search_datasets

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

With no annotations, the description carries the full burden. It discloses that datasets are un-normalized, have no VeriTR indicator id, and are intended for discovery/reporting gaps only. It also notes TÜİK metadata is English-only, which is a behavioral trait. It does not mention pagination or error behavior, but the output schema helps cover return structure. A solid 4.

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 well-structured: a clear opening sentence, a usage condition, a purpose clarification, and a practical note. Every sentence contributes unique information without redundancy. Perfectly front-loaded.

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?

Given the tool's moderate complexity (3 params) and the presence of an output schema, the description sufficiently covers purpose, usage context, and unique caveats. It lacks explicit mention of limit's effect, but the schema's default/max/min supply that. Overall, it is contextually complete for an AI agent to select and 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?

Schema coverage is 67%, with query and provider described. The description adds semantic guidance for the query parameter (English keywords match better), which is valuable. However, it does not elaborate on limit's behavior or provider's null meaning. The added query tip pushes it above baseline, but it only partially compensates for the uncovered parameter.

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's function: 'Search the raw dataset catalogs of the source institutions.' It distinguishes itself from siblings by explicitly referencing search_indicators and get_series, noting these are un-normalized datasets without a VeriTR indicator id. This provides a specific verb, resource, and clear differentiation.

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: 'Use this only when search_indicators returns nothing useful' and 'not for get_series.' It also provides a language caveat (English keywords match better) to guide effective use. This satisfies when-to-use and when-not-to-use criteria.

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