search_datasets

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches datasets, but doesn't describe how results are returned (e.g., format, pagination), whether it's read-only (implied but not stated), performance characteristics, or error handling. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, with no redundancy or fluff.

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 2 parameters), 100% schema coverage, and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits (e.g., result format, read-only nature) and usage guidelines versus siblings. Without annotations or output schema, more context would be beneficial for full understanding.

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 description coverage is 100%, with clear documentation for both parameters ('query' and 'limit'). The description adds no additional meaning beyond what the schema provides—it mentions searching by name or description, but the schema already describes 'query' as a search query for datasets. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('search') and resource ('datasets'), and specifies the searchable fields ('by name or description'). It distinguishes from siblings like 'get_dataset' (retrieves specific dataset) and 'list_datasets' (likely lists all without filtering). However, it doesn't explicitly differentiate from 'get_latest_data' or 'get_observation', which might be related but not identical search operations.

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 implies usage for searching datasets by name or description, but provides no explicit guidance on when to use this tool versus alternatives like 'list_datasets' (which might list all datasets without filtering) or 'get_dataset' (which retrieves a specific dataset by ID). There's no mention of prerequisites, exclusions, or comparative scenarios.

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