OECD-Search

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: defaults to last 100 observations (max 1000) for context window protection, supports filters and time periods, and warns about large datasets (e.g., 70,000+ observations). This adds valuable context beyond the input schema, though it could mention response formats or error handling.

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 appropriately sized and front-loaded with the core purpose, followed by important usage warnings. Every sentence adds value, such as the default behavior and dataset size warnings, with no redundant information. It could be slightly more structured by separating guidelines into bullet points, but it remains efficient.

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 complexity (5 parameters, no output schema, no annotations), the description is moderately complete. It covers purpose, usage guidelines, and behavioral traits well, but lacks details on parameter semantics and output values. For a data query tool with multiple parameters, this leaves room for improvement in guiding the agent on expected results or error cases.

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 description coverage is 0%, so the description must compensate. It adds meaning by explaining that parameters like 'filter', 'start_period', 'end_period', and 'last_n_observations' are used to control data size, and 'dataflow_id' is implied as the dataset identifier. However, it doesn't detail the syntax or format of these parameters, leaving gaps in understanding how to use them effectively.

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 verb ('query') and resource ('actual statistical data from an OECD dataset'), making the purpose explicit. However, it doesn't explicitly distinguish this tool from sibling tools like 'search_dataflows' or 'get_data_structure', which might also involve data retrieval but with different scopes or formats.

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 provides clear context on when to use this tool by emphasizing the need to control data size with filters, time periods, or limits, especially for large datasets. It implicitly suggests alternatives by warning about defaults, but it doesn't explicitly name when to use this versus sibling tools like 'search_dataflows' or 'get_popular_datasets'.

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