Vision MCP Server

Vision MCP Server

A Model Context Protocol server that provides text-only LLMs with visual capabilities through Z.AI-compatible vision tools, routing requests across multiple providers with automatic fallback.

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Vision MCP Server

A Vision MCP Server that gives text-only LLMs / coding agents visual capabilities over the Model Context Protocol.

The server exposes Z.AI-compatible vision tools (analyze_image, extract_text_from_screenshot, ui_diff_check, ...) and routes every request through a configurable chain of visual providers (AGY, Codex, Gemini API, OpenCode) with automatic fallback. Provider, model, API key and fallback order are server policy — the LLM never sees or chooses them.

Why a Vision MCP?

Most coding agents / text-only LLMs cannot "see" screenshots, error traces, UI mockups or diagrams. This server acts as their eyes:

Text-only LLM
      │  MCP
      ▼
Vision MCP Server
   ├── Z.AI-compatible Vision Tools
   ├── Specialized Prompt Layer
   ├── Media / Workspace Layer
   ├── Structured JSON Layer
   └── Provider Router (AGY → Codex → Gemini → OpenCode)

Features

  • 8 Z.AI-compatible vision tools + 2 aliases.
  • Provider-neutral tools: no provider/model/api_key/workdir/timeout in tool schemas — those are server config.
  • Provider Router with configurable order and fallback policy.
  • Unified structured JSON output (observations, texts, elements, bbox).
  • Local paths and HTTP(S) URLs; file:// rejected.
  • Per-task isolated workspaces; automatic cleanup.
  • CLI-native images (Codex -i, OpenCode --file) and AGY workspace staging with vision-capability detection.
  • Gemini API via google-genai.
  • vision-mcp doctor environment inspection + --probe vision smoke test.
  • No ACP / no transport abstraction in v1.

Requirements

  • Python 3.11+
  • macOS / Linux / Windows

Installation

pip install -e .

Or with a virtualenv + uv:

uv venv --python 3.11 .venv
source .venv/bin/activate
uv pip install -e ".[dev]"   # dev = pytest, pytest-asyncio, Pillow (for doctor --probe)

Quick start

# Copy the example config and edit it to suit your machine.
cp config.example.yaml ~/.config/vision-mcp/config.yaml

# Run as an MCP stdio server
vision-mcp --config ~/.config/vision-mcp/config.yaml

# Or
python -m vision_mcp --config ~/.config/vision-mcp/config.yaml

MCP client configuration

{
  "mcpServers": {
    "vision": {
      "command": "vision-mcp",
      "args": ["--config", "/Users/me/.config/vision-mcp/config.yaml"],
      "env": { "GEMINI_API_KEY": "..." }
    }
  }
}

Configuration

Configuration priority: CLI argument > environment variable > config file > built-in default.

version: 1

providers:
  order: [agy, codex, gemini, opencode]
  agy:
    enabled: true
    command: agy
    model: null
  codex:
    enabled: true
    command: codex
    model: null
  gemini:
    enabled: true
    model: null
    api_key_env: GEMINI_API_KEY
  opencode:
    enabled: true
    command: opencode
    model: null

runtime:
  workdir: null          # null => temporary dir per task
  timeout: 120
  max_concurrency: 2

fallback:
  enabled: true
  on:
    - command_not_found
    - not_authenticated
    - api_key_missing
    - quota_exhausted
    - unsupported_media
    - timeout
    - temporary_failure

media:
  max_image_mb: 20
  max_video_mb: 8
  download_timeout: 30
  max_download_mb: 32

logging:
  level: INFO

Provider order

The router tries providers in the configured order and falls back on failure. Default: agy → codex → gemini → opencode.

Not fallback-eligible by default: invalid_input, invalid_model, config_error. The fallback.on list is the final authority.

Provider model

Each provider's model is set in config and used automatically on fallback — there is no cross-provider model namespace to manage.

providers:
  agy:      { model: gemini-xxx }
  codex:    { model: gpt-xxx }
  gemini:   { model: gemini-xxx }
  opencode: { model: google/gemini-xxx }

Set a model to null to let the provider use its own default.

Gemini API key

API keys are never tool arguments. Resolution order:

VISION_MCP_GEMINI_API_KEY
    > config.providers.gemini.api_key_env (the env var it names)
    > GEMINI_API_KEY

For compatibility a plain api_key may be placed in the config file; it is stored as a SecretStr, never printed, never dumped, never returned in MCP responses, and never included in exceptions. Prefer the environment variable.

Environment variables

VISION_MCP_CONFIG                 config file path
VISION_MCP_WORKDIR                runtime workdir
VISION_MCP_TIMEOUT                runtime timeout (s)
VISION_MCP_MAX_CONCURRENCY        max concurrency
VISION_MCP_AGY_COMMAND            agy executable
VISION_MCP_AGY_MODEL              agy model
VISION_MCP_CODEX_COMMAND          codex executable
VISION_MCP_CODEX_MODEL            codex model
VISION_MCP_GEMINI_MODEL           gemini model
VISION_MCP_GEMINI_API_KEY         gemini API key
GEMINI_API_KEY                    gemini API key (fallback)
VISION_MCP_OPENCODE_COMMAND       opencode executable
VISION_MCP_OPENCODE_MODEL         opencode model
VISION_MCP_LOG_LEVEL              ERROR | WARNING | INFO | DEBUG

Workdir

With runtime.workdir: null (default), every task gets a brand-new temporary directory that is cleaned up on completion. With a project workdir configured, task media is staged under <workdir>/.vision-mcp/<uuid>/ and removed after. User files are never modified or deleted.

Media limits

Images: png/jpg/jpeg/webp/gif/bmp/tiff (default max_image_mb: 20). Videos: mp4/mov/m4v (Z.AI-compatible default max_video_mb: 8). Remote downloads are bounded by timeout, size and a redirect limit, and validated by MIME type.

Tools

Tool Purpose
ui_to_artifact Convert a UI screenshot into code / prompt / spec / description
extract_text_from_screenshot Verbatim OCR of code / terminal / config / docs
diagnose_error_screenshot Diagnose error / stack trace / root cause / fix
understand_technical_diagram Understand architecture / flowchart / UML / ER diagrams
analyze_data_visualization Analyze charts: trends, anomalies, comparisons
ui_diff_check Compare EXPECTED vs ACTUAL UI for visual regression
analyze_image General visual analysis
analyze_video Video analysis (mp4/mov/m4v)

Aliases share the same implementations: image_analysis → analyze_image, video_analysis → analyze_video.

Structured output

Every provider's result is normalized into one schema and wrapped in a standard envelope:

{
  "provider": "codex",
  "model": "gpt-xxx",
  "result": {
    "summary": "Short visual summary",
    "answer": "Direct answer",
    "observations": [{ "type": "text", "text": "...", "confidence": 0.95 }],
    "texts": [{ "text": "visible text", "bbox": [100, 100, 900, 200], "confidence": 0.98 }],
    "elements": [{ "label": "Build button", "type": "ui_element", "bbox": [700, 20, 820, 70], "confidence": 0.93 }],
    "warnings": []
  },
  "meta": {
    "duration_ms": 4812,
    "fallbacks": [],
    "usage": { "input_tokens": null, "output_tokens": null }
  }
}

bbox is normalized to 0..1000 as [x_min, y_min, x_max, y_max]. When a value can't be determined, providers do not guess — they omit it and add a warning.

Doctor

vision-mcp doctor
vision-mcp doctor --probe   # also runs a real AGY vision smoke test (needs Pillow)
vision-mcp --version

doctor never prints API key contents.

Provider detection

  • AGY: agy -p "<prompt>" --output-format json. Images are staged into the workspace and read natively via --add-dir (relative path reference). AGY's vision capability is probed once per process; if headless AGY auto-denies a tool permission it needs to read the image, that request raises unsupported_media and falls back, while later requests still get a real AGY try.
  • Codex: codex exec -i <img> ... --output-schema ... -s read-only. Images passed natively; read-only sandbox enforced.
  • Gemini: google-genai, structured JSON, multi-image, configured model.
  • OpenCode: opencode run --format json, images via --file, JSON event stream parsed for the final assistant result.

AGY non-determinism: AGY reads workspace images natively via --add-dir. However, as of AGY CLI 1.1.x, headless mode is non-deterministic — a run may intermittently need a read_file/command tool permission that headless mode auto-denies. When that happens the server detects it and transparently falls back to the next provider. vision-mcp doctor --probe reports the capability without failing the server.

Security

The server only LOOK / READ / UNDERSTAND / COMPARE / ANALYZE — it never EDIT / BUILD / EXECUTE / MODIFY. Codex runs in a read-only sandbox; AGY and OpenCode are never launched with dangerous auto-approval. API keys are redacted from all logs and responses.

Development

python -m pytest

Tests cover config, router, workspace, media, all four providers (subprocess / genai mocked), Z.AI tool-schema compatibility, and an MCP tools/list + tools/call smoke test.

Troubleshooting

  • agy falls back to codex for images — AGY reads workspace images via --add-dir, but headless mode is non-deterministic and may intermittently auto-deny a tool permission. That is expected; the server falls back transparently. Run vision-mcp doctor --probe to exercise AGY directly.
  • Nothing responds — no provider is enabled. Enable providers in config.
  • Gemini not used — an API key is required; see "Gemini API key".
  • Codex blocks on stdin — the server always closes stdin for CLI providers.
  • stdout corruption — all logs go to stderr; stdout is reserved for MCP.

License

MIT

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