Scout

Scout discovers MLX models on Hugging Face, bucketed by role for this host: general, coding, reasoning, vision, embedding, plus tool-use membership.

python3 scripts/mlx-agent discover --role coding --limit 3
python3 scripts/mlx-agent discover --new          # sort by Hub lastModified
python3 scripts/mlx-agent discover --fast         # skip enrichment
python3 scripts/mlx-agent discover --json

What makes the ranking honest

• Real sizing — actual quantized byte size from the HF tree API, not a name guess.
• Context-aware fit — architecture (layers, KV heads, head dim) from HF config gives each candidate an estimates.kv block with the maximum context your RAM supports. --context N tightens fits to weights + KV at that context.
• Reasoning detection — reads the chat template and tags; reasoning models stay out of fast/cheap roles.
• Quant dedup — -4bit / -8bit / -bf16 variants roll up to one logical model and the best quant that fits wins.
• License and gating — surfaced before any runtime fetch.
• Community bench — when the bundled aggregate has matching numbers for your chip, candidates carry a community_bench annotation.

Filters

--role, --limit, --memory-gb, --quantization, --license, --publisher, --runtime, --exclude-gated, --offline, --refresh, and --context compose freely. discover --wire <repo> --target <runtime> emits a ready config block for a chosen model.