Clink Forge

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

With no annotations, the description carries the burden and does well: it discloses per-call cost, x402/USDC payment, and the response's provenance chain and Ed25519 attestation. It omits rate limits and error behavior, but the disclosed operational details are genuinely useful.

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 focused sentences front-load the core purpose and pack payment and response behavior into the second. There is no filler or redundancy.

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 main output, payment behavior, and attestation details without an output schema. It stops short of fully specifying the response shape or explaining what 'provenance chain' means, but it is sufficient for initial selection and invocation.

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 description coverage is 100%, and the payment parameter is already fully described in the input schema. The tool description adds cost context but no additional parameter-level semantics, so the baseline of 3 applies.

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 identifies the exact resource (EPA AirNow observations), the transformation (aggregated into AQI categories), and the output (site counts per category). This distinguishes it from more granular siblings like aqi_site_lookup.

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 does not explicitly say when to use this tool over alternatives. It implies an aggregate use case but never names a sibling such as aqi_site_lookup or gives a 'use this when...' condition.

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?

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.

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

With no annotations provided, the description carries the behavioral disclosure burden and meaningfully discloses the $0.01 per-call cost, x402 payment flow, and the provenance chain plus Ed25519 attestation on every response. It does not cover not-found or error behavior, but for a simple identifier lookup the key operational traits are present.

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 with dataset, date range, and lookup semantics. However, 'provenance chain and an Ed25519 attestation' is stated twice, which is redundant and slightly dilutes the structure.

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 single-record lookup with one required parameter and no output schema, the description conveys the data source, time range, cost, payment requirements, and response attestation. It could be more complete by noting error behavior or how to source the identifier, but these are minor for this tool type.

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 description adds meaning to the 'id' parameter by explaining it selects 'one record by its identifier,' while the schema already documents 'payment' well. Still, it does not explain where identifiers come from or how to format them, so the 50% schema-description gap is only partially compensated.

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 returns 'one record by its identifier' from a named CDC dataset with a specific date range. This distinguishes it from sibling lookup tools by the unique dataset and identifier-based access, though it does not explicitly contrast with search-style siblings.

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?

It provides clear context: use this when you have the record identifier, and it advises omitting the payment parameter to receive a free quote. It does not explicitly describe when not to use the tool or name alternative tools for broader queries.

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?

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses the $0.01 per-call cost, the x402 payment flow, provenance chain, and Ed25519 attestation on every response. The read-style lookup behavior is clear even though it is not explicitly labeled read-only.

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 reasonably short and front-loaded with the dataset and action. However, the provenance chain and Ed25519 attestation are stated twice in consecutive sentences, creating avoidable redundancy.

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-required-parameter lookup with no output schema, the description gives the source, selection behavior, cost, payment method, and response authentication metadata. It does not enumerate the record's fields, but the detailed dataset title and 'one record' adequately frame the return value.

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 50% of parameters are documented in the schema; payment is described there, but id is only typed and length-limited. The description adds that the record is selected by its identifier, which clarifies id's role, but it gives no format or example for the identifier and adds no meaning for payment beyond 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?

The description names the specific HHS dataset and makes clear that the tool returns one record by its identifier, so the action and resource are identifiable. It does not use an explicit verb like 'fetches' or 'returns', but the lookup semantics are clear and the dataset name distinguishes it from sibling lookup tools.

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?

No guidance is given about when to use this tool versus alternatives such as the other COVID-19-related lookups or summary tools. The only implied guidance is that this is a single-record lookup for this particular HHS dataset, which the agent must infer on its own.

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?

With no annotations, the description carries the full burden, and it discloses the paid nature ($0.01 via x402/USDC on Base), the response wrapper (provenance chain and Ed25519 attestation), and the data source. It omits failure behavior and rate limits, but for a simple lookup the core behavior is well covered.

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 a single front-loaded sentence that is easy to scan, but the provenance-chain and attestation statement is repeated nearly verbatim, wasting words. It could be tightened without losing 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 2-parameter, no-output-schema lookup, the description covers the dataset, the lookup key, pricing, and the response wrapper. It does not explain how identifiers are obtained or how errors are reported, but the information needed to select and begin invoking the tool is present.

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 schema thoroughly documents the payment parameter, and the description adds that 'id' selects a record from the CDC dataset, giving the otherwise undocumented required parameter some meaning. It still leaves the id format and source unspecified, so it only partially compensates for the moderate schema coverage.

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 names the exact CDC DQS dataset and the operation ('one record by its identifier'), making the resource and action clear. It stops short of 5 because it does not explicitly distinguish itself from the many sibling lookup tools, though the dataset name largely does that work.

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?

Usage is implied: call this when you need a single record from this specific CDC dataset. There is no explicit when-to-use versus alternatives or exclusion criteria, so guidance is present but only implicit.

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?

With no annotations provided, the description carries the behavioral burden. It discloses the paid nature ($0.01 via x402/USDC on Base) and the response guarantees (provenance chain and Ed25519 attestation), which are material behavioral traits. It is not a full account of error/not-found behavior, but it is substantially transparent 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 with the source and action, but the provenance-chain/attestation detail is stated twice: once in the first sentence and again in the last sentence. That redundancy prevents a higher score.

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 low-complexity paid lookup with no output schema and no annotations, it provides the essential operational context: source, lookup scope, cost, payment mechanism, and response attestation. It could add id format/not-found behavior, but nothing critical is missing for deciding when to call the 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?

Schema coverage is only 50%; the required id parameter has no schema description. The description supplies the key semantic that id is the dataset's record identifier, but gives no format or example. The payment parameter is already documented in the schema, so the description adds only partial value there.

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?

Description names a concrete resource (HHS COVID-19 Small Area Estimations Survey Primary Vaccine Series Wave 08) and a specific verb/scope: retrieve one record by its identifier. This distinguishes it from the many sibling summary/list tools such as cpsc_recall_monthly_summary or cpi_us_monthly.

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?

"One record by its identifier" implies the point-lookup use case, but the description never states when to prefer this over the dozens of sibling lookup tools, nor does it list exclusions or prerequisites beyond payment. Usage is inferable rather than explicit.

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?

With no annotations, the description carries the burden and does add useful behavioral context: it costs $0.01 per call via x402 on Base, and every response includes a provenance chain and an Ed25519 attestation. However, it omits read-only/side-effect clarity, error behavior for missing IDs, and the free-quote flow, so transparency is partial.

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 repeats 'provenance chain and Ed25519 attestation' twice, contains a truncated ellipsis in the dataset title, and mixes the dataset title with functional instructions. It front-loads the resource but wastes words on duplication and extraneous title text.

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, so the description should state what the returned record actually contains; it only mentions provenance and attestation. It also leaves the identifier format and payment flow underspecified, making the description incomplete for a paid single-record lookup.

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 schema documents only the payment parameter, and the description adds that payment is x402 USDC on Base and costs $0.01. The 'id' parameter is only loosely described as 'identifier,' without explaining its format or where to obtain it, so the description only partially compensates for the 50% schema coverage.

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 specific CDC RSV dataset and states that the tool returns one record by its identifier, which conveys a lookup operation. It does not use an explicit verb like 'fetch' or 'look up,' but the resource and scope are clear enough to distinguish it from summary or search siblings.

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?

No guidance is provided on when to use this tool over sibling lookup or summary tools, and there are no mentions of prerequisites or alternative tools. The only usage signal is implied by 'one record by its identifier,' which is insufficient for an agent deciding between this and many similar lookup tools.

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?

No annotations are provided, so the description carries the full burden. It discloses the cost per call, payment mechanism via x402, the free-quote flow, and the provenance/attestation behavior on every response. These are meaningful operational details beyond the basic lookup action.

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 reasonably compact and front-loaded, but it repeats 'provenance chain and an Ed25519 attestation' verbatim in both sentences, which wastes space without adding new 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 straightforward single-record lookup, the description covers the source dataset, the operation, the response's provenance/attestation behavior, and the paid payment flow. It does not describe the actual data fields of the returned record, but the dataset name and lookup nature make the tool usable without that detail.

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 description coverage is 50%, with only 'payment' documented. The description partially compensates by clarifying that the required 'id' parameter is the record identifier, but it does not specify the expected ID format or how to obtain a valid ID.

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 names the specific resource (CDC Lyme disease aggregated data with geography, 2022-2023) and the operation ('one record by its identifier'). This clearly distinguishes it from sibling lookup tools that target other datasets.

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 'one record by its identifier' implies a single-record lookup use case. However, there is no explicit guidance about when to prefer this tool over alternatives, when not to use it, or any search-vs-lookup distinction.

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?

With no annotations, the description carries the burden of behavioral disclosure. It does add useful details: every response includes a provenance chain and Ed25519 attestation, and calls cost $0.01 via x402 on Base. However, it does not clarify read-only status, failure behavior, or authentication requirements beyond payment.

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 with the data source and operation, but it repeats the provenance/attestation point in both sentences, which wastes space. It is not excessively long, but the redundancy prevents a higher score.

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 single-record lookup with two parameters and no output schema, the description conveys the source, selection mechanism, response attestation, and cost. However, it lacks guidance on the identifier format and does not describe the expected record fields beyond the dataset name, leaving some ambiguity for an agent.

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% because the required 'id' parameter has no schema description. The tool description says 'by its identifier' but provides no detail on what kind of identifier is expected or how to discover valid values. The optional payment parameter is well covered by the schema, so the description adds little parameter meaning.

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 retrieving one record by identifier from a specific CDC NCHS dataset, which distinguishes it from sibling tools by data source. The verb is implied ('lookup' in the tool name, 'one record by its identifier' in text), but the resource is specific and the operation is unambiguous.

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 this tool is for fetching a single record by ID rather than aggregations or searches, but it does not explicitly state when to prefer this tool over sibling summary/lookup tools. No exclusions or alternatives are mentioned, leaving usage context to inference.

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?

With no annotations provided, the description carries the behavioral burden. It discloses the monetary cost ($0.01 per call), the payment mechanism (x402 on Base/USDC), and notable response traits (provenance chain and Ed25519 attestation). This adds meaningful context beyond a simple lookup statement.

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 with no filler. The first sentence covers purpose, source, and output fields; the second covers cost and attestation. All information is front-loaded and each clause earns its place.

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?

Covers the essential input (certificate number), the return profile fields, the payment/quote flow, and expected response signatures. With no output schema, it could specify the response format more, but it gives enough for an agent to invoke and interpret the call correctly.

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 50% because `id` has no description; the tool description compensates by identifying it as an FDIC certificate number. The `payment` parameter is already described well in the schema, including the free-quote behavior, so the description need not repeat it.

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?

Describes a specific resource (US bank profile from FDIC institution directory), specific fields (charter class, regulator, headquarters, established date, active status), and a precise lookup key (FDIC certificate number). Clearly distinguished from unrelated sibling tools by domain and function.

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?

States the lookup key and the domain clearly enough that an agent knows when to use it. Does not explicitly name alternatives or exclusions, but none of the sibling tools overlap with bank lookup, so no alternative routing is needed.

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?

With no annotations, the description carries the transparency burden and does disclose meaningful non-obvious behavior: the per-call cost, x402 payment via USDC on Base, the provenance chain, and the Ed25519 attestation. It does not cover rate limits or authentication details, but the key behavioral traits are surfaced.

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 a single, tightly packed sentence that leads with the core purpose and then adds cost and response details. Every clause contributes useful information, with no filler or repetition.

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 purpose, cost, payment behavior, returned fields, provenance, and attestation, which is substantial for a two-parameter lookup tool. However, without an output schema or an explicit format/example for constructing the required id, an agent may not be able 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?

The payment parameter is well documented in the schema, and the description adds context about the pay-per-call mechanism. However, the required 'id' parameter is only vaguely described as representing congress, type, and number; the exact encoding/format is not specified, leaving an important gap at 50% schema coverage.

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 names a specific resource ('US Congress bill record') with the exact lookup dimensions (congress, type, number) and enumerates the returned fields (title, sponsor, latest action, policy area, origin chamber). This clearly differentiates it from the sibling lookup tools, which are all in unrelated domains.

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 makes the intended use case clear: retrieving a specific congressional bill by its identifying components. It does not explicitly exclude alternatives, but the sibling tools cover different topics, so the applicable context is unambiguous.

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?

With no annotations, the description does disclose important behavioral traits: the $0.005 cost, the x402 USDC-on-Base payment rail, and that responses include a provenance chain and Ed25519 attestation. It does not cover error behavior, read-only status, or exact response shape, so it is not fully 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 sentences with no filler: the first states purpose and scope, the second packs pricing and attestation context. The output is front-loaded and every clause earns its place.

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 paid data tool with no annotations and no output schema, the description is incomplete operationally: it omits the accepted year format, the shape/content of the returned record(s), and how the payment flow proceeds when payment is omitted. The provenance/attestation detail is useful but does not make the call fully reproducible.

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 covers payment semantics, but the required year parameter is only a maxLength 7 string with no format. The description adds 'by month' but still does not clarify whether year should be 'YYYY' or 'YYYY-MM', or what range of observations is returned. This is a material gap for an agent constructing a valid call.

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 names the resource (US CPI by month from BLS), the scope (national all-items series), and the payload types (index level and series metadata). This is specific and clearly separates it from the unrelated sibling lookups, even without naming a direct alternative.

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?

Usage context is implied: it is for national all-items CPI data, so an agent can infer when to choose it. However, there is no explicit when/when-not guidance or mention of alternatives, such as regional or item-specific CPI series.

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?

With no annotations, the description carries the full burden and does well: it discloses the $0.005 per-call cost, the x402 USDC-on-Base payment rail, and the response's provenance chain and Ed25519 attestation. It does not describe error or not-found behavior, but the most operationally relevant traits are disclosed.

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?

A single, dense sentence with the purpose front-loaded and payment/response details appended. Every clause earns its place, and there is no redundant or filler wording.

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 paid lookup with no output schema, the description covers what is returned, how payment works, and what extra response artifacts are included. It stops short of explaining failure modes or unknown-recall behavior, but the core context needed to call it correctly is present.

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 50% because the required id parameter has no description; the tool description compensates by clarifying that id is a recall number. The payment parameter is already well documented in the schema, so the description adds just enough meaning beyond the schema for the key parameter.

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 opens with a specific verb and resource: US CPSC recall lookup by recall number, and lists the data fields returned. This clearly distinguishes it from siblings like fda_recall_lookup and cpsc_recall_monthly_summary without needing to inspect schemas.

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—when you have a recall number—but gives no explicit guidance on when not to use it or which sibling to choose instead. There is no mention of cpsc_recall_monthly_summary for aggregate recall data or fda_recall_lookup for FDA recalls.

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?

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.

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

With no annotations, the description carries the full burden of behavioral disclosure and does well: it discloses the $0.01 per call cost, the x402 payment mechanism using USDC on Base, and the response's provenance chain and Ed25519 attestation. It does not mention what happens if payment is omitted beyond the schema note, but the core behavioral traits are 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?

The description is one tightly written sentence that front-loads the core purpose and packs in essential operational context: cost, payment rail, response fields, and attestation. Every clause earns its place.

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 lookup tool with one required parameter, the description covers the expected response fields and the unusual payment/attestation behavior. Missing details are the CVE ID format and the explicit free-quote flow for omitted payment, but the schema documents the quote behavior, so the agent can still call the tool correctly.

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% because the required id parameter has no schema description. The tool description adds basic semantics by saying 'by CVE identifier', but it does not specify the expected format such as CVE-YYYY-XXXX. The payment parameter's semantics are already covered well by the schema's 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 resource (NVD CVE details by CVE identifier) and enumerates the returned data: description, CVSS metrics, CWE mapping, and dates. This distinguishes it from sibling tools like kev_status_by_cve and cve_published_daily_summary, though it lacks an explicit action verb and relies on the tool name for the 'lookup' action.

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 use case is implied: use this tool when you need detailed information about a specific CVE identifier. It does not explicitly state when to prefer cve_published_daily_summary or kev_status_by_cve instead, nor does it mention any prerequisites or exclusions.

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?

With no annotations provided, the description carries the full behavioral burden and does reveal important traits: the call costs $0.02 via x402/USDC on Base, and the response includes a provenance chain and Ed25519 attestation. It does not mention read-only status or rate limits, but the payment and response-evidence behavior is meaningfully disclosed.

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 with no filler: the first states the core purpose and aggregation, and the second adds essential cost and response-evidence details. The highest-value information is front-loaded.

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 the tool's simplicity—one optional parameter and no output schema—the description covers source, aggregation, cost, and response attestation. It could be more explicit about the exact date window or return shape, but those are minor omissions for a daily 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 single payment parameter is already fully described in the schema, including the omit-to-get-a-quote behavior, so the baseline is 3. The description adds the dollar amount and settlement network, which complements the schema without needing to repeat it.

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 names a specific resource (NVD CVEs published per day) and a specific action (counting by CVSS severity band). It is clearly distinguishable from siblings like cve_detail_lookup and kev_status_by_cve, which operate on individual CVEs or KEV status rather than aggregate daily summaries.

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 intended use is implied by the description: use this when you need a daily aggregate of published CVEs segmented by severity. However, there is no explicit when-to-use or when-not-to-use guidance, and no alternative sibling tools are named.

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?

With no annotations provided, the description carries the full burden. It discloses the cost per call, payment method (x402 USDC on Base), and the response includes a provenance chain and Ed25519 attestation. This goes well beyond basic schema information, though it does not explicitly state read-only behavior or potential side effects, which is acceptable for a lookup 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?

The description is a single, dense sentence that fronts the core purpose ('title status'), then adds cost, payment method, and response details without redundancy. Every element earns its place, and the most important information is presented first.

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 tool lacks an output schema, and the description does not specify the id parameter's format or provide guidance on obtaining a quote by omitting payment. It mentions the provenance chain and attestation but not the full response shape. For correct invocation, an agent would need to infer or discover the id format, leaving a significant 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?

The schema only describes the 'payment' parameter; the required 'id' parameter has no schema description. The tool description does not explain what format the id should take (e.g., CFR title number) or how it maps to 'one CFR title'. While payment benefits from cost/payment method context, the id remains undefined, and the description fails to compensate for the 50% schema coverage gap.

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: to provide the latest amendment date and issue date for one CFR title. This is specific and distinguishes it from sibling tools like fedreg_doc_lookup (which deals with individual federal register documents). It also mentions the cost and attestation, further clarifying the tool's unique value.

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 conveys the exact use case (checking a CFR title's status) without ambiguity. While it does not explicitly contrast with alternatives or state when not to use it, the tool's specificity makes confusion unlikely. The guidance is implicit but clear enough for an agent to select it appropriately.

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?

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.

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.

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

With no annotations, the description carries the burden, and it does disclose useful non-obvious behaviors: the $0.02 per-call cost, the x402/USDC-on-Base mechanism, and the response containing a provenance chain and Ed25519 attestation. However, it leaves unexplained how 'one US state' is determined and does not clarify the payment handshake beyond what the schema already says.

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 with no wasted words: the first delivers the core function and scope, the second packs pricing, payment rail, and response attestation details. Everything present earns its place.

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 does a reasonable job for a single-parameter paid API, but a critical gap remains: it claims to operate 'for one US state' with no state parameter and no explanation of how that state is provided. There is also no output schema, and while the provenance chain is mentioned, the overall return structure is not described.

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 has 100% coverage for the only parameter, payment, so the baseline is 3. The description adds cost and dataset context but no additional meaning about the payment parameter's encoding, format, or required workflow beyond what the schema already documents.

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 a specific action ('recall counts'), a resource ('FDA recalls'), and a scoping detail ('for one US state'), plus grouping dimensions ('classification and product type'). It stops short of 5 because it never explains how the state is actually selected given the schema only exposes a payment parameter, and it doesn't explicitly differentiate itself from the sibling fda_recall_lookup.

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 guidance on when to choose this tool instead of fda_recall_lookup or the CPSRC recall summaries. It mentions the payment/quote flow but no usage context, exclusions, or comparison to alternatives.

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?

No annotations are provided, so the description carries the burden. It discloses a concrete per-call cost, the x402/USDC payment channel, and non-obvious response elements (provenance chain, Ed25519 attestation). It does not explicitly state read-only semantics, but 'lookup'/document retrieval makes it evident.

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 with no filler; the resource and returned fields come first, and payment/attestation details are packed into the second sentence. Every clause adds 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 simple lookup with no output schema and no annotations, it covers the required inputs, output fields, cost, and response extras. The main omission is the lack of a concrete id format/example, but schema maxLength and the phrase 'document number' are enough for basic invocation.

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 covers payment, and the tool description adds that id is a document number, which is the core missing meaning for the required parameter. It stops short of specifying document number format or examples, so it only partially compensates for the 50% schema coverage.

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?

Identifies a specific resource (US Federal Register documents) and a lookup verb, and enumerates the key fields returned (title, agencies, document type, dates). It does not explicitly differentiate itself from sibling lookup tools, but the Federal Register scope is distinct enough.

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?

No guidance on when to choose this tool over alternatives or when not to use it. The 'by document number' phrase implies an access path, but there is no mention of exclusions or related tools for other document types.

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?

With no annotations provided, the description carries the behavioral disclosure burden. It does reveal meaningful non-obvious behavior: the call costs $0.005 via x402 (USDC on Base), and the response includes a provenance chain and Ed25519 attestation. It does not describe failure modes or payment enforcement, but the disclosed cost and attestation details are substantial.

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 one dense sentence that front-loads the domain and result, then adds payment and response details. There is no filler or repetition of schema 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?

The description is informative about pricing and response provenance, but with no output schema and no annotations it omits guidance an agent needs to invoke the tool correctly: how to form or obtain the required 'id' and that fmr_area_search should be used to find areas. A caller could easily pass a place name instead of a valid HUD area code and fail.

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 schema describes only 'payment'; the required 'id' parameter has no schema description. The description maps 'id' to a metro or county area but does not explain the expected ID format or how to obtain one, so the critical parameter remains under-specified. It adds some context beyond the schema but does not fully compensate for the coverage gap.

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 resource (HUD Fair Market Rent for one metro or county area) and the returned data (rent by bedroom count for the current fiscal year). It lacks an explicit verb and never names the sibling fmr_area_search, but the singular scope is clear from 'one metro or county area.'

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 'one metro or county area' implies this tool is for single-area lookups, and 'current fiscal year' constrains the data period. However, it does not explicitly say when to use fmr_area_search instead or tell the agent to first resolve an area ID.

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?

No annotations are provided, so the description must carry behavioral disclosure. It discloses cost, payment mechanism via x402, and the presence of a provenance chain and Ed25519 attestation in the response. However, it does not explicitly state whether the operation is read-only or describe any side effects, rate limits, or auth requirements beyond payment.

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?

A single sentence that front-loads the action and resource, then efficiently conveys payment details and output attestation. No wasted words.

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 search tool with two parameters and no output schema, the description covers the core purpose, result content, payment requirements, and output attestation. It lacks details on result limits, pagination, or error behavior, but these are not critical for basic invocation given the simplicity of the 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?

Schema covers payment with a description, and description adds that omitting payment yields a free quote. For the required 'q' parameter, the description only implies it is a name search (from the tool name), but does not clarify format, length, or behavior beyond maxLength in schema. With 50% schema coverage, the description partially compensates but not fully.

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 ('Search'), resource ('HUD Fair Market Rent areas'), and output ('matching areas with their rent schedules'). The distinction from the sibling 'fmr_area_lookup' is implied by the verb 'search' vs 'lookup', but not explicitly named.

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?

Provides operational context about payment (cost, quote option) but gives no guidance on when to choose this tool over alternatives like fmr_area_lookup. No explicit when-to-use or exclusions.

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?

With no annotations, the description carries the burden and does well by disclosing the $0.005 fee, x402 payment mechanism, provenance chain, and Ed25519 attestation in the response. It does not mention error behavior or idempotency, but the paid-call and attestation details are meaningful behavioral context.

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 entire description is one dense, front-loaded sentence that conveys purpose, scope, pricing, and response characteristics without wasted words. Every element earns its place.

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 single-CVE lookup with no output schema, the description provides enough operational context: what is returned, the payment requirement, and the attestation. It does not spell out the exact response schema or error cases, but that is not critical for a lightweight lookup 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 schema only describes the payment parameter, so the description adds necessary context by framing 'id' as a CVE identifier. It also clarifies the payment flow and cost, supplementing the bare parameter names without going into excessive detail.

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 returning CISA KEV status for a single CVE, including specific fields such as due date, required action, and ransomware-campaign flag. It lacks an explicit verb like 'get' or 'lookup', but the resource and scope are 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 indicates this is for one CVE, which implies it is not for batch or list queries. It does not explicitly mention alternatives or provide when-to-use versus when-not-to-use guidance, though the domain is distinct enough that confusion with siblings is unlikely.

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?

With no annotations, the description carries the burden, and it does disclose the $0.01 cost, the x402 mechanism, and that the response includes a provenance chain and Ed25519 attestation. However, it does not clarify whether the state selection is somehow embedded elsewhere, and it omits any mention of rate limits or what happens when no alerts exist.

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, front-loaded with the core purpose, then tight supplemental details about cost and response contents. No filler or redundant restating of schema fields.

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 tool is simple on the surface, but the unexplained state selection is a critical gap. The description mentions response attestation and aggregation dimensions, but without a state parameter or an explanation of how the state is determined, an agent cannot confidently call the tool for a specific state.

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 schema's only parameter, payment, is 100% described, so the baseline is 3. But the description says 'for one state' without any schema field to specify it, creating a fundamental mismatch that prevents an agent from knowing how to perform the stated task. The description adds cost context but not the essential link to the missing state parameter.

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 aggregates active US NWS alerts by severity, urgency, and event type, which distinguishes it from the other lookup and summary tools. However, 'for one state' is ambiguous because the input schema offers no state parameter, slightly clouding what the tool actually targets.

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?

It explicitly explains the payment flow: include the x402 payment header or omit it to receive a free quote. It does not mention when an agent should prefer this over sibling tools, but the context of NWS alerts and payment is clear enough for basic invocation.

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?

With no annotations provided, the description carries the burden of behavioral disclosure. It transparently mentions the $0.001 per-call cost, the x402 payment mechanism via USDC on Base, and that responses include a provenance chain and Ed25519 attestation. This goes well beyond a simple lookup description, though it could further clarify failure or edge-case behavior.

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 of dense, relevant information with no filler. It front-loads the core purpose and returned data, then adds payment and attestation details. Every clause earns its place.

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 what the tool returns (license metadata plus provenance and attestation), how much it costs, and the payment context. Combined with the schema, an agent has enough information to invoke it correctly and interpret the response. No output schema is provided, but the description explicitly lists the key return components.

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 schema only describes the payment parameter; the 'id' parameter has no description. The tool description clarifies that 'id' is an SPDX license identifier, which adds meaning, but it does not provide examples, case-sensitivity, or formatting hints. Since the schema coverage is only 50%, the description partially compensates but leaves room for interpretation.

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 a clear verb-like operation ('lookup') and specific resource ('Canonical SPDX license record by identifier'), and enumerates the returned fields (full name, OSI/FSF approval status, deprecation flag, reference URL). It is unambiguous and distinct from sibling tools due to the explicit SPDX license focus.

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 this tool should be used: whenever an agent needs authoritative SPDX license data by identifier. It does not explicitly state exclusions or name alternative tools, but among the listed siblings none overlap with SPDX license lookup, so the intended context is reasonably clear.

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?

With no annotations provided, the description carries the burden of behavioral disclosure. It adds meaningful detail beyond the schema: the data source, the cost model ($0.01 via x402/USDC on Base), and the response characteristics (provenance chain plus Ed25519 attestation). It does not cover rate limits, authentication, or error behavior, but for a simple data-lookup tool this is a strong level of transparency.

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 a single sentence that packs the data source, scope, pricing, and response attestation details into a compact form. It is front-loaded with the core purpose and contains no filler, though the density of the second half is slightly high.

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 tool is a paid API call and has no output schema, so the description needs to explain enough for an agent to invoke it correctly. It covers source, pricing, and response attestation, but leaves three of four parameters undefined and does not describe return structure or the payment-quote flow, which is only hinted at in the schema.

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 25% and the only described parameter is 'payment'. The description gives no explicit meaning for 'entity', 'start', or 'end'; the phrase 'by year for one state' is the only hint. Since the schema is largely undocumented, the description should compensate, but it doesn't explain formats, valid values, or how entity should identify a state.

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 resource (BEA state GDP) and the scope: real and current-dollar GDP by year for one state. It doesn't use an explicit verb like 'retrieve' or 'get', but 'series' makes the data-returning purpose obvious. The focus on GDP differentiates it from sibling lookup tools such as state_population_lookup, though no sibling is named.

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 the tool is for retrieving annual state-level GDP data and notes it covers one state, which gives some context. However, it does not explicitly state when to use this tool versus alternatives, nor does it give any exclusion criteria or conditions for using a different 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?

With no annotations, the description carries the transparency burden and does meaningful work: it discloses the $0.005 per-call cost, the x402 USDC-on-Base payment rail, and response contents (provenance chain and Ed25519 attestation). It does not mention rate limits or failure behavior, but for a read-only lookup the key behavioral and output traits are surfaced.

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 with no filler: the first states purpose and scope, the second packs price, payment rail, and response proof details. The critical information is front-loaded and every clause adds value.

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 call the description covers cost, payment route, and high-level response contents, but it omits the accepted format for the required `id` and gives no cross-tool usage context. Since there is no output schema and no annotations, a bit more detail on input values would be needed for fully reliable invocation.

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% because `id` has no description, and the tool description partially compensates by indicating the id refers to a single state and by adding pricing context for the `payment` parameter. It still does not specify what `id` values look like (e.g., FIPS code, abbreviation, or name), so an agent must guess the encoding.

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 identifies a specific resource (US Census Bureau population estimate) and clear scope (one state), which separates it from sibling tools like state_gdp_series or cpi_us_monthly. It lacks an explicit verb such as 'retrieves' or 'returns,' leaning on the tool name to convey the lookup action.

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?

No guidance is given about when to prefer this tool over alternatives, such as state_gdp_series or other Census-related tools, and no exclusions or prerequisites are stated. The payment context explains how calls are billed, not when the tool should be used.

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?

With no annotations provided, the description carries the full burden, and it meaningfully discloses cost, payment rail (x402/USDC on Base), and response characteristics (provenance chain and Ed25519 attestation). It does not cover errors or edge cases, but the main behavioral traits are surfaced.

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 a single dense sentence that front-loads the resource and then adds payment and response details without filler. It is efficient and informative, though the semicolon-packed structure could be more scannable in a list of tool options.

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 paid tool with no output schema, no annotations, and four mostly undocumented parameters, the description omits how entity/start/end should be represented, what units are returned, and how to obtain the payment quote. It gives a high-level sense of the response but not enough to confidently construct the first correct call.

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 payment has a schema description; entity, start, and end are undocumented. The tool description only vaguely suggests that start/end relate to observation dates and that entity identifies a station, without specifying formats or allowed values, so it fails to compensate for the 25% schema coverage.

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 names a precise resource—NOAA NCEI monthly average temperature (GSOM) for a single weather station—and specifies included metadata and observation date. It lacks an explicit verb but is specific and distinct enough to differentiate it from all sibling tools.

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?

No guidance is provided on when to choose this tool over alternatives or how to structure a valid request. The only usage-related detail is the $0.005/call x402 payment condition, which is a requirement rather than a selection guideline.

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?

With no annotations, the description carries the transparency burden and does well: it discloses the cost model ($0.01 per call via x402/USDC) and the response's provenance chain and Ed25519 attestation. It does not cover error/not-found behavior or rate limits, but the key payment and authentication-relevant behavior is stated.

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 purposeful sentences: the first front-loads purpose and output fields, the second conveys cost, transport, and response authenticity. No filler or redundant restatement.

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?

Despite having no output schema, the description enumerates the key return fields and adds payment and attestation context. It is complete enough to decide and invoke, though it could note how this differs from uk_new_companies_daily and what happens if the company number is not found.

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%: id has no schema description while payment does. The description compensates for id by identifying it as the company number, and the tool description's x402/payment context reinforces the payment parameter. It could be more explicit about the id format or how to obtain the payment header, but the essential mapping is provided.

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 first clause names the exact resource (UK Companies House company record) and key input (company number), then enumerates returned fields (registered name, status, incorporation date, type, registered office). This clearly distinguishes it from siblings such as uk_new_companies_daily.

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 'by company number' gives clear selection context: use this tool when you have a Companies House company number and need its current company record. It does not explicitly name alternatives or exclusion cases, but the scoping is clear enough for an agent to route correctly.

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?

With no annotations provided, the description carries the burden and does disclose pricing ($0.02 per call), the x402 payment mechanism (USDC on Base), and the presence of a provenance chain and Ed25519 attestation in the response. However, it does not clarify how the rolling window is bounded, what the response shape looks like, or whether the data updates at a specific time.

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 compact sentences and front-loads the core data content before moving to pricing and response details. Every clause adds information, with no filler or repetition.

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 tool with one optional parameter and no output schema, the description covers the data source, time window, cost, payment mechanism, and response attestation details. It still leaves some ambiguity about the exact rolling-window behavior and how the results are structured, but it is largely sufficient for invocation.

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 schema already fully documents the single optional payment parameter and its behavior, so the baseline is 3. The description adds payment context ($0.02 via x402/USDC) but does not meaningfully extend parameter semantics beyond 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?

The description clearly identifies the resource: companies newly incorporated in the UK per day, sourced from Companies House advanced search over a rolling window. It uses a specific noun phrase though not an explicit verb like 'returns' or 'lists', and the sibling uk_company_lookup suggests this is the aggregate daily counts tool rather than a single-company lookup.

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?

No explicit guidance is given about when to use this tool versus alternatives like uk_company_lookup, or what distinguishes the rolling-window daily data from other company data sources. The use case is implied by the name and description but not stated as a decision rule.

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?

No annotations are provided, so the description carries the full burden. It discloses the per-call cost, the payment protocol (x402, USDC on Base), and the response includes a provenance chain and Ed25519 attestation. It does not explicitly state read-only behavior or data freshness, but the disclosed traits are meaningful and beyond basic parameter info.

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 compact sentences front-load the resource and output, then add cost and response attestation details. There is no filler or repetitive content, and every phrase 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?

For a simple lookup with optional parameters, the description is close to adequate, but it leaves the start/end ambiguity unresolved and lacks an output schema to clarify return fields. An agent could still make a reasonable call, but the 'for one date' phrasing versus the optional date-time range parameters is a real 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 description coverage is only 33%: only the payment parameter has a description in the schema. The description does not explain the semantics of start and end parameters or how they map to 'one date.' This is a meaningful gap because an agent cannot confidently construct a correct request without knowing whether to pass start=end, a range, or neither.

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 resource (US Treasury Debt to the Penny record) and the specific data it returns: total public debt split into intragovernmental holdings and debt held by the public. It lacks an explicit verb such as 'retrieve' or 'fetch', but the meaning is unambiguous and the domain distinguishes it from the sibling tools.

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 use case is implied: get a Treasury debt record for a single date. It also communicates the payment requirement via x402. However, it gives no explicit when-to-use versus alternatives, and it does not clarify whether this tool is preferred over a broader dataset or how the optional start/end parameters should be used to target one date.

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?

With no annotations provided, the description carries the transparency burden. It discloses the per-call cost, the x402 payment mechanism via USDC on Base, and the presence of provenance and Ed25519 attestation in the response. This provides meaningful behavioral context beyond a generic lookup description, though it does not elaborate on failure modes or the free-quote flow.

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, stating the core purpose and key returned fields first, followed by payment and attestation details. Every clause earns its place, with no fluff or repetition.

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 straightforward lookup tool with two parameters and no output schema, the description covers the main purpose, key response fields, pricing, payment method, and attestation. The payment parameter's free-quote behavior is documented in the schema, so the overall context is reasonably complete.

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 documentation covers the payment parameter but not the required id parameter. The description adds meaning by clarifying that id is a USGS event id, but it does not provide examples or format details. This partially compensates for the 50% schema coverage but leaves some ambiguity.

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 USGS earthquake event lookup by event id and enumerates the returned fields (magnitude, depth, place, origin time, alert level). This distinguishes it from the sibling tools, which target different domains such as air quality, CPI, recalls, or population.

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: use this when you need a USGS earthquake event identified by event id. It does not explicitly state when not to use it or mention alternatives, but no sibling tool covers the same domain, so the guidance is adequate.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the cost ($0.005 per call via x402), the payment mechanism (USDC on Base), and the response characteristics (provenance chain and Ed25519 attestation). This goes beyond the schema and gives the agent critical operational knowledge, though it does not explicitly state that the operation is read-only or describe rate limits.

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 sentence defines the core purpose and output, the second adds the critical payment and response details. No fluff; every clause adds value. The most important information is front-loaded, making it easy to scan.

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 lookup tool with two parameters and no output schema, the description covers the essentials: what it returns, the cost, and the payment flow. It does not detail the exact JSON structure of the response, but the listed fields and attestation hint are sufficient for most agents. The payment requirement is explicitly explained, preventing failed calls.

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 schema only documents the 'payment' parameter (50% coverage), leaving 'id' undocumented. The description compensates by specifying that 'id' is an ISO code, adding semantic meaning. For 'payment', the schema already explains its purpose, so the description doesn't need more. Together, the description and schema provide adequate meaning for both parameters.

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 retrieves a World Bank country record by ISO code, listing the specific fields returned (region, income group, lending category, capital city, coordinates). This distinguishes it from sibling lookup tools like 'uk_company_lookup' or 'usgs_quake_lookup' by naming the exact resource and data types.

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 provides clear usage context by explaining the payment model and how to obtain a free quote (omit payment field). It does not explicitly list alternatives, but since no sibling tool covers World Bank country data, the unique purpose makes the usage obvious. No exclusion criteria are given, but they are unnecessary.

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