Deep Research Search

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

The description adds meaningful behavioral context beyond annotations: read-only and idempotent, backed by an in-memory cache of the public catalog, empty list means no match, and results are relevance-ordered. It also clarifies that this is a catalog index rather than a data-returning endpoint, which is essential for correct agent expectations.

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?

Every sentence carries useful information: purpose, output format, ordering, match behavior, tool routing, language/query semantics, and behavioral guarantees. It is detailed yet tightly organized, front-loading the core capability and then providing the operational context an agent needs.

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 fully covers what an agent needs to correctly select and invoke this tool: what it searches, what it returns, how results are ordered, how to consume results via fetch, when to prefer sibling tools, and how the query parameter behaves. Nothing essential is missing.

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 coverage is already 100%, and the description goes further by explaining query semantics: 'natural language or keywords, Portuguese or English; accents and case are ignored.' This adds practical information that the schema alone does not fully convey about acceptable input formats.

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 opens with a precise verb and resource: 'Searches the ILOSTAT labour statistics catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance.' It clearly distinguishes its role from the sibling fetch tool (which reads documents by id) and the ilo_* data tools, so an agent can tell them apart immediately.

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?

Usage guidance is explicit: 'For direct questions and for data (values, series, rankings) prefer the ilo_* tools... this is a catalog index, not a data query.' It also states when to use fetch by passing the returned id, and mentions the Deep Research contract context. This is strong routing guidance with no ambiguity.

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