search_datasets

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false), so the description need not restate those traits. It adds useful scope context (the 53-dataset/181-endpoint universe and match targets) but says nothing about result format, pagination, ranking, or empty-result behavior. No contradiction with the annotations, and the added value is moderate but not deep.

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?

Two sentences with zero waste. The primary verb-resource pair is front-loaded in the first sentence, and the second sentence delivers scope and match-target details. Every clause carries information an agent needs; nothing is redundant with the schema or annotations.

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?

For a one-parameter read-only search tool with full schema coverage and a complete annotation set, the description provides everything needed to invoke it correctly. The only gap: it doesn't hint at the result shape (e.g., that results are matching dataset/endpoint references suitable for feeding into query_fiscal_data), which would aid workflow chaining. This is minor given the tool's simplicity.

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%: the query parameter is fully documented with type, case-insensitivity, and concrete examples ('debt', 'exchange rate', 'gold', 'auction'). Per the baseline for high schema coverage, the description earns a 3. It adds mild value by enumerating the fields the keyword matches against (name, table name, endpoint path, description), which is context not present in the schema.

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?

States a specific verb ('Search'), a precise resource ('Treasury Fiscal Data datasets and endpoints'), and the searchable dimensions ('name, table name, endpoint path, and description'). The scope quantification ('all 53 datasets (181 endpoints)') is concrete and distinguishes this discovery tool from data-retrieval siblings like query_fiscal_data and structural tools like list_datasets and 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?

Usage is implied rather than explicit: the scope statement makes clear this is the discovery entry point for the Treasury Fiscal Data API, which routes an agent toward it before query_fiscal_data or get_endpoint_fields. However, it never names alternatives or states when not to use it, leaving the routing decision to inference. A short 'use query_fiscal_data for actual data retrieval' note would have pushed this to a 4 or 5.

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