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compare

Read-onlyIdempotent

Retrieves excerpts from 2-5 URLs for comparing content against a specific question. Helps evaluate differences or similarities across sources.

Instructions

Fetch 2-5 URLs concurrently and return per-URL excerpts so the LLM can compare them against a single question in one round trip.

Best for:
- Side-by-side product/feature/article comparisons.
- "Compare X to Y" or "How does A differ from B" queries.
- Triangulating a fact across multiple sources.

Not recommended for:
- >5 URLs -> use `fetch_batch`.
- 1 URL -> use `fetch`.
- Don't have URLs yet -> use `search` or `research` first.

Returns:
- markdown (default): a comparison brief with per-URL sections, each
  containing title, sitename, published date, and a smart-truncated excerpt.
- json: {question, urls, excerpts:[{url, title, excerpt, ...}],
  tokens_estimated}.

Common mistakes:
- Asking `compare` to actually answer the question — it returns material,
  the LLM does the comparison.
- Passing >5 URLs and expecting them all to fit in context — use
  `fetch_batch` for bulk reads.

Args:
    question: The comparison question the LLM will answer using the
        returned excerpts.
    urls: 2-5 absolute http(s) URLs.
    format: "markdown" (default) or "json".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes
urlsYes
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, idempotentHint, openWorldHint, so safety is covered. The description adds detailed behavior: returns a comparison brief with title, sitename, date, smart-truncated excerpt; supports markdown and json formats; and constraints on URL count. 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.

Conciseness5/5

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

Well-structured with sections (Best for, Not recommended for, Returns, Common mistakes, Args). Every sentence adds value; no fluff. Front-loaded with core 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?

Given the tool's complexity (3 parameters, output schema, annotations), the description is complete. It covers purpose, usage guidelines, behavioral details, parameter semantics, and return format. No gaps.

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 0% description coverage, so description fully compensates. It explains the question parameter as the comparison query, urls as 2-5 absolute http(s) URLs, and format as markdown (default) or json with examples. Adds meaning beyond schema.

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 fetches 2-5 URLs concurrently and returns per-URL excerpts for comparison. It distinguishes from siblings (fetch for 1 URL, fetch_batch for >5 URLs) with specific verb+resource.

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?

Explicitly provides best-for and not-recommended-for scenarios, names alternative tools (fetch, fetch_batch, search, research), and lists common mistakes to avoid. Comprehensive guidance for selection.

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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