Local Eyes 👁️

Private eyes for text-only AI agents. Local Eyes is an MCP server that lets any agent or model see images, using Gemma 4 running on your own machine (Ollama) or through the Gemini API.

Frontier models can already see. But many of the open models people run locally for coding and agents are text-only (gpt-oss, DeepSeek, Qwen-Coder), and private or offline setups can't send screenshots to a cloud model. Local Eyes plugs Gemma 4's vision into any MCP client as three tools that return strict JSON. A text-only agent can then read a broken UI, a stack-trace screenshot or a photo of a receipt, without the image ever leaving the laptop.

 text-only agent ──MCP──▶ Local Eyes ──▶ Gemma 4 (Ollama, on this machine)
 (gpt-oss, Qwen,          read_ui           └─ or gemma-4-31b-it on the Gemini API
  Claude, Cursor…)        extract_text
                          describe_image  ◀── validated JSON (Pydantic), 1 auto-retry

Tools

Every result is validated against a Pydantic schema. If the model returns invalid JSON, Local Eyes retries once and sends back the exact validation error. On Ollama, the schema is also enforced while the model writes its answer (structured outputs). Each result carries _meta with the backend, model, latency, attempts and whether it came from the cache.

Related MCP server: vision-mcp

Setup

Requires Python 3.10+ and uv.

git clone https://github.com/Arcanum703/local-eyes && cd local-eyes
uv sync
cp .env.example .env        # pick a backend; add GEMINI_API_KEY if using gemini

Private mode (Ollama, works offline):

ollama pull gemma4:e4b

Cloud mode (Gemini API): set LOCAL_EYES_BACKEND=gemini and GEMINI_API_KEY=... (from AI Studio) in .env. The default model is gemma-4-31b-it.

Try it without an MCP client:

uv run local-eyes-cli read_ui demo/broken_form.png

Connect to any MCP client

Claude Code (3 lines):

claude mcp add local-eyes -- uv --directory /path/to/local-eyes run local-eyes

Claude Desktop / Cursor / any MCP client (mcpServers entry):

"local-eyes": {
  "command": "uv",
  "args": ["--directory", "/path/to/local-eyes", "run", "local-eyes"]
}

Add "--backend", "gemini" or "--cache" to the args to change behaviour, or set LOCAL_EYES_BACKEND in .env.

Replay cache (--cache)

With --cache, every validated response is saved to cache/<hash>.json. The key is the image bytes + tool + question, so the backend doesn't matter. The next identical call is answered from disk instantly, with no model and no network. This keeps a live demo working through rate limits or dead conference Wi-Fi. Cached results show "_meta": {"cached": true}. Answers for the demo images ship in cache/.

Demo: a text-only agent fixes a broken form

scripts/agent_demo.py is a ~80-line agent harness. Its "brain" is any Ollama model with tool calling, and it never receives image bytes, only Local Eyes' JSON:

AGENT_MODEL=gpt-oss:20b uv run python scripts/agent_demo.py demo/broken_form.png "Why can't the user sign up? Propose a fix." --cache

The agent calls read_ui, learns about the invalid email, the mismatched passwords, the 422 date_of_birth expected YYYY-MM-DD banner and the submit button hanging off the card, then proposes the fix. With Ollama it all works with Wi-Fi off.

Other demo images: demo/stacktrace.png (try extract_text on it).

scripts/mcp_smoke.py <tool> <image> [--cache] [--backend gemini] exercises the server exactly like an MCP client does.

Configuration

Only files that decode as images are ever sent to a model (25 MB / 50 MP caps).

License

MIT. Built at MLH Hacktoberfest Hack Day Budapest, 31 Oct 2026.