list_datasets

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

Annotations already declare readOnlyHint=true, and the description adds context beyond that by specifying the exact return fields (dataset name, data table name, API endpoint path, description) and the exact counts. This helps set expectations without contradicting the annotations.

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 two concise sentences, front-loaded with the key action and resource, then detailing the return fields. Every word adds value, with no redundancy or filler.

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?

With no output schema, the description adequately explains what the tool returns. The simple, no-parameter design means the description is sufficient for an agent to understand the tool's scope and output. No critical information is missing.

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 tool has 0 parameters, so the baseline is 4. The description doesn't need to explain parameter semantics and doesn't attempt to invent any. Schema coverage is effectively 100% since there are no properties.

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 uses a specific verb ('List') with a clear resource ('U.S. Treasury Fiscal Data API datasets') and includes concrete details (53 datasets, 181 endpoints). It also lists the return fields, distinguishing it from sibling tools like query_fiscal_data or get_endpoint_fields.

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 clearly implies when to use this tool: when you need a catalog of all Treasury datasets and their endpoints. It doesn't explicitly mention alternatives or exclusions, but the zero-parameter design and 'all' keyword make the intended usage evident.

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