HUD Fair Market Rent for one metro or county area: rent by bedroom count for the current fiscal year. $0.005 per call via x402 (USDC on Base); response includes a provenance chain and an Ed25519 attestation.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| payment | No | Encoded x402 X-PAYMENT header value. Omit to receive the payment requirements (free quote). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It does reveal meaningful non-obvious behavior: the call costs $0.005 via x402 (USDC on Base), and the response includes a provenance chain and Ed25519 attestation. It does not describe failure modes or payment enforcement, but the disclosed cost and attestation details are substantial.
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 one dense sentence that front-loads the domain and result, then adds payment and response details. There is no filler or repetition of schema information.
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 informative about pricing and response provenance, but with no output schema and no annotations it omits guidance an agent needs to invoke the tool correctly: how to form or obtain the required 'id' and that fmr_area_search should be used to find areas. A caller could easily pass a place name instead of a valid HUD area code and fail.
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 describes only 'payment'; the required 'id' parameter has no schema description. The description maps 'id' to a metro or county area but does not explain the expected ID format or how to obtain one, so the critical parameter remains under-specified. It adds some context beyond the schema but does not fully compensate for the coverage gap.
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 resource (HUD Fair Market Rent for one metro or county area) and the returned data (rent by bedroom count for the current fiscal year). It lacks an explicit verb and never names the sibling fmr_area_search, but the singular scope is clear from 'one metro or county area.'
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 phrase 'one metro or county area' implies this tool is for single-area lookups, and 'current fiscal year' constrains the data period. However, it does not explicitly say when to use fmr_area_search instead or tell the agent to first resolve an area ID.
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 unique dataset and operation: lookups by ID, summary aggregations, time series, or search. Even tools with similar descriptors (e.g., FMRArea lookup vs. search, FDA vs. CPSC recalls) are clearly separated by resource and output type.
All tool names are lowercase snake_case and mostly follow a `domain_resource_kind` pattern such as `fda_recall_lookup` and `cpsc_recall_monthly_summary`. A few outliers like `bank_profile_lite`, `cpi_us_monthly`, and `us_debt_to_penny` break the dominant suffix convention but remain readable.
24 tools is on the high side for a single server, but this appears to be an aggregator of many independent public datasets, so each tool represents a distinct data source and has a purpose. It is slightly above the ideal ergonomic range but not bloated or redundant.
As a read-only attested-data lookup service, the set provides good coverage with both point lookups and aggregate summaries across many domains. The main gaps are the lack of a catalog/discovery endpoint and search support for most identifier-based lookups, but agents can work around those with known identifiers.