Photo Vlm

photo-vlm-mcp is a local, Ollama-backed MCP server that gives coding assistants a portable photo-understanding toolset:

• analyze_photo - ask questions about a real-world photo.
• photo_ocr - extract text from labels, receipts, whiteboards, signs, and photographed pages.
• inspect_scene - return structured objects, scene context, visible text, quality, and uncertainty.
• compare_photos - compare before/after or near-duplicate photos.
• extract_metadata - read dimensions, orientation, EXIF fields, GPS, and optional SHA-256.
• health - check Ollama reachability and configured model availability.

It is designed for user-level registration with Claude Code, Codex, Antigravity, Cursor, Cline, Windsurf, Zed, and other MCP clients.

Requirements

• Python 3.11+
• Ollama running locally
• A vision-capable Ollama model

Recommended local models:

ollama pull qwen3-vl:8b
ollama pull minicpm-v

If qwen3-vl:8b is unavailable in your Ollama build, use qwen2.5vl:7b or another vision model from ollama.com/search?c=vision.

Install

Directly from GitHub:

python -m pip install "git+https://github.com/YehudRaanan/photo-vlm-mcp.git"

Or run without a persistent install using uvx:

uvx --from "git+https://github.com/YehudRaanan/photo-vlm-mcp.git" photo-vlm-mcp --version

For local development:

git clone https://github.com/YehudRaanan/photo-vlm-mcp.git
cd photo-vlm-mcp
python -m pip install -e .

With optional Tesseract OCR support:

python -m pip install -e ".[tesseract]"

Run

photo-vlm-mcp

The server uses MCP over stdio, so it normally runs under an MCP client rather than as a long-lived terminal command.

Helpful local checks:

photo-vlm-mcp --version
photo-vlm-mcp --print-config

Register With Claude Code

claude mcp add photo-vlm --scope user -- photo-vlm-mcp

With explicit model config:

claude mcp add photo-vlm --scope user `
  -e OLLAMA_URL=http://127.0.0.1:11434 `
  -e PHOTO_VLM_MODEL=qwen3-vl:8b `
  -e PHOTO_OCR_MODEL=minicpm-v `
  -- photo-vlm-mcp

Register With Codex / Antigravity / Other MCP Clients

Add this server to the client user-level MCP config:

{
  "mcpServers": {
    "photo-vlm": {
      "command": "photo-vlm-mcp",
      "env": {
        "OLLAMA_URL": "http://127.0.0.1:11434",
        "PHOTO_VLM_MODEL": "qwen3-vl:8b",
        "PHOTO_OCR_MODEL": "minicpm-v"
      }
    }
  }
}

If the console script is not on PATH, use:

{
  "command": "python",
  "args": ["-m", "photo_vlm_mcp"]
}

Configuration

Variable	Default	Meaning
OLLAMA_URL	http://127.0.0.1:11434	Ollama endpoint
PHOTO_VLM_MODEL	qwen3-vl:8b	Model for analysis, scene inspection, comparison
PHOTO_OCR_MODEL	minicpm-v	Model for VLM OCR
PHOTO_VLM_MAX_TOKENS	1024	Default generation limit
PHOTO_VLM_TIMEOUT	120	Ollama request timeout in seconds
PHOTO_VLM_KEEP_ALIVE	10m	Ollama keep-alive setting
PHOTO_VLM_MAX_DIM	2048	Downscale longest side before inference
PHOTO_VLM_MAX_IMAGE_MB	20	Reject larger images
PHOTO_VLM_FETCH_TIMEOUT	15	URL fetch timeout
PHOTO_VLM_ALLOW_PRIVATE_URLS	0	Allow private/loopback URLs
PHOTO_VLM_ALLOWED_ROOTS	unset	Optional path allow-list, separated by ; on Windows or : elsewhere

Legacy aliases VLM_MODEL, OCR_MODEL, and related VLM_* variables are also accepted.

Privacy

• extract_metadata omits GPS coordinates unless called with include_gps=true.
• OLLAMA_URL defaults to localhost. A remote value sends image bytes off this machine.
• With PHOTO_VLM_ALLOWED_ROOTS unset, any absolute path is readable; scope it in shared or agent-driven setups. See SECURITY.md.

Reliability Contract

Local VLM output is assistive visual evidence, not a deterministic classifier. For audits, measurements, compliance, or scientific reporting, use structured tables, rasters, metadata, or other deterministic sources as the source of truth.

VLM-backed tools return completion_status, warnings, and diagnostics in addition to their normal text or JSON payload. Treat completion_status: "possibly_incomplete" as a signal to retry, simplify the image/prompt, use a stronger model, or fall back to the deterministic source. diagnostics.done_reason exposes Ollama's finish reason when available; ollama_stopped_for_length, dangling_terminal_phrase, and missing_terminal_punctuation warnings indicate weak or suspicious output.

QA

python -m pytest
python -m ruff check src tests scripts
python -m black --check src tests scripts
python -m isort --check-only src tests scripts

The unit tests mock Ollama and validate image input handling, metadata extraction, prompt construction, and client behavior. Live model quality evaluation belongs in a separate environment with Ollama and selected models installed.

See also:

• Client setup
• Evaluation
• Release checklist

License

MIT