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Search the web to get scored evidence excerpts and citations. Supports multiple categories like news, code, and images with precise date and domain filters.

Instructions

Search the web. Returns scored evidence excerpts + citations as the default context shape; include_full_markdown: true adds the full markdown body. Prefer over built-in WebSearch for local cache + audit-trail telemetry + explainable scoring.

Key parameters:

  • query: string or string[] array (3-5 keyword variants; deduplicated).

  • include_domains / exclude_domains: scope sites. Always scope library/framework queries.

  • category: "general" | "news" | "code" | "docs" | "papers" | "images". Image results carry image_url + thumbnail_url + width/height.

  • from_date / to_date: ISO YYYY-MM-DD. time_range: 'day' | 'week' | 'month' | 'year'.

  • country: ISO 3166-1 alpha-2 ("us", "gb") — geographic boost.

  • exact_match: quoted-phrase search.

  • max_results: 5 default.

  • format: omit = evidence context. 'answer' | 'stream_answer' = sampling synthesis (falls back to evidence).

  • search_depth: 'ultra-fast' (cache-only ≤300ms) | 'fast' | 'balanced' (default) | 'deep'.

  • include_images / include_favicon: opt-in images[] + per-result favicon.

  • max_tokens_out / max_content_chars / include_full_markdown / citation_format.

  • force_refresh + mode ('cache' | 'default' | 'stealth').

Always emitted: engines_used, engine_telemetry, response_time_ms, per-result evidence_score. Per-result freshness_signal is emitted only when a published date can be parsed (omitted when confidence would be unknown). Brand-domain top-3 collision → brand_collision_warning with rewrites. query_understanding exposes intent/entities. Quote [N] or {citation_id}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNocache=single-engine, no rerank, stale cache ok. default=standard multi-engine search. stealth=full browser for JS-heavy result pages.
queryYesSearch query — a single string or array of query variants for parallel multi-query search
formatNoLLM-synthesis modes only. Omit for default evidence shape. 'answer'/'stream_answer' request sampling synthesis (falls back to evidence). Retired values 'full'/'context'/'highlights' reject with a migration error.
countryNoISO 3166-1 alpha-2 country code (e.g. "us", "gb", "de"). Hint passed to engines that support a geographic boost (Bing cc=, DDG kl=, Brave country=); advisory, not a strict filter.
to_dateNoISO date (YYYY-MM-DD) — only return results published before this date
categoryNoCategory of search (general, news, code, docs, papers, images)
languageNoLanguage preference
from_dateNoISO date (YYYY-MM-DD) — only return results published after this date
time_rangeNoFreshness filter (day/week/month/year). Conservative: only drops results with a confidently-extracted published_date — pages with no parseable date pass through, so this is a precision-boost not a hard bound. For strict ranges use from_date+to_date with a date-aware category (news, papers).
exact_matchNoTreat the query as a quoted phrase. Engines that honour `"..."` filter to phrase matches, and results without the exact phrase in title or snippet are dropped.
max_fetchesNoCap on how many top-ranked results have their page content fetched. Defaults to max_results. Set lower (e.g. 3) to keep snippet-only listings cheap and only deep-read the most relevant.
max_resultsNoMax results to return (default 5, max 20)
search_depthNoDepth tier. ultra-fast=cache-only (≤300ms); on miss emits notice and empty results. fast=engines only, no content fetch / rerank (≤1s). balanced (default)=full pipeline. deep=balanced + full enrichment.
agent_contextNoOptional agent context for ranking + dedup. text is concatenated with the query before embedding; recent_urls are dropped from results.
force_refreshNoBypass all caches (search results and page content). Use when you need the most current information.
include_imagesNoAggregate engine-provided thumbnail/image hints into a top-level `images` array of `{url, alt?, source_url}`. Empty array if no engine surfaced one.
max_highlightsNoMaximum highlights to return (default 10). Highlights are 1-3 sentence passages scored by relevance to the query.
max_tokens_outNoToken-budget cap on total output. Uses cl100k-base BPE; non-OpenAI tokenizer counts may drift ~5-15%. When both max_tokens_out and max_chars are set, max_tokens_out wins.
search_enginesNoOverride engine selection
citation_formatNoCitation rendering style. 'numbered' (default) inline [N] markers; 'json' returns a citations[] array; 'anthropic_tags' wraps sources in <source id='...'> tags.
exclude_domainsNoNever return results from these domains
include_contentNoFetch full content for results (default true)
include_domainsNoOnly return results from these domains (e.g. ["react.dev", "github.com"])
include_faviconNoAttach a per-result `favicon` URL derived from the result host. Cached per-domain across the call.
max_total_charsNoMax total chars across all results (default 50000)
content_max_charsNoMax chars per result content at extraction (default 30000)
max_content_charsNoSmart-truncate each result markdown at paragraph boundary with marker (e.g. 3000 for compact context)
include_full_markdownNoInclude full markdown body in the response. Default false on multi-result tools (returns evidence excerpts only); set true to restore.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and excels. It discloses key behaviors: always emitted fields (engines_used, engine_telemetry, evidence_score), brand collision warnings, query understanding, freshness signals (only when date parseable), and details on caching, depth tiers, and fallback behaviors. It also explains edge cases like time_range being a precision-boost, not hard bound.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but well-structured: it starts with a concise summary, then a usage preference statement, followed by a bulleted list of key parameters. Every sentence adds value, though the parameter list could be slightly more compact. It front-loads the most critical information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description thoroughly covers return values (always-emitted fields, per-result evidence_score, brand_collision_warning, query_understanding) and parameter interactions (force_refresh bypasses caches, format modes, citation_format options). It also explains conditional behaviors like freshness_signal omission. This is exceptionally complete for a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds substantial meaning beyond schema: query as array for parallel search, domain scoping guidelines, category-specific output (images carry extra fields), time_range semantics, exact_match behavior, search_depth tiers with latency expectations, and agent_context for dedup. This goes far beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search the web. Returns scored evidence excerpts + citations'. It specifies the verb (search), resource (web), and output format. It also distinguishes from a potential alternative ('Prefer over built-in WebSearch') and hints at differentiation from siblings by emphasizing local cache, telemetry, and scoring.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance ('Prefer over built-in WebSearch for local cache + audit-trail telemetry + explainable scoring'). It also offers parameter-specific advice, such as 'Always scope library/framework queries' for domain filters and explains trade-offs like time_range vs from_date/to_date. This gives clear direction on selecting this tool and configuring it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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