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Query multiple web engines simultaneously and return a consolidated, deduplicated link list with filters for domain, freshness, and content type.

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. If the defaults DO return 0, the keyless
  HTTP extras `engines=["google"]` or `engines=["anysearch"]` (no key, no
  browser) are the best recovery before reaching for the browser-gated ones.
- 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+bing.
        (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, AND all active filters — freshness, include/exclude
        domains, category, include/exclude text) within the cache TTL.
        Changing any filter is a different cache entry. 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.
        Best-effort: applied as an engine time-window param AND a client-side
        date check, but most HTML-engine results carry no parseable date, so
        undated results are kept rather than dropped (unknown != old). Treat
        it as a strong hint, not a hard filter; googlenews dates are exact.
    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" => code forges (github/gitlab/
        codeberg/bitbucket/sourceforge/...). "news" keeps only ~33 major
        outlets (client-side whitelist), so most DDG/Mojeek hits are dropped
        — pair it with the default engines (googlenews is auto-added) and
        note googlenews URLs resolve to the publisher on fetch/research.
    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
formatNomarkdown
enginesNo
categoryNo
freshnessNo
use_cacheNo
max_resultsNo
exclude_textNo
include_textNo
max_age_hoursNo
exclude_domainsNo
include_domainsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With annotations declaring readOnlyHint=true and openWorldHint=true, the description adds substantial behavioral context: caching details (use_cache, max_age_hours), engine selection nuances (defaults, browser-gated engines), category filtering mechanics (client-side whitelist), freshness best-effort handling, and return format differences. No contradictions with annotations.

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 with clear sections (Best for, Not recommended for, Returns, Common mistakes, Args). Every sentence adds value, but given the length, it could be slightly more concise. However, the structure enables skimming and front-loading of purpose.

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?

With 12 parameters, no schema descriptions, and an output schema present, the description fully covers caching behavior, engine selection, category/freshness details, common pitfalls, and return formats. It leaves no significant gaps for an AI agent to misuse the 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 description coverage is 0%, so the description carries the full burden. The 'Args' section explains every parameter in detail – default values, behavior implications, nuanced use (e.g., freshness best-effort, category filtering completeness, engines interoperability). The 'Common mistakes' section adds practical parameter usage context, far exceeding minimal schema compensation.

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 starts with a clear verb+resource phrase: 'Run a multi-engine web search and return a ranked, deduplicated link list.' It explicitly distinguishes from siblings like fetch, research, and cache_search in the 'Not recommended for' section, making the tool's unique role obvious.

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 includes a dedicated 'Best for' section for appropriate use cases and a 'Not recommended for' section listing alternatives (fetch, research, cache_search, read_doc). The 'Common mistakes' section further clarifies when not to use certain parameters, providing comprehensive guidance for an AI agent.

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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