US Census Bureau population estimate for one state, with vintage and series identifiers. $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, the description carries the transparency burden and does meaningful work: it discloses the $0.005 per-call cost, the x402 USDC-on-Base payment rail, and response contents (provenance chain and Ed25519 attestation). It does not mention rate limits or failure behavior, but for a read-only lookup the key behavioral and output traits are surfaced.
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?
Two sentences with no filler: the first states purpose and scope, the second packs price, payment rail, and response proof details. The critical information is front-loaded and every clause adds value.
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 simple two-parameter call the description covers cost, payment route, and high-level response contents, but it omits the accepted format for the required `id` and gives no cross-tool usage context. Since there is no output schema and no annotations, a bit more detail on input values would be needed for fully reliable invocation.
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 only 50% because `id` has no description, and the tool description partially compensates by indicating the id refers to a single state and by adding pricing context for the `payment` parameter. It still does not specify what `id` values look like (e.g., FIPS code, abbreviation, or name), so an agent must guess the encoding.
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 identifies a specific resource (US Census Bureau population estimate) and clear scope (one state), which separates it from sibling tools like state_gdp_series or cpi_us_monthly. It lacks an explicit verb such as 'retrieves' or 'returns,' leaning on the tool name to convey the lookup action.
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?
No guidance is given about when to prefer this tool over alternatives, such as state_gdp_series or other Census-related tools, and no exclusions or prerequisites are stated. The payment context explains how calls are billed, not when the tool should be used.
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.