US Consumer Price Index by month from the Bureau of Labor Statistics: index level and series metadata for the national all-items series. $0.005 per call via x402 (USDC on Base); response includes a provenance chain and an Ed25519 attestation.
| Name | Required | Description | Default |
|---|---|---|---|
| year | 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 does disclose important behavioral traits: the $0.005 cost, the x402 USDC-on-Base payment rail, and that responses include a provenance chain and Ed25519 attestation. It does not cover error behavior, read-only status, or exact response shape, so it is not fully transparent.
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 pricing and attestation context. The output is front-loaded and every clause earns its place.
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 paid data tool with no annotations and no output schema, the description is incomplete operationally: it omits the accepted year format, the shape/content of the returned record(s), and how the payment flow proceeds when payment is omitted. The provenance/attestation detail is useful but does not make the call fully reproducible.
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 covers payment semantics, but the required year parameter is only a maxLength 7 string with no format. The description adds 'by month' but still does not clarify whether year should be 'YYYY' or 'YYYY-MM', or what range of observations is returned. This is a material gap for an agent constructing a valid call.
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 names the resource (US CPI by month from BLS), the scope (national all-items series), and the payload types (index level and series metadata). This is specific and clearly separates it from the unrelated sibling lookups, even without naming a direct alternative.
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?
Usage context is implied: it is for national all-items CPI data, so an agent can infer when to choose it. However, there is no explicit when/when-not guidance or mention of alternatives, such as regional or item-specific CPI series.
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.