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

With no annotations, the description carries the full burden and succeeds: it identifies the data source (public CFPB HMDA 2025), the computation basis (actions 1,2,3; loan_type 2), that it is a historical observation not a prediction, and that output may include an open discrepancy note. No annotation contradiction exists.

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 well sectioned and front-loaded with the questions it answers. The question list and illustrative statistic add some redundancy, but every block earns its place (source, limitation, attribution).

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 single-optional-parameter tool with no output schema, the description is complete: it specifies return fields, optional-state behavior, scope exclusions, data provenance, and caveats. An agent has enough to call it and interpret the result.

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 covers 100% of the single parameter, and the description adds meaningful semantics: state is a two-letter USPS code, optional, and omitting it returns the ranked list. This goes beyond the schema's bare description.

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 identifies the tool's resource—small-loan denial penalty—and specifies outputs (denial rates under $150K / over $250K, ratio, ranking). It is easy to tell apart from generic denial-stat siblings by its scope, though it never names a sibling alternative, so it stops short of full differentiation.

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?

It gives explicit when-to-use context via the ANSWER question list and a when-not boundary ('NOT FOR: loan sizes between the bands'). It also explains the optional state parameter and omission behavior, but it does not explicitly point to an alternative tool for excluded cases.

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