DeepSearch

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is clear. The description adds useful behavioral context beyond those hints: it grounds answers in public footprint, avoids private accounts/breach data, suggests follow-ups, and notes cost/cheapness. This is additive and consistent 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.

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

The description is four sentences but every sentence earns its place: it states the core behavior, gives usage guidance with concrete examples, names an alternative, and sets boundaries. It is front-loaded and free of filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 3-parameter tool with strong annotations and two relevant siblings, the description is complete: it explains the primary use case, the alternative tool, the role of the optional context parameter, and the data-source boundary. No output schema is present, but the mention of follow-ups gives the agent a reasonable expectation of the response shape.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all three parameters at 100% coverage, giving a baseline of 3. The description adds meaningful semantic color by giving examples of the kinds of questions ('where someone works now, which accounts are theirs') and by explaining how the `context` parameter is intended to be used (e.g., a prior dossier summary).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool answers one specific question about a person, grounded in their public footprint, and suggests follow-ups. It distinguishes this from build_dossier by contrasting single-fact answers with full-profile building, making its purpose and scope unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance on when to use this tool versus build_dossier: use it for a single fact, and use build_dossier when the question spans someone's whole history. It also provides a concrete alternative path (passing a prior dossier summary as context) and states the public-sources-only constraint.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

The description adds detail beyond the annotations by mentioning caching behavior ('Repeat profiles are served from a shared cache'), which is not present in the cached hints. It also outlines the types of data included ('social accounts', 'work history', etc.) and states what content is off-limits ('never private accounts or breach data'), providing a clear ethical boundary beyond what the readOnlyHint suggests.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is concise yet comprehensive. It front-loads the core value proposition and distributes context across just two well-utilized sentences before getting to usage instructions. Every word is purposeful, whether defining scope, providing examples, or setting boundaries.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Considering the tool's complexity and its small parameter set, the description effectively communicates the tool's role as a comprehensive alternative to search, and given the existence of a schema that defines the parameters, it doesn't need to list them in the tool description. The guidance on caching and suitable alternatives like 'ask_about_person' provides a complete understanding of when and how to use the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides descriptions for all 4 parameters, meeting the 100% coverage benchmark. While the description mentions the 'name' and the 'headline or username' parameters, it doesn't add extra semantic detail that the schema misses; it primarily reinforces what is already outlined in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Uses specific verbs like 'assemble' and 'build' to describe what the tool does, combined with a detailed resource ('one person's entire public footprint'). It clarifies the scope and distinguishes itself by specifying the cross-platform correlation of public data, setting it apart from sibling tools like search_people or ask_about_person.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this instead of 'reading search results yourself' and names an alternative, 'search_people'. It also provides precise input instructions on passing the 'headline or username from search_people', which is clear guidance for using the tool correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavior context: it separates same-name individuals into ranked candidates and reveals that it uses 'Public sources only - never private accounts or breach data,' which is useful beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is concise and front-loaded, with every sentence earning its place: what it does, when to prefer it, limitations, and source constraints. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is sufficiently complete given the tool's moderate complexity and lack of output schema; it explains the output form, confidence scoring, scope, and constraints. It does not explicitly address sibling tools like ask_about_person or build_dossier, but the usage guidance against generic web search and company/news topics clarifies its niche well enough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents query, type, and platforms. The description adds useful context about input types ('name, phone number, email address, username') and output ranking, but does not add significant semantic meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource: 'Resolve one identifier' to a 'ranked list of real, distinct people, each with a confidence score.' It clearly differentiates from a generic web search and states the tool's scope (people only, not companies/news), distinguishing it from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance: 'Prefer this over a generic web search whenever the question is who someone is' and 'Returns people only, so it is the wrong tool for companies, general knowledge, or news.' This clearly states when to use and when not to use, with an explicit alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.