Preview Watch
preview_watchDry-run a structured filter over recent papers to iterate on criteria like categories, novelty, or similarity before creating a watch. Returns match count and sample scores.
Instructions
Dry-run a structured filter over recent papers WITHOUT creating a watch — the tuning loop. Returns {window_days, needs_similarity, match_count, sample} so you can iterate (add a category, raise min_novelty, switch the collection relation) before saving with create_watch. Structured watches rank by 'rising' (forecasted breakout impact) by default, and tighten with min_impact_pct for an anti-noise watch that surfaces only the breakout papers in your niche. NOTE: for a similarity filter, match_count is capped at 200 (the cosine fetch window) and so saturates at 200 on broad/hot topics — tune by the sample scores and narrow with categories/min_novelty (or a higher similar floor) rather than relying on match_count alone. Read-only. Requires SF_API_KEY.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| criteria | Yes | The structured filter to test. | |
| recency_days | No | Window in days (default 7; the 'cites' relation uses 30). |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| window_days | No | ||
| needs_similarity | No | ||
| match_count | No | ||
| sample | No | A sample of matching papers. | |
| ok | No | ||
| message | No |