FHA denial statistics for one lender by name, slug, or LEI (top-100 by volume). Historical observation computed from the public CFPB HMDA 2025 record (actions 1,2,3; loan_type 2). Not a prediction about any individual application. Attribution: FinanceRateCalc, CC BY 4.0.
| Name | Required | Description | Default |
|---|---|---|---|
| lender | Yes | Lender name, slug, or 20-char LEI |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It thoroughly explains that the statistics are a 'historical observation computed from the public CFPB HMDA 2025 record', specifies criteria ('actions 1,2,3; loan_type 2'), adds scope limitations ('top-100 by volume'), and explicitly states it is 'not a prediction about any individual application'. This goes well beyond minimal disclosure.
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 three concise sentences: the first states the core function, the second provides data source and calculation context, and the third gives attribution and a caveat. It is front-loaded and each sentence adds value with no redundant content.
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?
The description is nearly complete for a simple stats tool: it covers data source, calculation criteria, scope, and attribution. The main gap is that it does not specify what the returned statistics actually are (e.g., denial rate, counts, sample size), which would be useful since there is no output schema. Still, it is quite informative.
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 the single 'lender' parameter completely with 'Lender name, slug, or 20-char LEI'. The description adds no new parameter-specific details, but it does reinforce the accepted input forms. Since schema coverage is 100%, the baseline 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 clearly states it provides 'FHA denial statistics for one lender' by name, slug, or LEI. This specific verb-resource combination, plus the scope restriction to a single lender, distinguishes it from sibling tools like national or state 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 gives clear context by specifying it is for 'one lender' and 'top-100 by volume', implying it is not for national or state aggregates. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct data scope: national, state, lender, metro gap, door effect, lender listing, and claim certification. The purposes are clearly separable even though several tools concern FHA denial statistics.
Tool names follow a consistent verb_noun snake_case pattern: get_* for retrievals, list_lenders for enumeration, and check_claim_contract for validation. There are no convention clashes or vague single-word names.
Seven tools is well-scoped for a focused statistics and claim-validation server. Each tool serves a distinct purpose and none feel redundant or bolted on.
The surface covers the main read-only operations: national, state, lender, metro, and door-level stats plus lender listing and claim contract verification. Minor gaps include retrieval of full contract metadata or other statistic categories, but core workflows have no dead ends.