US FDA enforcement action by recall number: classification, reason for recall, distribution pattern, recalling firm and current status. $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 behavioral burden. It discloses paid access ($0.005 per call), the x402 payment mechanism, and response characteristics including a provenance chain and Ed25519 attestation. It does not describe not-found behavior or rate limits, but the lookup nature and core response format are sufficiently 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 dense, front-loaded sentences: the first states purpose and output fields, the second covers cost and response verification. No wasted words or redundant restatement of the schema.
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 lookup with no annotations and no output schema, the description covers the essential context: what is being looked up, what fields come back, how payment works, and how the response can be verified. The schema fills in the payment quote behavior, so no critical gap remains.
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%, with 'id' described only as a string with maxLength 40. The description supplies the essential meaning: the id is an FDA recall number. The 'payment' parameter's meaning comes from the schema, and the description adds cost and payment-rail context.
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 states the specific resource ('US FDA enforcement action') and the accessor ('by recall number'), then enumerates the returned content (classification, reason, distribution, firm, status). This clearly differentiates it from sibling tools like fda_recall_state_summary by focusing on a single recall lookup rather than an aggregate summary.
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 clear context for use: the caller should have a recall number and want details of a specific FDA enforcement recall. It does not explicitly name alternatives or exclusions, but the 'by recall number' basis makes the appropriate use case apparent.
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.