Current EPA AirNow air-quality observation for one monitoring site: AQI value, category and reporting parameter. $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 provided, the description carries the full behavioral burden. It discloses that this is a paid call ($0.005 via x402 USDC on Base) and that the response includes a provenance chain and Ed25519 attestation, which are important operational traits. It does not cover error behavior or invalid-id handling, but those are less critical for a simple lookup.
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 compact and front-loaded, starting with the core function and then adding cost and response attestation details. Every clause adds useful information, with no redundancy or filler.
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 two-parameter lookup with no output schema and no annotations, the description covers the main operational facts: data source, temporal scope, output fields, cost, payment mechanism, and response provenance/attestation. The main gap is that it does not help the agent construct a valid id or choose between this and the related aqi_category_summary tool.
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 input schema documents the payment parameter, but the id parameter has no schema description. The tool description partially compensates by indicating the id identifies a single monitoring site, yet it does not describe the id format or how to discover valid site ids, leaving real ambiguity for the agent.
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 specifies the resource (one EPA AirNow monitoring site) and the output (AQI value, category, reporting parameter), so an agent can understand what the tool does. However, it lacks an explicit verb and does not explicitly contrast itself with the sibling aqi_category_summary, so it stops short of full sibling differentiation.
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 phrase 'for one monitoring site' and 'Current EPA AirNow air-quality observation' imply when this tool is appropriate, and the pricing information gives a practical consideration. But there is no explicit guidance about when not to use it or which sibling tool would be a better alternative, leaving the agent to infer the boundary.
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.