search_datasets

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 clearly describes the search functionality and return format (matching dataset IDs and names), but doesn't mention limitations like result count, pagination, search scope, or error conditions. The behavioral information provided is adequate but lacks depth about operational constraints.

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 efficiently structured with clear paragraph separation: purpose statement, usage guidance, and concrete examples. Every sentence adds value - no redundant information. It's front-loaded with the core functionality and appropriately sized for the tool's complexity.

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 (search operation with 1 parameter), no annotations, and no output schema, the description provides good contextual coverage. It explains the tool's role in the workflow, distinguishes it from siblings, and gives usage examples. However, it doesn't describe the output format in detail (just 'matching dataset IDs and names'), which would be helpful since there's no output schema.

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 100%, so the schema already fully documents the single 'query' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

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 purpose with specific verb ('Search') and resource ('ECB's 100+ statistical datasets'), and distinguishes it from siblings by explaining it returns dataset IDs and names for discovery. It explicitly differentiates from data retrieval tools like get_exchange_rates and metadata tools like explain_dataset.

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 explicit guidance on when to use this tool ('to discover what data the ECB publishes') versus alternatives ('use explain_dataset to learn its structure, or use the specific data tools... for common queries'). It includes three concrete example questions that demonstrate appropriate usage scenarios.

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