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ymylive
by ymylive

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Read-only

Run a multi-engine web search to find relevant links and snippets for discovery queries. Returns a ranked, deduplicated list of URLs to support further research.

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=["brave","bing","baidu"]` by default — those need
  captcha-friendly conditions; stick with 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+startpage.
    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 miss. 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
Behavior4/5

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

Annotations already declare readOnlyHint=true (safe read) and openWorldHint=true (web results vary). The description adds behavioral details: multi-engine merging, dedup logic, engine defaults (duckduckgo+mojeek+startpage), and limitations (engine caps around 10-20, category index lag). 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 well-structured with sections (Best for, Not recommended for, Returns, Common mistakes, Args). Each sentence adds value. Slightly verbose due to extensive examples, but still efficient for the complexity.

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?

Given 12 params, 0% schema coverage, and no output schema, the description fully covers all parameter semantics, return format details, usage constraints, and error prevention. It is complete for a complex search 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 has 12 params but 0% description coverage. The description thoroughly explains each parameter, adding meaningful context: natural-language query, engine defaults, useful max_results range, cache behavior (TTL, max_age_hours), freshness filter, domain filters, category shortcuts (e.g., 'paper' => arxiv/acm/springer/ieee/etc), text filters, and format differences (~40% fewer tokens for markdown).

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 begins with a clear verb phrase 'Run a multi-engine web search and return a ranked, deduplicated link list.' It distinguishes the tool from siblings like fetch (for known URLs), research (for links+full text), cache_search (local cache), and read_doc (reading PDFs/DOCX).

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 clearly state when to use this tool and when to use alternatives (fetch, research, cache_search, read_doc). Common mistakes 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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