US EIA retail electricity series for one state and sector: price, revenue and sales by period. $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, the description carries the full behavioral burden. It discloses the $0.01 per-call cost, the x402 payment rail on USDC/Base, and that the response includes a provenance chain and Ed25519 attestation. This adds meaningful behavioral context beyond the schema, though rate limits and error behavior are not addressed.
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 with no filler. It front-loads the core data scope and then adds the essential payment and attestation details. Every sentence contributes useful information.
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?
Given four parameters, no annotations, no output schema, and low schema coverage, the description is incomplete. It omits response structure, parameter formats, entity coding, date handling, and the practical flow for requesting a free quote versus making a paid call. These gaps matter for an agent to invoke the tool correctly on the first attempt.
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?
Only the payment parameter has a schema description, so schema coverage is just 25%. The description hints that 'entity' corresponds to a state and sector and that 'start'/'end' relate to period, but it does not explain formats, allowed values, defaults, or required combinations. The payment quote nuance is present in the schema but not reinforced in the description.
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 tool as a US EIA retail electricity series scoped to one state and sector, and lists the key measures (price, revenue, sales by period). This is specific enough to distinguish it from siblings such as state_gdp_series or cpi_us_monthly, though it lacks an explicit verb like 'retrieve' or 'query'.
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 when to use the tool through its domain and scope ('US EIA retail electricity series for one state and sector'), which is clear context. However, it gives no explicit guidance on when not to use it, no alternative tool names, and no prerequisites such as required state/sector identifiers or date conventions.
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.