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deep_search

Runs a search, reads multiple top pages, and returns ranked verbatim passages, providing the evidence you need for multi-page questions in one call.

Instructions

Search the web across several pages to answer a question in ONE call: it runs a search, fetches several of the top result pages, ranks passages across all of them, and returns the few verbatim slices most relevant to your question. Use this for a question that needs reading and comparing MULTIPLE pages ("which databases use X and how do they differ", "what do reviewers say about Y") instead of running web_search and then many fetch calls yourself - it does that multi-page reading for you and hands back only what fits your context window. Do NOT use it for a quick fact a single search snippet already answers (use web_search), or to read ONE page whose URL you already have (use fetch). Returns passages (verbatim, ranked, each with its source url) - it returns EVIDENCE, not a written answer: YOU read the passages and compose the answer, and YOU decide whether they actually answer your question (grounder does not judge that - it depends on what you are really asking). If the passages do not contain the answer, say so rather than guessing. If you would rather be handed prose than read the evidence yourself, set include_answer: true and the reply ALSO carries answer - a short answer written from those same passages. The passages still come back either way, so you can check the answer against its own evidence, and the field is omitted rather than guessed at when the passages do not support one. It costs no extra pages. The pack is capped to fit your context window (max_tokens, default 600) so it won't overflow a small local model. Slower than a single fetch (it reads several pages). Costs 1 page per page read (default up to 6).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_tokensNo
max_fetchesNo
include_answerNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description fully carries the burden and excels: it discloses that the tool returns evidence, not a written answer; that the agent must compose and judge the answer; that the 'include_answer' field is optional and omitted when unsupported; and that the pack is capped to fit context and costs per page read.

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 somewhat lengthy but well-structured: it front-loads the core purpose, then covers usage, behavior, and parameters. Each sentence adds value, though it could be tightened by merging a few related points without losing substance.

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?

The description is remarkably complete for a complex tool: it covers purpose, usage, alternatives, return behavior, parameter semantics, costs, performance, and limitations. The output schema is mentioned ('Returns `passages`') and the return values are explained, so the agent has all the information needed.

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?

Although schema description coverage is 0%, the description adds meaning for the non-obvious parameters: it explains max_tokens ('pack is capped to fit your context window'), max_fetches ('Costs 1 page per page read (default up to 6)'), and include_answer ('set include_answer: true and the reply ALSO carries answer'). The required query parameter is self-evident.

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 across several pages to answer a question in ONE call' and details the multi-step process (search, fetch, rank, return verbatim slices). It explicitly distinguishes itself from siblings by naming web_search and fetch with contrasting use cases, making it unambiguous.

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 and when-not-to-use guidance with concrete examples: 'Use this for a question that needs reading and comparing MULTIPLE pages' and 'Do NOT use it for a quick fact a single search snippet already answers (use web_search), or to read ONE page whose URL you already have (use fetch).' This is exemplary.

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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