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👁️ mcp-vision-bridge

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Give your text-only coding agent eyes.

DeepSeek V4 Flash writes great code — 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 · Codex · opencode · Kimi · PI · Cursor and any MCP client.

English · 中文


Why you need this

Your agent can't see. You paste a screenshot → "I can't see images." You transcribe the error by hand. With this, the agent calls one tool and gets a complete text description — verbatim text, layout, colors, anomalies — 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-VL…) and returns a detailed description. No images ever enter your agent's context.


Related MCP server: vision-mcp

🚀 Install (pick your agent — that's the whole setup)

Claude Code (one command)

claude plugin marketplace add KuaaMU/agent-plugins
claude plugin install mcp-vision-bridge

That's it — 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. Set VISION_NO_SYNC=1 to 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 — 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 ≥ 18.


🎯 Use

After install, restart your agent, then:

Best way — 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 — works identically in Claude Code, Cowork, Codex, opencode, PI, and more. No clipboard, no paste quirks.

  1. Drag an image file into the input box (or Ctrl+V in Claude Code / Cowork)

  2. Say "看看这个" (or "analyze this", "what's the error?")

  3. Your agent calls analyze_image → 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 — no clipboard needed. If a desktop GUI doesn't register a paste (it can fail silently), just drag the file in — a path always works.

The one tool

Agent docs → 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"
)
  • image — 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").

  • task — prompt presets for common jobs; ocr asks the vision model to extract text, ui specs a screen, etc. (There's no bundled OCR engine — the model itself does the reading.)

  • prompt — free-form question (overrides task). Pass the user's actual question here — the vision model answers what you ask, so a specific question ("what error is shown?") beats a generic describe.

How pasted images are discovered

Pasting an image into a coding agent stores it somewhere. image="recent" / "session" find it automatically — no clipboard, no manual paths:

Agent

Where pasted images land

Auto-found?

Claude Code CLI/TUI

~/.claude/image-cache/<uuid>/N.png (paste with Alt+V)

Reasonix

~/.reasonix/sessions/ + project .reasonix/attachments/

opencode

~/.local/share/opencode/opencode.db (SQLite part table, Node ≥ 22.5)

Cowork (Claude-3p desktop)

%LOCALAPPDATA%\Claude-3p\...\uploads\*_image.png

Codex

~/.codex/attachments/<session>/image-*.png

Grok Build

~/.grok/sessions/*/*/images/

Windows clipboard reality: in Explorer, "copy file" (Ctrl+C) puts a file list on the clipboard — 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 — analyze_image reads it directly.


Architecture

Three parts that close the loop for a text-only agent:

  • MCP tool (analyze_image) — the capability. Sends pixels to your vision model, returns text.

  • Skill (skills/vision/) — the guidance. Tells the agent when and how to call it.

  • Hook (UserPromptSubmit) — 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 — 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

VISION_OPENAI_BASE_URL

OpenAI-compatible

https://opencode.ai/zen/go/v1

VISION_OPENAI_API_KEY

OpenAI-compatible

sk-...

VISION_MODEL

always

mimo-v2.5, gpt-4o, qwen-vl-max

VISION_PROVIDER

non-openai

anthropic | gemini

VISION_ANTHROPIC_API_KEY

anthropic

sk-ant-...

VISION_GEMINI_API_KEY

gemini

AIza...

VISION_MAX_TOKENS

optional

4096 per image — multi-image multiplies it ×N (each image keeps its own budget, capped 32000) so detailed descriptions aren't truncated

VISION_TIMEOUT_MS

optional

30000

VISION_BLOCK_PRIVATE_URLS

optional

true to block localhost fetches


Development

npm install
npm run build          # tsc → dist/
npm test               # vitest
npm run test:e2e       # stdio pipeline against a mock provider

Layout: 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 — GitHub Actions runs tests and publishes to npm automatically.


Security

  • Keys live in env/config only — never in tool arguments.

  • Optional SSRF guard for URL sources.

  • Images go only to your configured vision provider.

License

MIT


DeepSeek writes the code. mcp-vision-bridge reads the screen.

GitHub · npm · Plugins · ⭐ Star it if it's useful

Available Tools

1 tool
analyze_imageA

Analyze an image using a multimodal model and return a detailed text description. The vision model sees the image; the calling agent is text-only and cannot.

Sources for image (pick one):

  • "path": absolute or relative path to a local image file (PNG/JPEG/WEBP/GIF)

  • URL: http(s) URL to an image on the web or a local server

  • "data:...": base64 data URI, e.g. data:image/png;base64,

  • "clipboard": read the image currently copied to the system clipboard

  • "recent": auto-find the most recently pasted image (scans Codex attachments, Grok session images, Claude transcripts)

  • "session": auto-find images pasted in this session

  • "raw": the string itself is the literal raw image bytes

Pick task for common jobs (describe | ocr | ui | layout | qa) or pass your own prompt. detail defaults to "high" for maximum completeness. Use save_to to write a long description to a file and get back only a path + summary.

ParametersJSON Schema
NameRequiredDescriptionDefault
taskNoCommon analysis task. Ignored when `prompt` is provided.
imageYesImage source: file path, URL, data: URI, 'clipboard', 'recent', 'session', or 'raw'.
detailNoDesired detail level. Defaults to 'high'.
promptNoFree-form question or instruction about the image. Overrides `task`.
save_toNoOptional file path (.txt/.md) to write the full description to.

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations to lean on, the description carries the full burden and does well: it discloses the use of a multimodal model, the text-only nature of the agent, the return of a detailed description, and the effect of 'save_to' (writes to file, returns path + summary). It describes defaults ('detail' defaults to 'high') and source resolution behaviors. It does not cover error cases or permissions, but the disclosed behavior is solid.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with bullets, front-loads the core purpose in the first sentence, and every subsequent line adds distinct information (sources, tasks, defaults, file output). It is appropriately sized for a tool with 5 parameters and no output schema, with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, no output schema, no annotations), the description is remarkably complete. It covers all input source types, task modes, prompt override, detail level, and save_to behavior. The only minor omission is exact return formatting, but the description explicitly says 'detailed text description' and 'path + summary' for save_to, which is sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds substantial meaning beyond the schema. It explains each image source format with examples, declares the precedence between 'prompt' and 'task', clarifies the 'detail' default, and describes the 'save_to' output behavior. This is exactly the kind of added value that helps an agent pick and fill parameters correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Analyze an image using a multimodal model and return a detailed text description.' It also explains the motivation (the vision model sees the image, the calling agent is text-only), making the purpose unambiguous and strongly differentiated from any generic tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly explains when to use the tool: whenever a text-only agent needs image understanding. It gives concrete guidance on selecting image sources and tasks, and notes that 'task' is ignored when 'prompt' is provided. No explicit when-not-to-use or alternatives are mentioned, but there are no sibling tools to differentiate from.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.8
    • Changedanalyze_image1 field changed
      • changedInput schema / properties / image / description
        Previous value: -"Image source: file path, http(s) URL, data: URI, 'clipboard', or 'raw'."New value: +"Image source: file path, URL, data: URI, 'clipboard', 'recent', 'session', or 'raw'."
  2. 1 tool updatev0.1.0
    • First observedanalyze_image

TDQS

A4.7/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap. The single tool has a clear and distinct purpose: analyzing images.

Naming Consistency5/5

The tool name 'analyze_image' follows a clean verb_noun pattern. Since there is only one tool, naming consistency is perfect.

Tool Count4/5

The server has exactly one tool, which feels slightly thin but is reasonable for a highly focused vision analysis server. The tool is comprehensive, handling many tasks through parameters, so the count is not inadequate.

Completeness5/5

The tool covers a wide range of vision tasks including describe, OCR, UI, layout, and QA, with multiple input sources and output options. There are no obvious gaps for the stated purpose of enabling text-only agents to analyze images.

Maintenance

ActivitySlowing
ResponsivenessNo issues

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