list_datasets

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the search capability and parameter defaults, but doesn't describe important behaviors like pagination, rate limits, authentication requirements, error conditions, or what the output looks like. For a list operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 extremely efficient - a single sentence states the core purpose with optional feature, followed by clear parameter explanations. Every element earns its place with no redundant information. The structure separates the high-level description from parameter details, making it easy to parse.

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 (list operation with search), no annotations, but with an output schema present, the description is minimally adequate. The output schema will handle return value documentation, so the description doesn't need to explain outputs. However, for a tool with no annotations, it should provide more behavioral context about how the listing works, what authentication is needed, or any limitations.

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?

With 0% schema description coverage, the description compensates well by explaining both parameters: 'search' is described as a 'free-text query (searches title and description)' and 'limit' as 'max datasets to return (1-100)'. This adds meaningful context beyond the bare schema, though it doesn't cover all possible semantic nuances like search algorithm details or limit enforcement behavior.

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 action ('List datasets') and target resource ('on datos.cdmx.gob.mx'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'describe_dataset' or 'query_records', but the core functionality is well-defined. The description avoids tautology by specifying the domain and search capability.

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 no guidance on when to use this tool versus alternatives like 'describe_dataset' or 'query_records'. It mentions an optional free-text search feature but doesn't explain when searching is appropriate versus other filtering methods. There are no usage prerequisites, exclusions, or comparisons to sibling tools.

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