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

With no annotations present, the description carries the full behavioral disclosure burden. It states the data source (public CFPB HMDA 2025 record, actions 1,2,3; loan_type 2), clarifies that the result is a historical observation rather than a prediction, and discloses the coverage constraint and applicant-mix caveat. This is strong transparency beyond the structured schema.

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 compactly organized with label-prefixed sections such as ANSWERS, PARAM, COVERS ONLY, and NOT FOR. Every sentence earns its place: it front-loads the tool's purpose, then covers scope, exclusions, source, and attribution without 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?

For a single-parameter read-only statistics tool with no output schema, the description is complete. It tells the agent accepted identifier types, exactly what values will be returned, the coverage boundary, what the tool is not for, the underlying data source, and the interpretive caveat.

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 coverage for the single parameter is 100%, and the schema already describes lender as 'Lender name, slug, or 20-char LEI'. The description repeats that format information but adds no new parameter-level semantics beyond what the schema provides, so the baseline score of 3 is appropriate.

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 is built around a concrete question-answer contract: it lists the exact queries the tool answers, such as 'what is <lender>'s FHA denial rate' and 'how strict is <lender>'. It names the return fields, declares the scope (only the 100 largest FHA lenders by 2025 volume), and is clearly distinguishable from siblings like get_national_fha_stats or get_state_denial_stats.

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 explicit when-to-use signals through its list of answerable questions and a 'NOT FOR' section that excludes individual-approval predictions and lenders outside the top-100 set. It does not explicitly route to alternative sibling tools, but the scope boundaries are clear enough for an agent to make the right call.

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