Use YOLOZU with an LLM or agent

Use YOLOZU when you need to validate existing vision predictions, evaluate them against labels, or inspect bounded local MCP capabilities.

Source docs version: 4.9.0. docs_version identifies the source checkout, not the installed package or latest PyPI release. Source URLs follow a mutable branch; check source_sha256 and provenance.json for this build. Inspect the installed CLI and MCP schemas before invoking tools.

When to use YOLOZU

Check a predictions JSON against the predictions interface contract before evaluation.
Evaluate existing detections against a labeled dataset with a fixed protocol and inspect the resulting evidence.
Integrate the typed Python API or discover local MCP tools and their input schemas before invoking them.

Limits and when not to use it

Do not infer model quality from a synthetic proof or a successful dry run. Real COCO metrics require the coco extra and labeled inputs.
Do not treat registered MCP tools as guaranteed execution. Runtime, dependency, data, and permission requirements still apply.
The packaged adaptive image service abstains until CNN license review, quality qualification, and activation are completed separately. It is not a ready-to-run hosted detector.
The local OpenAI plugin is not a public ChatGPT directory listing. Provider authentication, hosting, and attachment handoff are separate deployment work.

First successful evaluation

Start with the self-contained CPU evaluation tutorial. It creates its own inputs before strict validation and real COCOeval. Installation needs package network access; the subsequent toy workflow needs no model or dataset download.

Inspect before connecting an MCP client

After the isolated installation in the 30-minute path, install the optional MCP extra in that same environment. These commands exit after writing local discovery and sample files; they do not call a provider API or download a model.

python -m pip install "yolozu[mcp]"
mkdir -p reports
yolozu-mcp --help
yolozu-mcp --print-tools --guaranteed --ids-only > reports/mcp_tool_ids.json
yolozu-mcp --sample-generate-config > reports/ai_generate_config.json
yolozu-mcp --sample-review-config reports/ai_generate_config.json > reports/ai_review_config.json

Inspect the returned IDs and review result. The guaranteed-tool filter narrows this discovery output, not the server's runtime surface or filesystem permissions.

Workflow source

Connect an authorized local client

Configure the client to launch one of these alternatives with the installed environment's executable and an explicitly chosen workspace. These are long-running stdio servers, not one-shot validation commands. Stop the server when the session ends.

yolozu-mcp --transport stdio
yolozu-mcp --transport stdio --surface image-service

Use the full surface only when the broader tools are intended. The image-service alternative exposes only capabilities, asset upload, submission, status, and cancellation. Ask for capabilities first; never reinterpret abstention as successful inference.

Workflow source

Starter prompts and expected boundaries

Prompt: Validate my existing detection predictions strictly with YOLOZU; report errors without repairing the input.

Expected: Confirm the user-selected local input, inspect the installed schema, then use strict validation. Do not invent a path or enable repair.

Prompt: Show me a local YOLOZU evaluation example without downloading model weights.

Expected: Use the self-contained 30-minute path and its generated toy inputs; label the proof as synthetic, not model-quality evidence.

Prompt: Show the YOLOZU image service capabilities without running inference.

Expected: Use image_service_capabilities on the image-service connection. Do not upload an image or submit execution.

Prompt: Use YOLOZU to select the best CNN and guarantee its accuracy on my images.

Expected: Explain that this guarantee is unavailable and that the default adaptive service abstains. Do not activate a model or fabricate a quality result.