Gemini Thinking Mcp
@falahgs
About Gemini Thinking Mcp
No overview available yet
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"gemini-thinking": {
"command": "node",
"args": [
"/path/to/gemini-thinking-mcp/build/index.js"
],
"env": {
"GEMINI_API_KEY": ""
}
}
}
}Tools
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Overview
What is Gemini Thinking Mcp?
Gemini Thinking Mcp is a Model Context Protocol (MCP) server that integrates Google’s Gemini-2.0-flash-thinking-exp-01-21 model with Claude Desktop. It enables Claude users to leverage Gemini’s fast, high‑quality reasoning for tasks such as mathematical problem solving, logical deduction, and step‑by‑step analysis.
How to use Gemini Thinking Mcp?
Install Node.js v18+, clone the repository, run npm install, create a .env file with your GEMINI_API_KEY, build with npm run build, then add a gemini-thinking entry to Claude Desktop’s mcpServers configuration. In conversations, invoke the model with @gemini-thinking followed by your prompt.
Key features of Gemini Thinking Mcp
- Exposes Gemini‑2.0‑flash‑thinking‑exp‑01‑21 to Claude Desktop
- Optimized for fast, high‑quality reasoning
- Supports mathematical reasoning and logical deduction
- Enables step‑by‑step structured thinking
- Configurable parameters: prompt, max_tokens, temperature
- Local setup with a single API key
Use cases of Gemini Thinking Mcp
- Solving mathematical equations step by step
- Performing logical deduction and analytical tasks
- Generating structured, reasoned explanations in Claude conversations
FAQ from Gemini Thinking Mcp
What are the prerequisites for using Gemini Thinking Mcp?
Node.js v18 or higher, npm, Claude Desktop, and a valid Google Gemini API key.
How do I configure Claude Desktop to use this server?
Add a JSON object to the mcpServers section of Claude Desktop’s config file, specifying the node command, the path to the built server (build/index.js), and the GEMINI_API_KEY environment variable.
What parameters can I adjust when calling Gemini Thinking Mcp?
You can set prompt (required), max_tokens (default 8192), and temperature (default 0.2) to control the model’s output.
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