Fovux MCP

From dataset to deployed ONNX, in one conversation.

Fovux is a professional-grade, open-source edge-AI computer vision workbench. It lets a computer vision practitioner run the full YOLO lifecycle through natural-language conversation with any MCP-compatible AI client: dataset curation, training, evaluation, error analysis, quantization, export, on-device benchmarking, and RTSP inference.

Brand: Fovux is the region of the retina responsible for sharp central vision. We help you see your models clearly.

Why Fovux?

	Fovux	Ultralytics Platform	GongRzhe/YOLO-MCP
Local-first, no account	✅	❌	✅
Full lifecycle (train→deploy)	✅	✅	❌
Error analysis	✅	Partial	❌
INT8 quantization report	✅	❌	❌
VS Code companion	✅	❌	❌
RTSP live inference	✅	❌	❌
Open source	✅	❌	✅

Status

Packaged releases are produced by GitHub Actions in oaslananka/fovux-kit. Install fovux-mcp from PyPI when you need the signed release artifact, or use the source workflow below for development.

Install From Source

git clone https://github.com/oaslananka/fovux-kit
cd fovux-kit/fovux-mcp
uv sync --frozen --extra dev

The Apache-2.0 core keeps YOLO engine dependencies optional. Install the yolo extra only when the Ultralytics backend and its separate AGPL/commercial terms are appropriate for your use case:

uv sync --frozen --extra dev --extra yolo

Quick start (5 minutes)

See docs/getting-started.md for the full tutorial.

# 1. Install from source
git clone https://github.com/oaslananka/fovux-kit
cd fovux-kit/fovux-mcp
uv sync --frozen --extra dev --extra yolo
uv run fovux-mcp doctor

# 2. Configure your MCP client (example: Cursor / Windsurf / VS Code)
# Add to your MCP client settings:
#   "fovux": { "command": "fovux-mcp" }

# 3. Start chatting
# "Inspect my dataset at ~/data/coco128"
# "Train yolov8n on it for 50 epochs"
# "Run error analysis on the best checkpoint"
# "Export to ONNX and benchmark on CPU"

For Studio or HTTP demos, start the local transport explicitly:

uv run fovux-mcp serve --http --tcp --metrics

MCP client configuration

Cursor / Windsurf

{
    "mcpServers": {
        "fovux": {
            "command": "fovux-mcp",
            "env": {
                "FOVUX_HOME": "~/.fovux"
            }
        }
    }
}

VS Code (with MCP extension)

{
    "mcp.servers": {
        "fovux": {
            "command": "fovux-mcp"
        }
    }
}

The tool set

Fovux MCP 1.5.0 currently exposes 47 local tools.

Tool	Purpose
`active_learning_queue_list`	List review queue entries from the SQLite database.
`active_learning_queue_rank`	Rank unlabeled images by uncertainty using a YOLO checkpoint and populate the review queue.
`active_learning_queue_submit`	Submit label corrections for a queue entry, copy the image to the dataset, and write the YOLO label file.
`active_learning_select`	Rank unlabeled images by model uncertainty for annotation prioritization.
`annotation_quality_check`	Inspect YOLO labels for common annotation mistakes before a bad dataset wastes training time.
`benchmark_latency`	Measure local inference latency and throughput for a model artifact.
`dataset_augment`	Create a local augmented YOLO dataset copy using deterministic transforms.
`dataset_convert`	Convert between supported YOLO and COCO dataset layouts.
`dataset_find_duplicates`	Perceptual hash duplicate detection for image datasets.
`dataset_inspect`	Comprehensive dataset statistics for YOLO or COCO exports.
`dataset_split`	Create reproducible train, val, and test splits.
`dataset_validate`	Deep integrity checks for YOLO datasets.
`demo_init`	Initialize a demo workspace for first-run onboarding.
`deployment_advise`	Analyze deployment readiness, preflight checks, parity, and benchmarks.
`distill_model`	Start a student-model training run with teacher-model distillation metadata.
`eval_compare`	Evaluate multiple checkpoints on the same dataset and rank the results.
`eval_error_analysis`	Inspect confusion patterns and worst examples beyond headline metrics.
`eval_per_class`	Return a sorted per-class view over evaluation output.
`eval_run`	Run a validation pass on a checkpoint.
`export_onnx`	Export a checkpoint to ONNX and optionally verify parity.
`export_reproducibility_bundle`	Export a reproducibility bundle zip file for a training run.
`export_tflite`	Export a checkpoint to TFLite, optionally with INT8 enabled.
`fovux_doctor`	Inspect the local Fovux environment before training, exporting, or opening Studio live views.
`generate_support_bundle`	Generate a redacted support bundle zip file containing system diagnostic information.
`get_policy_status`	Retrieve the current security policy status and allowed tools for the active environment.
`infer_batch`	Run inference over an image directory and persist the detections as a reusable manifest.
`infer_ensemble`	Run inference with multiple checkpoints and fuse the detections.
`infer_image`	Run structured inference on a single image.
`infer_rtsp`	Run live inference over an RTSP stream with reconnection logic.
`list_audit_events`	Retrieve audit event logs from the local database.
`model_compare_visual`	Generate visual comparison artifacts between two model checkpoints.
`model_list`	List tracked checkpoints and exported model artifacts.
`model_profile`	Profile a checkpoint so you can choose between accuracy, size, and compute cost before training or export.
`quantize_int8`	Produce an INT8 ONNX export using a calibration dataset.
`quantize_report`	Compare original and quantized checkpoints on the same evaluation set.
`run_archive`	Archive a completed training run to a compressed file.
`run_compare`	Generate a markdown and PNG summary across multiple training runs.
`run_delete`	Deletes a non-running training run from the SQLite registry and, by default, removes its run.
`run_tag`	Replaces the tag list for a training run. Tags are stored in the local SQLite registry and used by.
`set_policy_mode`	Set the local security policy mode to adjust permissions and confirmation prompts.
`sync_to_mlflow`	Sync a training run to a local or remote MLflow tracking server.
`train_adjust`	Adjust hyperparameters of a running training run.
`train_preflight`	Perform preflight checks and return a diagnostic training compatibility summary.
`train_resume`	Resume a stopped or failed run from its latest checkpoint.
`train_start`	Launch a non-blocking YOLO training subprocess.
`train_status`	Read the latest state and metrics for a tracked training run.
`train_stop`	Stop a running training subprocess and mark the run as stopped.

VS Code companion

Use Fovux Studio in this repo for visual run dashboards, dataset inspection, and an export wizard.

Documentation

Docs source lives in fovux-mcp/docs. Generated site/ output is a build artifact and is not committed.

uv run mkdocs build --strict

Contributing

See CONTRIBUTING.md. All contributions welcome.

License

Fovux core is Apache-2.0. The Ultralytics YOLO backend is optional and carries its own AGPL/commercial licensing boundary; install the yolo extra only when that backend is appropriate for your use case. See LICENSE, NOTICE, and docs/adr/0003-ultralytics-adapter-boundary.md.

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