) - 0-2 (default: 0.7)
Testing
# Create .env file first
cp .env.example .env
# Edit .env with your credentials
# Run tests
npm testDevelopment
npm run dev # Watch mode
npm run build # Build
npm test # Run testsLicense
MIT
Available Tools
4 toolsgemini_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
Scored across 4 tools
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.
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.
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.
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
Related MCP Connectors
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Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
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