US Bureau of Economic Analysis state GDP series: real and current-dollar GDP by year for one state. $0.01 per call via x402 (USDC on Base); response includes a provenance chain and an Ed25519 attestation.
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | ||
| start | No | ||
| entity | No | ||
| 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 burden of behavioral disclosure. It adds meaningful detail beyond the schema: the data source, the cost model ($0.01 via x402/USDC on Base), and the response characteristics (provenance chain plus Ed25519 attestation). It does not cover rate limits, authentication, or error behavior, but for a simple data-lookup tool this is a strong level of transparency.
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 a single sentence that packs the data source, scope, pricing, and response attestation details into a compact form. It is front-loaded with the core purpose and contains no filler, though the density of the second half is slightly high.
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 tool is a paid API call and has no output schema, so the description needs to explain enough for an agent to invoke it correctly. It covers source, pricing, and response attestation, but leaves three of four parameters undefined and does not describe return structure or the payment-quote flow, which is only hinted at in the schema.
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 25% and the only described parameter is 'payment'. The description gives no explicit meaning for 'entity', 'start', or 'end'; the phrase 'by year for one state' is the only hint. Since the schema is largely undocumented, the description should compensate, but it doesn't explain formats, valid values, or how entity should identify a state.
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 (BEA state GDP) and the scope: real and current-dollar GDP by year for one state. It doesn't use an explicit verb like 'retrieve' or 'get', but 'series' makes the data-returning purpose obvious. The focus on GDP differentiates it from sibling lookup tools such as state_population_lookup, though no sibling is named.
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 implies the tool is for retrieving annual state-level GDP data and notes it covers one state, which gives some context. However, it does not explicitly state when to use this tool versus alternatives, nor does it give any exclusion criteria or conditions for using a different tool.
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.