Vexa
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Description from the maker (original English)
Model Context Protocol server for Google Gemini AI with chat, research, and grounding
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
GEMINI_API_KEYValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"gemini": {
"command": "npx",
"args": [
"-y",
"@houtini/gemini-mcp"
],
"env": {
"GEMINI_API_KEY": "your-value"
}
}
}
}Run this in your terminal
claude mcp add gemini --env GEMINI_API_KEY=your-value -- npx -y @houtini/gemini-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"gemini": {
"command": "npx",
"args": [
"-y",
"@houtini/gemini-mcp"
],
"env": {
"GEMINI_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"gemini": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@houtini/gemini-mcp"
],
"env": {
"GEMINI_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account houtini-ai, which owns the repo
We have not read this repo from GitHub yet
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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You need to fill in: GEMINI_API_KEY
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (2.10.1) reached the registry 14 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Vexa, emailmd, MCP WhatsApp Cloud
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I've had this Gemini MCP server running in my Claude Desktop setup for the best part of a year now. It's one of the few I leave switched on permanently. Not because Gemini replaces Claude (it doesn't), but because grounded search, image generation, SVG diagrams and video are things Gemini happens to do well, and having them as tools inside Claude beats flipping between browser tabs.
Fourteen tools, built around the models people actually come looking for: Nano Banana Pro (gemini-3-pro-image) and Nano Banana 2 for images, Veo 3.1 for video with synchronised audio, Gemini 4 Argon for chat and Gemini 3.1 Pro for deep research, both with Google Search grounding. Everything previews inline in Claude Desktop through MCP Apps rather than landing as a file path you have to go and open.
One npx command. That's it.
New here? The full user guide walks through every tool, the free-tier gotcha, and what to do when something breaks.
Quick Navigation
What it makes | Get started | What it does | Image output | Configuration | Tools | Models | Requirements
Everything below came out of the tools in this repo, unretouched, on the afternoon I wrote this. Prompts are in the captions so you can judge for yourself.
Image generation with search grounding. One generate_image call with use_search=true. Gemini looked up the actual Met Office forecast for the week, then drew it. The dates and temperatures are real (well, as real as a forecast gets).
*Prompt: "A polished editorial infographic poster: London weather, this week. Use real forecast data from search for the next 5 days..." - gemini-3-pro-image, 16:9, 2K.*
Text that's actually spelled correctly. This is the thing Nano Banana Pro does that the previous generation of image models couldn't. Every label here is straight out of the model.
*Prompt: "A clean, magazine-quality infographic titled How an MCP tool call works, with four numbered steps..." - gemini-3-pro-image.*
Generate, then edit. Left is generate_image. Right is edit_image on that file with one sentence of instructions: change the track to Monza in daylight, swap the rim lighting for window light, make the pedals red. Same cockpit, same camera angle.
generate_image | edit_image |
|---|---|
Image to video with Veo 3.1. The night-time cockpit above, passed to generate_video as firstFrameImage. Eight seconds at 1080p with generated audio (engine note, tyre hiss) - the GIF below is silent and squashed for GitHub, the real file is a proper MP4.
SVG that you can actually use. Not a picture of a diagram - real vector markup you can drop into a page, edit by hand or commit to a repo. Both of these are the raw .svg files the tool wrote to disk.
generate_svg style=technical | generate_svg style=data-viz |
|---|---|
A landing page from a paragraph. generate_landing_page with a brief, a company name and a brand colour. Self-contained HTML, inline CSS, the little chart in the hero is animated SVG. Screenshot of the file opened in Chrome, nothing else touched.
And the fast one. Nano Banana 2 (gemini-3.1-flash-image) for when you want volume rather than 4K. This took about ten seconds.
Step 1: Get a Gemini API key
Go to Google AI Studio and create one.
A word on the free tier, because the defaults lean on a paid model. Google's pricing page gives Gemini 3 Flash Preview a free tier, but the flagship models are paid-only - and gemini-4-argon is the default for gemini_chat and generate_landing_page, with gemini-3.1-pro-preview the default for gemini_deep_research (synthesis) and analyze_image. On a free key those calls will fail unless you point them at a free model: set GEMINI_DEFAULT_MODEL=gemini-3-flash-preview (covers chat and landing pages), GEMINI_DEEP_RESEARCH_MODEL and GEMINI_IMAGE_ANALYSIS_MODEL likewise, or pass model per call. generate_svg already defaults to gemini-3-flash-preview. Check the pricing page for the other models before relying on them - Google changes these tiers.
Step 2: Add to your Claude Desktop config
Config file locations:
C:\Users\{username}\AppData\Roaming\Claude\claude_desktop_config.json~/Library/Application Support/Claude/claude_desktop_config.json{
"mcpServers": { "gemini": { "command": "npx", "args": ["@houtini/gemini-mcp"], "env": { "GEMINI_API_KEY": "your-api-key-here" } } } }
Step 3: Restart Claude Desktop
That's it. Tools show up automatically. npx pulls the package on first run, so there's no separate install.
For development, or if you'd rather not rely on npx:
git clone https://github.com/houtini-ai/gemini-mcp
cd gemini-mcp npm install --include=dev npm run build
Then point your config at the local build:
{
"mcpServers": { "gemini": { "command": "node", "args": ["C:/path/to/gemini-mcp/dist/index.js"], "env": { "GEMINI_API_KEY": "your-api-key-here" } } } }
Claude Code doesn't read claude_desktop_config.json. Use claude mcp add instead:
claude mcp add -e GEMINI_API_KEY=your-api-key-here -s user gemini -- npx -y @houtini/gemini-mcp
With an output directory for images and video:
claude mcp add \
-e GEMINI_API_KEY=your-api-key-here \ -e GEMINI_IMAGE_OUTPUT_DIR=/path/to/output \ -s user \ gemini -- npx -y @houtini/gemini-mcp
Check with claude mcp get gemini - you want to see Status: Connected.
Use gemini:gemini_chat to ask: "What changed in the MCP spec in the last month?"
Grounding is on by default. Gemini searches Google before it answers, so you get this month's information rather than a training-cutoff guess, and the sources come back as markdown links. For questions where you want pure reasoning ("explain this code", that sort of thing) set grounding: false.
Runs on gemini-4-argon (Gemini 4 Argon) unless you say otherwise. Pass model: "gemini-3.8-flash" if you'd rather have speed than depth, or model: "gemini-3.1-pro-preview" for the previous flagship. thinking_level works on Gemini 3.x and 4 models: high for the hard stuff, low to keep it snappy.
Use gemini:gemini_deep_research with:
research_question="What are the current approaches to AI agent memory management?" max_iterations=5
Runs grounded search passes and then writes them up as one report. The passes run on gemini-3.8-flash with low thinking (they're gathering facts, not reasoning about them), and the synthesis at the end runs on gemini-3.1-pro-preview with high thinking. Two passes plus a synthesis is the default and lands in two to three minutes.
That split matters more than it sounds. Earlier versions ran everything on Pro with full thinking, and one pass on a broad question could run past Claude Desktop's four-minute timeout on its own. Keep max_iterations at 2 or 3 in Claude Desktop; in an IDE or an agent framework, 5 to 7 produces noticeably better synthesis. focus_areas takes an array if you want to steer each pass.
Use gemini:generate_image with:
prompt="Stock price chart showing Apple (AAPL) closing prices for the last 5 trading days" use_search=true aspectRatio="16:9"
Default model is gemini-3-pro-image, which is Nano Banana Pro now that it's out of preview. It renders legible text, does 4K, and it's the only one that supports conversational editing. gemini-3.1-flash-image (Nano Banana 2) is the fast option - not far off in quality, a fraction of the time and the cost. There's a Lite variant too if you're doing hundreds.
With use_search=true, Gemini looks things up before it draws. Weather, prices, sports results, that kind of data-driven image works reliably. The full-resolution file always goes to disk; the inline preview is resized to fit the MCP transport cap but the original is untouched.
Use gemini:generate_video with:
prompt="A close-up shot of a futuristic coffee machine brewing a glowing blue espresso, steam rising dramatically. Cinematic lighting." resolution="1080p" durationSeconds=8
Google's Veo 3.1. Four to eight second clips at up to 4K with native, synchronised audio. It's asynchronous on Google's side and takes two to five minutes; the tool polls until it's ready so you don't have to.
Options worth knowing about:
aspectRatio - 16:9 landscape or 9:16 for verticalgenerateAudio - on by default; dialogue and effects that match the promptfirstFrameImage - animate from a still (that's how the cockpit clip above was made)referenceImages - up to three, for characteOnly the beginning is shown; read the rest in the repo