Provides tools for generating text, images, videos (Veo), and transcribing audio using Google Gemini's API.
Click on "Install 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., "@Gemini MCP Create a haiku about AI"
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.
Remote MCP server that exposes Google Gemini's text, image, video (Veo), and audio transcription capabilities as tools any MCP client (Claude.ai, Claude Code, Cowork) can call directly. Same architecture as the Bosta and Salestrail MCP connectors: Node.js + Express, deployed as a Render web service, talking to Gemini's REST API.
Tool | What it does |
| Text generation — captions, copy, summaries, translation, analysis. Optional |
| Generate an image from a prompt, or edit/compose an existing one via |
| Kicks off a Veo video job from a prompt (+ optional reference image for image-to-video). Returns an |
| Polls an |
| Transcribes audio (m4a/mp3/wav/aac/ogg/flac) from base64, with an optional instruction (e.g. "transcribe in Egyptian Arabic"). |
All tools return MCP error results (isError: true) on failure rather than crashing
the server — confirmed against the live Gemini API during build (an invalid key
correctly came back as a tool error, not a connection drop).
Related MCP server: Nano Banana MCP
Audio inline size limit: requests with inline base64 data top out around ~20MB total. Longer call recordings will need the Gemini File API (upload first, then reference by URI) — not implemented yet. Worth adding if your typical .m4a files are long.
.m4a MIME type: the tool defaults to audio/mp4 for .m4a files, since that's
the container format (AAC inside MP4). This has not been verified against a real
audio file yet. If Gemini rejects it, try audio/aac instead — pass it explicitly
via the mime_type argument.
Veo response shape: check_video_status tries a couple of known response
shapes (generateVideoResponse.generatedSamples[0].video and predictions[0]).
Worth confirming against a real job once you have Veo access, since Google has
changed this shape across API versions before.
Veo access: Veo 3 may require allowlisting / billing on your Gemini API key —
check the Gemini API console if start_video_generation errors out.
npm install
cp .env.example .env # fill in GEMINI_API_KEY
npm startHealth check: curl http://localhost:3000/health
Push this folder to a GitHub repo.
Render dashboard → New → Web Service → connect the repo.
Build command: npm install
Start command: npm start
Add environment variable GEMINI_API_KEY (and optionally the model overrides
from .env.example) in Render's Environment tab.
Once deployed, your MCP endpoint is https://<your-app>.onrender.com/mcp.
Add it as a custom connector pointing at https://<your-app>.onrender.com/mcp,
the same way Bosta MCP / Salestrail MCP are connected.
This server cannot be installed
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If you are the server author, to access and configure the admin panel.
MCP server for Google Veo AI video generation
OCR, transcription, file extraction, and image generation for AI agents via MCP.
Remote MCP for Gemini upgrade evals, prompt regressions, output diffs, and eval receipts.
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