vision-mcp
Enables text-only AI coding agents to analyze images and videos via vision-capable models (Gemini, Grok, OpenRouter), returning text descriptions for reasoning.
README
vision-mcp
MCP server that gives text-only AI coding agents the ability to understand images and videos. Works with OpenCode, Claude Desktop, Claude Code, Cursor, VS Code, and any MCP-compatible client — including agents running on models without native vision.
agent (text-only) → vision-mcp → Gemini / Grok / OpenRouter → text description
Why
Most coding agents run on strong text models (DeepSeek, Claude, etc.) that can't see images. When you paste a screenshot, a UI mockup, or an error screenshot, they say "I can't view images". vision-mcp fixes that: it takes the file, sends it to a vision-capable model, and returns a plain-text description the agent can reason over.
Features
- 2 tools:
analyze_imageandanalyze_video - 3 backends, automatic fallback: Gemini → Grok (xAI) → OpenRouter — first success wins, rest cancelled
- Zero config file: keys come from environment variables only
- Image preprocessing: auto-rotate (EXIF), downscale to ≤1024px, JPEG re-encode — small payloads, fast responses
- Video support: ffmpeg keyframe extraction (up to 8 frames) with timestamps
- Typed & tested: TypeScript, 21 unit/integration tests, MCP protocol verified over real stdio
- No Python: pure Node.js (sharp for images, ffmpeg binary for video)
Requirements
- Node.js ≥ 18
- ffmpeg (for video analysis only; images work without it)
- At least one of these API keys:
| Backend | Env var | Get it |
|---|---|---|
| Gemini (default) | GEMINI_API_KEY |
https://aistudio.google.com/app/apikey |
| Grok (xAI) | XAI_API_KEY |
https://console.x.ai |
| OpenRouter | OPENROUTER_API_KEY |
https://openrouter.ai/keys |
Install
npx from GitHub (recommended — no npm publish needed)
# Run directly (no install)
npx -y github:pongsakornp/vision-mcp
npx clones the repo, installs deps, auto-builds via the
preparescript, and runs the server over stdio.
From source
git clone https://github.com/pongsakornp/vision-mcp.git
cd vision-mcp
npm install
npm run build
Usage
MCP client config
OpenCode (opencode.jsonc):
{
"mcp": {
"vision-mcp": {
"type": "local",
"command": ["npx", "-y", "github:pongsakornp/vision-mcp"],
"environment": {
"GEMINI_API_KEY": "AIza...",
"XAI_API_KEY": "xai-...",
"OPENROUTER_API_KEY": "sk-or-..."
},
"enabled": true
}
}
}
Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"vision-mcp": {
"command": "npx",
"args": ["-y", "github:pongsakornp/vision-mcp"],
"env": {
"GEMINI_API_KEY": "AIza..."
}
}
}
}
VS Code / Cursor (.vscode/mcp.json or workspace):
{
"servers": {
"vision-mcp": {
"type": "stdio",
"command": "npx",
"args": ["-y", "github:pongsakornp/vision-mcp"],
"environment": {
"GEMINI_API_KEY": "AIza..."
}
}
}
}
Keys live in the MCP config — no shell profile edits needed. Keep the config out of git if it contains keys.
CLI (for scripting / debugging)
# Spawn the server and talk JSON-RPC over stdio
GEMINI_API_KEY=AIza... node dist/index.js
Tools
analyze_image(path, prompt?)
Analyze an image file (png, jpg, jpeg, webp, bmp, gif, avif, tiff). Path can be absolute, relative, or ~/....
analyze_video(path, prompt?)
Analyze a video (mp4, webm, mov, avi, mkv, flv, wmv, m4v). Extracts up to 8 evenly-spaced keyframes with ffmpeg, then describes the sequence.
Configuration
| Env var | Default | Description |
|---|---|---|
GEMINI_API_KEY |
— | Gemini API key (tried first) |
XAI_API_KEY |
— | Grok/xAI API key (tried second) |
OPENROUTER_API_KEY |
— | OpenRouter key (tried last) |
VISION_GEMINI_MODEL |
gemini-flash-latest |
Gemini model override |
VISION_GROK_MODEL |
grok-4.5 |
Grok model override |
VISION_OPENROUTER_MODEL |
google/gemini-flash-latest |
OpenRouter model override |
VISION_TIMEOUT_MS |
20000 |
Per-backend request timeout |
Backend behavior
- Only backends with keys configured are launched.
- All configured backends run in parallel; the first success wins and the rest are cancelled.
- If all fail, the error reports every backend's failure for diagnosis.
Development
npm run build # compile TypeScript → dist/
npm test # run the 21 tests (vitest)
npm run typecheck # tsc --noEmit
Test coverage: config parsing, backend fallback logic, path resolution, image preprocessing (sharp), video keyframe extraction (real ffmpeg), and the full MCP stdio protocol.
License
MIT — see LICENSE.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.