CPSC recalls per month, counted by hazard type over the current recall dataset. $0.01 per call via x402 (USDC on Base); response includes a provenance chain and an Ed25519 attestation.
| Name | Required | Description | Default |
|---|---|---|---|
| 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?
There are no annotations, so the description carries the full burden. It explicitly discloses the cost ($0.01 per call via x402 USDC on Base), the payment mechanism, and that the response includes a provenance chain and Ed25519 attestation. It also implies that omitting payment yields a quote. This goes beyond basic parameters and gives the agent important operational context. It does not describe potential side effects, but for a read-only summary tool, none are expected. This is strong transparency given the absence of structural hints.
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 two sentences: the first states the purpose and scope, the second covers cost and response traits. It is tightly written with no redundant words, front-loads the primary function, and conveys essential operational details in minimal space. Ideal structure for an API description.
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 covers the tool's purpose, cost, and response attestation, but it does not describe the actual data structure of the output (e.g., the list of months, hazard types, counts). Since there is no output schema, the description should at least hint at the response shape. It only mentions provenance and attestation, leaving the core data content to inference. For such a simple tool, the lack of explicit return format is a minor gap, so a 3 is fair.
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 only parameter, 'payment', is fully documented in the schema with its purpose and behavior (encoded header, omit for quote). The description adds context about the cost and payment network but does not explain how to construct the header value itself. Since schema coverage is 100%, the description need not re-explain the parameter; it provides marginal additional value about the payment ecosystem, not the parameter's semantics. Baseline 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 the tool's function: 'CPSC recalls per month, counted by hazard type over the current recall dataset.' This is a specific verb (count/summarize) applied to a specific resource (CPSC recalls) with clear grouping dimensions (month, hazard type). It naturally distinguishes itself from the sibling cpsc_recall_lookup, which is presumably a detail lookup tool, and from other agency recall tools. The purpose is unmistakable.
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 no explicit guidance on when to use this tool versus alternatives. It does not mention that cpsc_recall_lookup should be used for individual recall lookups, nor does it explain that this tool is for aggregated monthly summaries. The name conveys some of this, but the description itself lacks any 'use this when' or 'for alternative use X' guidance. With several sibling recall tools available, this is a notable omission.
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.