vision-mcp
Click on "Deploy 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., "@vision-mcp analyze this image: ./screenshot.png"
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.
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 descriptionWhy
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.
Related MCP server: vision-mcp
Features
2 tools:
analyze_imageandanalyze_video3 backends, automatic fallback: Gemini → Grok (xAI) → OpenRouter — tried in priority order, first success wins
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) |
| |
Grok (xAI) |
| |
OpenRouter |
|
Install
npx from GitHub (recommended — no npm publish needed)
# Run directly (no install)
npx -y github:pongsakornp/vision-mcpnpx 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
pnpm install
pnpm run buildUsage
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.jsTools
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 (tried first) |
| — | Grok/xAI API key (tried second) |
| — | OpenRouter key (tried last) |
|
| Gemini model override |
|
| Grok model override |
|
| OpenRouter model override |
|
| Per-backend request timeout |
Backend behavior
Only backends with keys configured are tried.
Backends are tried sequentially in priority order (Gemini → Grok → OpenRouter); the first success wins and later backends are never called.
If all fail, the error reports every backend's failure for diagnosis.
Development
pnpm run build # compile TypeScript → dist/
pnpm test # run the 21 tests (vitest)
pnpm run typecheck # tsc --noEmitTest 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.
Available Tools
2 toolsanalyze_imageAnalyze imageA
Analyze an image file (png, jpg, webp, bmp, gif, avif) and return a text description. Use for screenshots, diagrams, UI mockups, photos. Provide an absolute or relative path.
Inputs: path (required), prompt.
analyze_videoAnalyze videoA
Analyze a video file (mp4, webm, mov, avi, mkv) by extracting keyframes and describing the content. Requires ffmpeg installed.
Inputs: path (required), prompt.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools are clearly separated by media type: images versus videos. An agent can easily select the right tool based on the file extension or input format, with no meaningful overlap.
Both tools follow the exact same verb_noun snake_case pattern: analyze_image and analyze_video. The naming is clean, predictable, and immediately signals what each tool does.
Two tools is minimal but reasonable for a vision server focused on content description of static and moving media. Each tool earns its place, though the set feels slightly thin compared to broader vision-capable servers.
The core workflow of analyzing visual media is covered: images and videos both have a dedicated tool. Minor gaps exist, such as no URL-based input and no specialized vision capabilities like OCR or object detection, but these are not clearly implied by the server's stated scope.
Maintenance
Related MCP Connectors
OCR, transcription, file extraction, and image generation for AI agents via MCP.
Generate images, GIFs, videos, and PDFs from HTML, URLs, or templates — from your AI agent.
Ask other AI models and generate images, speech, music and video from your coding agent.
AI image + video generation for agents: --flag prompt DSL, async generate/poll, x402 pay-per-use.
Related MCP Servers
- AlicenseAqualityDmaintenanceEnables AI agents to analyze images, extract text, compare images, and analyze video through any OpenAI-compatible vision model.4375 npm20MIT
- AlicenseAqualityCmaintenanceGives text-only coding agents the ability to 'see' images, videos, and screenshots by routing them to a vision model and returning structured text.833 npm2MIT
- AlicenseAqualityCmaintenanceEnables text-only coding agents to analyze local images using a dedicated vision provider, returning markdown and structured JSON evidence for screenshots, diagrams, UI mockups, and error captures.11127 npm11MIT
- AlicenseAqualityCmaintenanceGives text-only LLM coding agents vision by routing images to a multimodal model and returning detailed textual descriptions. Supports local files, URLs, clipboard, base64, raw bytes, and multiple providers like OpenAI, Anthropic, and Gemini.174 npm13MIT