| description | Search the web for information using natural language queries. Returns summaries and sources from live web results. |
| name | web_search |
| source | built-in |
| tags | ["search","realtime","general"] |
| tools | ["perplexity_search"] |
When to use
Apply when the work item requires discovering current facts, statistics, regulations,
market data, or any information that needs to come from live web sources. Signals
include: "find", "research", "what is the current", "identify", "discover", "look
up". Use early in a task to establish baselines before browsing specific URLs or
inspecting datasets.
How to execute
- Decompose the work item into 2–4 specific, narrow questions — avoid vague queries.
- Write each query precisely: include domain, timeframe, and geography where relevant
(e.g. "EU AI Act compliance requirements 2024" not "AI regulations").
- Run each search and read the returned answer and citations carefully.
- If the answer cites authoritative URLs (government, academic, primary source),
follow them with web_browse to extract the full content.
- Cross-check key facts with a second query phrased differently.
- Record all citations — URL, title, and the specific claim each one supports.
Output contract
Produce a structured findings block in the proof field containing: the specific
questions asked and answers found, all citations (URL + title + claim supported),
confidence in each finding (high / medium / low) with rationale, and any
contradictions found between sources.
Quality bar
- Every factual claim must have at least one citation.
- Do not rely on a single source for high-stakes facts — verify with a second query.
- If results are sparse or contradictory, state that explicitly rather than guessing.
- Queries must be specific enough to return useful results — no single-word queries.
- Check timeframe — flag information older than 2 years as potentially stale.
Pairs well with
- web_browse
- pdf_extraction
- dataset_inspection