Rank
83
A Model Context Protocol (MCP) server for GitLab
Traction
No public download signal
Freshness
Updated 2d ago
Crawler Summary
Professional Model Context Protocol server for Google Gemini AI models with enterprise-grade features @houtini/gemini-mcp $1 $1 $1 ${badge_line} **I've been running this MCP server in my Claude Desktop setup for several months, and it's one of the few I leave enabled permanently.** Not because Gemini replaces Claude -- it doesn't -- but because grounded search, deep research, image generation, and video are things Gemini does well. Having them as tools inside Claude beats switching between browser tabs. Thirteen tool Capability contract not published. No trust telemetry is available yet. 6 GitHub stars reported by the source. Last updated 2/25/2026.
Freshness
Last checked 2/25/2026
Best For
@houtini/gemini-mcp is best for mcp, model-context-protocol, gemini workflows where MCP compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB MCP, runtime-metrics, public facts pack
Professional Model Context Protocol server for Google Gemini AI models with enterprise-grade features @houtini/gemini-mcp $1 $1 $1 ${badge_line} **I've been running this MCP server in my Claude Desktop setup for several months, and it's one of the few I leave enabled permanently.** Not because Gemini replaces Claude -- it doesn't -- but because grounded search, deep research, image generation, and video are things Gemini does well. Having them as tools inside Claude beats switching between browser tabs. Thirteen tool
Public facts
4
Change events
0
Artifacts
0
Freshness
Feb 25, 2026
Capability contract not published. No trust telemetry is available yet. 6 GitHub stars reported by the source. Last updated 2/25/2026.
Trust score
Unknown
Compatibility
MCP
Freshness
Feb 25, 2026
Vendor
Houtini
Artifacts
0
Benchmarks
0
Last release
2.2.0
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. 6 GitHub stars reported by the source. Last updated 2/25/2026.
Setup snapshot
git clone https://github.com/houtini-ai/gemini-mcp.gitSetup complexity is MEDIUM. Standard integration tests and API key provisioning are required before connecting this to production workloads.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Houtini
Protocol compatibility
MCP
Adoption signal
6 GitHub stars
Handshake status
UNKNOWN
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
6
Snippets
0
Languages
typescript
json
{
"mcpServers": {
"gemini": {
"command": "npx",
"args": ["@houtini/gemini-mcp"],
"env": {
"GEMINI_API_KEY": "your-api-key-here"
}
}
}
}bash
git clone https://github.com/houtini-ai/gemini-mcp cd gemini-mcp npm install --include=dev npm run build
json
{
"mcpServers": {
"gemini": {
"command": "node",
"args": ["C:/path/to/gemini-mcp/dist/index.js"],
"env": {
"GEMINI_API_KEY": "your-api-key-here"
}
}
}
}text
Use gemini:gemini_chat to ask: "What changed in the MCP spec in the last month?"
text
Use gemini:gemini_deep_research with: research_question="What are the current approaches to AI agent memory management?" max_iterations=5
text
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"
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB MCP
Editorial quality
ready
Professional Model Context Protocol server for Google Gemini AI models with enterprise-grade features @houtini/gemini-mcp $1 $1 $1 ${badge_line} **I've been running this MCP server in my Claude Desktop setup for several months, and it's one of the few I leave enabled permanently.** Not because Gemini replaces Claude -- it doesn't -- but because grounded search, deep research, image generation, and video are things Gemini does well. Having them as tools inside Claude beats switching between browser tabs. Thirteen tool
I've been running this MCP server in my Claude Desktop setup for several months, and it's one of the few I leave enabled permanently. Not because Gemini replaces Claude -- it doesn't -- but because grounded search, deep research, image generation, and video are things Gemini does well. Having them as tools inside Claude beats switching between browser tabs.
Thirteen tools. One npx command.
Generated images and diagrams render inline in Claude Desktop with zoom controls, file paths, and prompt context:
| Image generation | SVG / diagram generation |
|:---:|:---:|
|
|
|
Step 1: Get a Gemini API key
Go to Google AI Studio and create one. The free tier covers most development use -- you'll hit rate limits on deep research if you're hammering it, but for day-to-day work it's fine.
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. The tools show up automatically. npx pulls the package on first run -- 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"
}
}
}
}
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 answering, so you get current information rather than training data cutoff answers. Sources come back as markdown links.
For questions where you want reasoning over live search -- "explain this code" or similar -- set grounding: false.
Supports thinking_level on Gemini 3 models: high for maximum reasoning depth, low to keep it fast, medium/minimal on Gemini 3 Flash only.
Use gemini:gemini_deep_research with:
research_question="What are the current approaches to AI agent memory management?"
max_iterations=5
Runs multiple grounded search iterations, then synthesises a full report. Takes 2-5 minutes depending on complexity. Worth it for anything where you need comprehensive coverage rather than a quick answer.
Set max_iterations to 3-4 in Claude Desktop (4-minute tool timeout). In IDEs (Cursor, Windsurf, VS Code) or agent frameworks with longer timeout tolerance, 7-10 iterations produces noticeably better synthesis. Pass focus_areas as an array to steer toward specific angles.
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-preview (Nano Banana Pro). Also supports gemini-2.5-flash-image for faster generation.
When use_search=true, Gemini searches Google for current data before generating. Financial and news queries work reliably and return 2-5 grounding sources as markdown links. Weather queries are inconsistent (Gemini API limitation, not a code issue).
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
Uses Google's Veo 3.1 model. Generates 4-8 second videos at up to 4K resolution with native synchronised audio. Processing takes 2-5 minutes -- the tool polls automatically until the video is ready.
Options worth knowing about:
aspectRatio -- 16:9 (landscape, default) or 9:16 (portrait/vertical)generateAudio -- on by default, produces dialogue and sound effects matching the promptsampleCount -- generate up to 4 variations in one callseed -- for deterministic output across runsgenerateThumbnail -- extracts a frame via ffmpeg (needs ffmpeg in PATH)generateHTMLPlayer -- creates a local HTML player alongside the videoUse gemini:generate_svg with:
prompt="Architecture diagram showing a microservices system with API gateway, three services, and a shared database"
style="technical"
width=1000
height=600
Generates clean, production-ready SVG code for diagrams, illustrations, icons, and data visualisations. Styles: technical (diagrams), artistic (illustrations), minimal (simple), data-viz (charts).
Conversational editing -- Gemini 3 Pro Image maintains context across editing turns using thought signatures. The server captures these automatically. Pass them back on subsequent edit calls for full continuity:
Use gemini:edit_image with:
prompt="Change the colour scheme to blue and green"
images=[{data: imageBase64, mimeType: "image/png", thoughtSignature: "fromPreviousCall"}]
Skip thought signatures and each edit starts from scratch.
Analysis -- two tools for different purposes:
describe_image -- Fast general descriptions using Gemini 3 Flashanalyze_image -- Structured extraction and detailed reasoning using Gemini 3.1 ProLoad local files:
Use gemini:load_image_from_path with filePath="C:/screenshots/error.png"
Returns base64 data ready for any image tool.
Reduce token usage by up to 75% whilst maintaining quality:
| Level | Tokens | Savings | Best for |
|-------|--------|---------|----------|
| MEDIA_RESOLUTION_LOW | 280 | 75% | Simple tasks, bulk operations |
| MEDIA_RESOLUTION_MEDIUM | 560 | 50% | PDFs/documents (OCR saturates here) |
| MEDIA_RESOLUTION_HIGH | 1120 | default | Detailed analysis |
| MEDIA_RESOLUTION_ULTRA_HIGH | 2000+ | per-image only | Maximum detail |
For PDF OCR, MEDIUM gives identical text extraction quality to HIGH at half the tokens. Set global_media_resolution to apply to all images, or override per-image with mediaResolution.
Use gemini:generate_landing_page with:
brief="A SaaS tool that helps developers monitor API latency"
companyName="PingWatch"
primaryColour="#6366F1"
style="startup"
sections=["hero", "features", "pricing", "cta"]
Returns a self-contained HTML file -- inline CSS and vanilla JS, no external dependencies. Styles: minimal, bold, corporate, startup.
The gemini_prompt_assistant tool includes 9 professional chart design systems:
| System | Inspiration | Best for | |--------|------------|----------| | storytelling | Cole Nussbaumer Knaflic | Executive presentations -- everything muted except one bold highlight | | financial | Financial Times | Editorial journalism -- FT Pink background, serif titles | | terminal | Bloomberg / Fintech | High-density dark mode with electric neon | | modernist | W.E.B. Du Bois | Bold geometric blocks, stark contrasts | | professional | IBM Carbon / Tailwind | Enterprise dashboards | | editorial | FiveThirtyEight / Economist | Data journalism | | scientific | Nature / Science | Academic rigour | | minimal | Edward Tufte | Maximum data-ink ratio | | dark | Observable | Modern dark mode |
Use gemini:gemini_prompt_assistant with:
request_type="template"
use_case="product"
desired_outcome="Generate a professional product comparison chart"
Use gemini:gemini_help with topic="overview"
Documentation for all features without leaving Claude. Topics: overview, image_generation, image_editing, image_analysis, chat, deep_research, grounding, media_resolution, models, all.
Default behaviour: Images return as inline base64 previews (quality 100, 1024px) rendered directly in Claude.
Persistent storage: Set GEMINI_IMAGE_OUTPUT_DIR to auto-save all generated images:
"env": {
"GEMINI_API_KEY": "your-api-key-here",
"GEMINI_IMAGE_OUTPUT_DIR": "C:/Users/username/Pictures/gemini-output"
}
Every image saves with a timestamp filename. The tool returns both the inline preview and the file path.
Per-call override: Pass outputPath on any generation tool to save to a specific location.
The server uses a two-tier compression approach to handle the MCP protocol's ~1MB JSON-RPC limit whilst preserving full-resolution files on disk:
| Tier | Quality | Max dimension | Purpose | |------|---------|---------------|---------| | Full-res | Original | Original | Saved to disk | | Viewer preview | 100 | 1024px | MCP App inline preview (~400KB) |
Gemini returns 2-5MB images. The full image is saved to disk immediately, and a compressed preview is created for the MCP App viewer.
| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| GEMINI_API_KEY | Yes | -- | Google AI API key from AI Studio |
| GEMINI_DEFAULT_MODEL | No | gemini-3.1-pro-preview | Default model for gemini_chat and analyze_image |
| GEMINI_DEFAULT_GROUNDING | No | true | Enable Google Search grounding by default |
| GEMINI_IMAGE_OUTPUT_DIR | No | -- | Auto-save directory for generated images |
| GEMINI_ALLOW_EXPERIMENTAL | No | false | Include experimental/preview models in auto-discovery |
| GEMINI_MCP_LOG_FILE | No | false | Write logs to ~/.gemini-mcp/logs/ |
| DEBUG_MCP | No | false | Log to stderr for debugging tool calls |
| Tool | Description |
|------|-------------|
| gemini_chat | Chat with Gemini 3.1 Pro. Google Search grounding on by default. Supports thinking_level for Gemini 3 |
| gemini_deep_research | Multi-step iterative research with Google Search. Synthesises comprehensive reports |
| gemini_list_models | Lists available models from the API |
| gemini_help | Documentation for all features without leaving Claude |
| gemini_prompt_assistant | Expert guidance for image generation with 9 chart design systems |
| generate_image | Image generation with search grounding and thought signatures for conversational editing |
| edit_image | Edit images with natural-language instructions. Supports multi-turn continuity |
| describe_image | Fast image descriptions using Gemini 3 Flash |
| analyze_image | Structured extraction and analysis using Gemini 3.1 Pro |
| load_image_from_path | Read a local image file and return base64 for any image tool |
| generate_video | Video generation with Veo 3.1 -- 4-8 seconds at up to 4K with native audio |
| generate_svg | Production-ready SVG graphics for diagrams, illustrations, and data visualisations |
| generate_landing_page | Self-contained HTML landing pages with inline CSS/JS |
| Model | Used by | Notes |
|-------|---------|-------|
| gemini-3.1-pro-preview | gemini_chat, analyze_image | Default. Advanced reasoning |
| gemini-3-pro-image-preview | generate_image, edit_image | Nano Banana Pro -- highest quality generation |
| gemini-2.5-flash-image | generate_image (optional) | Faster generation, higher volume |
| gemini-3-flash-preview | describe_image | Fast general descriptions |
| veo-3.1-generate-preview | generate_video | Veo 3.1 -- 4K video with native audio |
Gemini 3 notes: Temperature is forced to 1.0 on Gemini 3 models (Google's requirement -- lower values cause looping). Thought signatures are captured automatically for conversational image editing. Thinking level only applies to gemini_chat.
Apache-2.0
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/snapshot"
curl -s "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/contract"
curl -s "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
Rank
83
A Model Context Protocol (MCP) server for GitLab
Traction
No public download signal
Freshness
Updated 2d ago
Rank
80
A Model Context Protocol (MCP) server for GitLab
Traction
No public download signal
Freshness
Updated 2d ago
Rank
74
Expose OpenAPI definition endpoints as MCP tools using the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)
Traction
No public download signal
Freshness
Updated 2d ago
Rank
72
An actix_web backend for the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)
Traction
No public download signal
Freshness
Updated 2d ago
Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/snapshot",
"contractUrl": "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/contract",
"trustUrl": "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/trust"
},
"curlExamples": [
"curl -s \"https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/snapshot\"",
"curl -s \"https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/contract\"",
"curl -s \"https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"MCP"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_MCP",
"generatedAt": "2026-04-17T02:37:43.354Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"rows": [
{
"key": "MCP",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
},
{
"key": "mcp",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "model-context-protocol",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "gemini",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "google",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "ai",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "llm",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "chatbot",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "typescript",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "nodejs",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "claude",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "anthropic",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "generative-ai",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "machine-learning",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "artificial-intelligence",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "cli",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
}
],
"flattenedTokens": "protocol:MCP|unknown|profile capability:mcp|supported|profile capability:model-context-protocol|supported|profile capability:gemini|supported|profile capability:google|supported|profile capability:ai|supported|profile capability:llm|supported|profile capability:chatbot|supported|profile capability:typescript|supported|profile capability:nodejs|supported|profile capability:claude|supported|profile capability:anthropic|supported|profile capability:generative-ai|supported|profile capability:machine-learning|supported|profile capability:artificial-intelligence|supported|profile capability:cli|supported|profile"
}Facts JSON
[
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Houtini",
"href": "https://houtini.com/gemini-mcp/",
"sourceUrl": "https://houtini.com/gemini-mcp/",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-02-25T03:09:34.611Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "MCP",
"href": "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/contract",
"sourceUrl": "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-02-25T03:09:34.611Z",
"isPublic": true
},
{
"factKey": "traction",
"category": "adoption",
"label": "Adoption signal",
"value": "6 GitHub stars",
"href": "https://github.com/houtini-ai/gemini-mcp",
"sourceUrl": "https://github.com/houtini-ai/gemini-mcp",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-02-25T03:09:34.611Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/trust",
"sourceUrl": "https://xpersona.co/api/v1/agents/mcp-houtini-ai-gemini-mcp/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
]Change Events JSON
[]
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