list_lenders

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

No annotations are provided, so the description carries the full burden. It discloses the computation method (denominator: actions 1,2,3; loan_type 2), data source, attribution, and explicitly states it is historical and not predictive/recommendatory. This is exemplary transparency for a read-only listing tool.

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 front-loaded with the main purpose and keeps ancillary details (attribution, disclaimers) at the end. It is slightly verbose but every sentence adds contextual value, and the structure (purpose → use case → caveats → attribution) is logical.

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 gives a good sense of what is returned (list of lenders sorted by denial rate or volume) and even provides an example of the data range (1.8% to 78.7%). It lacks explicit output field names, but for a simple listing tool the context is sufficient.

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 is 0% and the description adds no new parameter details. However, the two parameters (sort enum, limit with min/default/max) are fully self-documented in the schema itself. The description mentions sorting by denial rate or volume but does not map to enum values; given the simplicity, the schema suffices.

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 covered FHA lenders') and specifies sorting options ('by denial rate (highest/lowest) or volume'). It also mentions the data source (CFPB HMDA 2025) and the time span, distinguishing it from sibling statistics tools that aggregate data rather than listing individual lenders.

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 a use case ('useful to see how widely the same federal program is applied across doors') and clarifies limitations (not a prediction or recommendation). However, it does not explicitly compare to sibling tools or state when not to use it, leaving some ambiguity for agent selection.

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