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Run a multi-engine web search to get a ranked, deduplicated list of links with snippets. Use for discovery queries and to obtain URLs for further fetching.

Instructions

Run a multi-engine web search and return a ranked, deduplicated link list.

Best for:
- Discovery queries ("what is X", "find me X", "who is X").
- Getting a list of URLs you can hand to `fetch` / `fetch_batch` next.
- Topics likely to be after your knowledge cutoff (use `freshness="week"`).
- Filtering to specific domains (`include_domains=["python.org"]`) or
  content types (`category="paper"|"pdf"|"github"|"news"|"forum"|"blog"`).

Not recommended for:
- You already know the URL -> use `fetch` instead.
- You want both links AND their full text in one call -> use `research`.
- You want to query pages already in the local cache -> use `cache_search`.
- Reading PDFs/DOCX from a known URL -> use `read_doc`.

Returns:
- markdown (default): numbered list of `n. title`, `<url>`, snippet — ~40%
  fewer tokens than json.
- json: dict with `results` (list of {title,url,snippet,engines,score}),
  `engines`, `cached`, optional `errors` map, optional `hint` string.

Common mistakes:
- Passing a URL as `query` — that's `fetch`'s job.
- Cranking `max_results` to 50 hoping for better recall; engines cap around
  10-20 each, anything beyond is duplicate noise.
- Adding `engines=["startpage","brave","bing","baidu"]` by default — those
  need browser rendering or captcha-friendly conditions; stick with the
  defaults unless they returned 0.
- Using `category="news"` for breaking news without also setting
  `freshness="day"` — the index lag is days, not minutes.

Args:
    query: Natural-language query (the same string a human would type).
    engines: Subset of `engines()`. None = duckduckgo+mojeek+googlenews.
        (startpage is opt-in and browser-rendered.)
    max_results: Merged result count after dedup. 5-20 is the useful range.
    use_cache: Reuse the last result for this exact (query, engines,
        max_results) within the cache TTL. False forces a re-fetch.
    max_age_hours: Treat cached results older than this as a read miss; a
        fresh result is ALWAYS written back to the cache regardless of this
        value, so caching is never disabled. Use 0 to force-refresh while
        keeping cache writes; None = use server default TTL (7 days).
    freshness: "day"|"week"|"month"|"year" — restrict to recent results.
    include_domains: List of domains to restrict to (e.g. ["python.org"]).
    exclude_domains: List of domains to exclude.
    category: "news"|"pdf"|"github"|"paper"|"forum"|"blog" — content-type
        shortcut. "paper" => arxiv/acm/springer/ieee/etc; "forum" =>
        reddit/HN/stackexchange; "github" => github.com only.
    include_text: Substring required in title or snippet (case-insensitive).
    exclude_text: Substring forbidden in title or snippet.
    format: "markdown" (default) or "json".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
enginesNo
max_resultsNo
use_cacheNo
max_age_hoursNo
freshnessNo
include_domainsNo
exclude_domainsNo
categoryNo
include_textNo
exclude_textNo
formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds detailed behavior: caching strategy, engine defaults, format differences, and limitations (e.g., engines cap around 10-20). No contradictions.

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 long but well-structured with sections. While every sentence adds value, some redundancy (e.g., caching repeated in max_age_hours and use_cache) could be trimmed. Still, structure aids readability.

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?

For a complex tool with 12 parameters and no schema descriptions, the description is remarkably complete. It covers caching, domain filtering, category mappings, output formats, common errors, and usage contexts.

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 description coverage is 0%, but the description explains every parameter in detail: defaults, effects, and constraints (e.g., max_results range, freshness values, category mappings). This fully compensates for the schema's lack of 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 'Run a multi-engine web search and return a ranked, deduplicated link list.' It distinguishes from siblings by listing specific alternatives (fetch, research, cache_search, read_doc) and contexts (discovery queries, known URLs, etc.).

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?

Explicit 'Best for' and 'Not recommended for' sections with clear scenarios and alternative tool names. Also includes 'Common mistakes' that further guide correct usage.

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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