llm-vision-mcp
This server acts as a bridge, giving text-only agents vision by routing images to a multimodal model and returning text descriptions. The analyze_image tool accepts images from local files, URLs, base64 data URIs, clipboard, session-recent images, or raw bytes. It supports predefined tasks (describe, ocr, ui, layout, qa) or free-form prompts, with high/low detail control. Long outputs can be saved to a file, receiving a summary. Compatible with any MCP client (Claude Code, Codex, opencode, Kimi, Cursor, etc.) and configurable via environment variables for major vision providers (GPT-4o, Claude, Gemini, Qwen-VL) with secure API key handling. Optional SSRF guard blocks private URLs.
Integrates with Google Gemini vision models to analyze images, providing detailed descriptions, OCR, UI/layout understanding, and Q&A about visual content.
Integrates with OpenAI-compatible vision APIs to analyze images, providing detailed descriptions, OCR, UI/layout understanding, and Q&A about visual content.
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., "@llm-vision-mcpDescribe the UI screenshot in my clipboard."
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.
ποΈ mcp-vision-bridge
Give your text-only coding agent eyes.
DeepSeek V4 Flash writes great code β but it can't see the error dialog, the broken UI, or the screenshot you just pasted. This MCP server gives any text-only agent vision by routing images through a multimodal model of your choice.
Works with Claude Code Β· Codex Β· opencode Β· Kimi Β· PI Β· Cursor and any MCP client.
Why you need this
Your agent can't see. You paste a screenshot β "I can't see images." You transcribe the error by hand. With this, the agent calls one tool and gets a complete text description β verbatim text, layout, colors, anomalies β and can debug, fix, and explain.
Not a vision model. It's a bridge: it sends your image to a multimodal model you already pay for (mimo, Claude, Gemini, GPT-4o, Qwen-VLβ¦) and returns a detailed description. No images ever enter your agent's context.
Related MCP server: vision-mcp
π Install (pick your agent β that's the whole setup)
Claude Code (one command)
claude plugin marketplace add KuaaMU/agent-plugins
claude plugin install mcp-vision-bridgeThat's it β the plugin bundles the MCP server + vision skill + auto-loop hook. Claude Code will prompt you for your vision endpoint, API key, and model once.
Auto-updating: the MCP server self-syncs the bundled skill + hook into
~/.claude/on startup, so every restart pulls the latest version along with the npm package. SetVISION_NO_SYNC=1to disable auto-sync.
Prefer to manage it in cc-switch (see it + sync to Codex/opencode/Gemini)? Use the installer below instead.
Codex / Reasonix / opencode / Kimi / anything else (one command)
git clone https://github.com/KuaaMU/mcp-vision-bridge && cd mcp-vision-bridge
./install.sh # auto-detects your agent./install.sh claude | reasonix | codex | opencode | kimi if it doesn't auto-detect. You'll be asked for three values: endpoint, key, model.
Reasonix reads the same .mcp.json as Claude Code, so ./install.sh reasonix
(or a manual .mcp.json with the vision server) works β pasted images land in
.reasonix/attachments/ and image="recent" finds them.
Manual (no install script)
Add this as a stdio MCP server in your agent:
{
"command": "npx",
"args": ["-y", "mcp-vision-bridge"],
"env": {
"VISION_OPENAI_BASE_URL": "https://your-endpoint/v1",
"VISION_OPENAI_API_KEY": "sk-your-key",
"VISION_MODEL": "your-vision-model"
}
}Requires Node.js β₯ 18.
π― Use
After install, restart your agent, then:
Best way β drag the image file into the chat. Dragging an image file into
any agent (TUI or GUI) inserts its real path, which analyze_image accepts
directly β works identically in Claude Code, Cowork, Codex, opencode, PI, and
more. No clipboard, no paste quirks.
Drag an image file into the input box (or Ctrl+V in Claude Code / Cowork)
Say "ηηθΏδΈͺ" (or "analyze this", "what's the error?")
Your agent calls
analyze_imageβ the vision model describes it in detail
Paste 3 images? The hook reads your session transcript (lossless, multi-image).
image="recent" auto-finds pasted images across Claude Code CLI, Reasonix,
Cowork, and Codex β no clipboard needed. If a desktop GUI doesn't register a
paste (it can fail silently), just drag the file in β a path always works.
The one tool
Agent docs β README_AGENT.md (tool contract, source choice, error handling).
analyze_image(
image = "path | URL | clipboard | recent | session | data:URI", // single, or
["path","path",...] // several in one call
task = "describe | ocr | ui | layout | qa", // or use prompt:
prompt = "What error is on screen?",
detail = "high" | "low",
save_to = "optional file for long output"
)imageβ local path, http(s) URL,"clipboard","recent"(most recent pasted image in this session),"session"(every image pasted in this session, analyzed in one call), a base64 data URI, or an array of these to analyze multiple images at once (e.g. "compare these two").taskβ prompt presets for common jobs;ocrasks the vision model to extract text,uispecs a screen, etc. (There's no bundled OCR engine β the model itself does the reading.)promptβ free-form question (overridestask). Pass the user's actual question here β the vision model answers what you ask, so a specific question ("what error is shown?") beats a genericdescribe.
How pasted images are discovered
Pasting an image into a coding agent stores it somewhere. image="recent" /
"session" find it automatically β no clipboard, no manual paths:
Agent | Where pasted images land | Auto-found? |
Claude Code CLI/TUI |
| β |
Reasonix |
| β |
opencode |
| β |
Cowork (Claude-3p desktop) |
| β |
Codex |
| β |
Grok Build |
| β |
Windows clipboard reality: in Explorer, "copy file" (Ctrl+C) puts a file list on the clipboard β not image bytes. So pasting a local image into a CLI only works if you copy the image content (screenshot tool, browser "copy image"). Otherwise just paste the file path β
analyze_imagereads it directly.
Architecture
Three parts that close the loop for a text-only agent:
MCP tool (
analyze_image) β the capability. Sends pixels to your vision model, returns text.Skill (
skills/vision/) β the guidance. Tells the agent when and how to call it.Hook (
UserPromptSubmit) β the automation. Captures a pasted image from the session transcript and triggers the call for you.
Install them all with the plugin (Claude Code) or install.sh (any agent).
How it works
Pure text in, pure text out. The server never interprets the image β it fetches the bytes and lets your vision model do the seeing.
Configuration
All via environment variables (the MCP reads them from your agent's server config).
Variable | When | Example |
| OpenAI-compatible |
|
| OpenAI-compatible |
|
| always |
|
| non-openai |
|
| anthropic |
|
| gemini |
|
| optional |
|
| optional |
|
| optional |
|
Development
npm install
npm run build # tsc β dist/
npm test # vitest
npm run test:e2e # stdio pipeline against a mock providerLayout: src/ (server), skills/vision/ (skill), hooks/ (auto-loop hook),
install.sh (installer), examples/ (per-agent templates).
Release: bump the version in package.json, push, then
git tag vX.Y.Z && git push origin vX.Y.Z β GitHub Actions runs tests and
publishes to npm automatically.
Security
Keys live in env/config only β never in tool arguments.
Optional SSRF guard for URL sources.
Images go only to your configured vision provider.
License
DeepSeek writes the code. mcp-vision-bridge reads the screen.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Tools
Related MCP Servers
- AlicenseAqualityBmaintenanceGives vision-less LLMs the ability to recognize clipboard screenshots and images by proxying to an OpenAI-compatible vision model.1422MIT
- AlicenseAqualityBmaintenanceGives text-only coding agents the ability to 'see' images, videos, and screenshots by routing them to a vision model and returning structured text.8361MIT
- FlicenseAqualityBmaintenanceProvides image understanding capabilities to coding models without vision support by automatically invoking a vision model and returning text descriptions, enabling seamless context-aware coding with images.12
- FlicenseNot gradedqualityCmaintenanceEnables text-only language models to 'see' and describe images by calling multimodal APIs (OpenAI, Anthropic) for image analysis.
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