Frontera Signal

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds non-obvious behavior the annotations cannot convey: the 4-week truncation limit, that the remainder is paid, and that each row carries week, as_of and status. It stops short of stating pagination or ordering guarantees, so it is strong but not exhaustive.

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?

Three sentences, zero filler: scope first, series enumeration second, paid-alternative routing last. The most decision-relevant constraint (4 weeks, free) is front-loaded rather than buried.

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?

There is no output schema, and the description compensates by describing row shape (week, as_of, status). Combined with the enum explanation and the paid/free boundary, an agent has everything needed to call this correctly and to know when not to.

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 100% and the enum already lists the five values, so the baseline is 3. The description goes beyond by explaining what each value actually measures (e.g. 'permits-weekly (building permits by city)', 'labor-ranges (labor and financing conditions)'), letting an agent pick the right series without external knowledge.

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?

States a specific verb (Get) and resource (latest 4 weeks of a weekly Rio Grande Valley series) and enumerates each available series with a parenthetical gloss of what it measures. An agent can distinguish this from get_sample and list_endpoints without opening either schema.

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?

Explicitly scopes the tool to the free window ('latest 4 weeks ... for free') and routes the agent elsewhere for anything older: 'The full history is a paid endpoint; how_to_pay with series-<name> gives its URL and price.' That is a clear when-to-use / when-to-use-something-else split naming the sibling tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds real value: the response is byte-for-byte what a paid call returns, so results mirror production. It omits any note on sample size or truncation, which matters for a preview tool.

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 sentences, no filler. The core value proposition leads, and the workflow detail (where to get the endpoint key) follows as supporting 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?

For a one-parameter tool with no output schema, the definition supplies the essentials: what comes back, how to obtain the argument, and why you'd call it. Only the absence of any statement about response size or preview limits leaves a small gap.

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 100% with a 24-value enum, so the schema does most of the work. The description still adds provenance by naming list_endpoints as the authoritative source of valid keys, though its inline examples duplicate enum values already visible in the schema.

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?

States a specific verb and outcome: fetch a real paid-endpoint response for free to inspect structure and coverage before paying. It is clearly distinct from list_endpoints (which supplies the key) but never mentions how it differs from the sibling get_free_series, which plausibly offers overlapping free data.

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?

Gives a clear when-to-use rule: run this before paying to validate fields, dates and coverage. It also implies the prerequisite workflow by pointing at list_endpoints for the key. It stops short of stating exclusions, e.g. when to prefer get_free_series or how often sampling is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes well beyond the annotations: it discloses that validation mirrors the free check the API runs before payment, that a refused or empty query surfaces here, that the paid endpoint is never called and nothing is paid. It also explains the payment model (x402, no account or API key, USDC on Base), which no annotation conveys.

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?

Four sentences, front-loaded with what is returned, then usage, then safety, then payment mechanics. Every sentence carries information an agent needs, with no restatement of the name or title.

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?

With no output schema, the description does the work of describing the return payload (URL, price, network, asset) and the observable failure mode (refused or empty query). The one gap is that it doesn't say whether the response is cached or how stable the quoted price is, which matters for a pre-payment check.

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 100%, so the baseline is 3, but the description adds meaning: the params you supply are run through the same validation gate as the real call, so they are not just passthrough. It doesn't restate formats, but that is already handled by the schema.

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?

Names a specific verb and resource: it returns the exact URL to call for a paid answer, plus price, network and asset. An agent can tell this apart from a live data fetch because the description states it never calls the paid endpoint. It stops short of naming siblings like list_endpoints or get_sample directly in the prose.

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?

Says to pass the endpoint key and the query parameters you plan to send, and explains the result is the same free pre-payment check the API runs, which tells the agent when this is the right call. It doesn't explicitly say to run list_endpoints first or when to skip this step, so the routing guidance is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint and non-destructive, so the safety profile is covered; the description adds the meaningful extras — that the listing is itself free ("Free.") and that pricing is quoted in USDC per call. It doesn't state ordering or whether the catalog is stable across calls, but the annotation coverage lowers the bar.

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 core purpose and the "before you spend anything" trigger are front-loaded, but the middle sentence is a sprawling run-on enumeration of data domains (permits, bridge wait times, peso, diesel, alcohol sales, home health agencies) that costs significant length for marginal selection value. It earns some of its place by conveying coverage, but not all of it.

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?

With no output schema, the description carries the full burden of describing return values, and it does so explicitly: price per call, what the call returns, example question, parameters with valid values, and a free sample URL. Nothing an agent needs to call this zero-param tool is missing.

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?

Zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies the tool takes no input by framing it as a full catalog dump.

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?

States a specific verb and resource ("List every paid Frontera Signal endpoint") and scopes it to a concrete domain (Rio Grande Valley, Texas market data). An agent knows immediately what it gets back: a catalog of priced endpoints with per-call cost in USDC, return description, example question, parameters and a free sample URL.

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?

"before you spend anything" gives a clear condition for calling it: discovery prior to any paid invocation. It does not explicitly name the siblings (get_free_series, get_sample, how_to_pay) or say when to prefer them over this one, so routing between the free-sample path and the catalog is left implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.