query_data

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

With no annotations, the description carries full burden. It discloses default behavior (last 100 observations, max 1000) and context protection. However, it does not state whether the operation is read-only or mention any side effects.

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?

Two sentences, one bold warning, no fluff. Key information is front-loaded and every sentence earns its place.

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 no output schema, the description adequately covers the critical behavioral trait (data size limits). It does not explain return format, but that is acceptable for a query tool where the data structure is likely known from get_data_structure.

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 100%, but the description adds context beyond the schema: it explains the purpose of last_n_observations, its default and max, and warns about large datasets. This adds value for an agent deciding how to parameterize the call.

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 'Query actual statistical data from an OECD dataset,' specifying the verb and resource. It distinguishes from siblings like list_dataflows or get_data_structure by focusing on data retrieval.

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?

Provides explicit guidance on controlling data size with filters, time periods, or last_n_observations, including a concrete example (SOCX_AGG). Warns about defaults and limits, but does not explicitly state when not to use this tool versus alternatives.

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