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

With no annotations, the description carries the full burden. It transparently discloses the output contents (columns, numeric flags, row count, provenance banner) and its role as the schema-introduction tool. It doesn't explicitly state read-only behavior, but the nature of the output makes that obvious. It adds useful context about being a first-step call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no filler. The first sentence front-loads the core output (columns, numeric flags, row count, provenance banner) and the second adds a clear usage directive. Every word 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 zero-parameter tool with no output schema, the description fully covers what it returns and when to call it. It specifies the exact dataset and the purpose of the call. Nothing an agent needs to correctly invoke it is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema is empty and there is nothing to explain. Per the rubric, a baseline of 4 applies for tools with no parameters. The description doesn't need to add parameter information, and it doesn't try to invent any.

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 what the tool does: it returns the columns, numeric flags, row count, and provenance banner of a specific dataset. It also explicitly frames this as the schema-learning entry point, distinguishing it from siblings like dataset_provenance or dataset_stats that serve different purposes.

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 'Call this first to learn the schema' provides explicit guidance on when to use this tool, establishing it as the initial discovery step. It doesn't explicitly contrast against alternatives, but the context and sibling names make the intended usage clear enough.

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 bears full responsibility for behavioral disclosure. It reveals that rows are returned in the order of given values, but omits critical details such as whether the output is a list, how missing values are handled, case sensitivity, pagination, or error behavior. The agent has insufficient information to predict the tool's exact behavior beyond the basic filter.

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, concise sentence that front-loads the core purpose and key constraint (order). It wastes no words and communicates the primary function effectively, though it could benefit from a slightly more structured breakdown of parameters and outputs.

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 absence of an output schema, annotations, and any schema property descriptions, the tool definition is incomplete. The description does not explain the return format, potential errors, or the exact expectations for 'column' and 'values' (e.g., whether column names are case-sensitive, whether values need to match exactly). An agent would struggle to confidently construct a valid call without external knowledge of the dataset.

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 0%, so the description must compensate. It clarifies that 'column' is the field to filter on and 'values' are the values to match, and notes that row order follows the given value order. However, it does not explain that 'column' must be a valid column name from the dataset, nor does it specify constraints like case sensitivity or matching logic (exact vs substring). The description adds value but leaves several semantic gaps.

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 a specific action (retrieve rows) on a specific resource (Card Machine Pricing dataset) with a defined filter condition (column matches any given values) and ordering behavior. It explicitly ties to a use case ('X vs Y' questions), making it distinguishable from sibling tools like dataset_row (single row) or dataset_search (keyword search).

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 X vs Y questions' implies a comparative use case, but it does not explicitly state when to use this tool over alternatives or when not to use it. No sibling tools are referenced, and there is no guidance on prerequisites such as valid column names or value formats. The intended context is only partially conveyed.

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 bears the burden of behavioural disclosure. It does specify what is returned (source, date, licence, citation) and 'Read this' implies a read-only operation, but it does not explicitly state that it is non-destructive or requires no permissions. The added context about content is helpful, yet the safety profile is only implied.

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 succinct sentences with no filler: the first lists the exact content returned, and the second front-loads the usage context. Every word earns its place, and the most relevant information appears 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?

For a zero-parameter, output-schema-free tool, the description covers the essentials: what the data refers to, what fields are present, and when to use it. The only small gap is the exact return format, but this is unlikely to block correct 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 tool has zero parameters, so the schema already covers everything; the baseline for 0 parameters is 4. The description need not elaborate, and adding parameter details would have been redundant.

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 verb and resource: it returns the source, compute date, licence, and citation for the Card Machine Pricing dataset. This clearly distinguishes it from siblings like dataset_row or dataset_stats, which analyze data rather than explain its provenance.

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 states a clear usage context: 'Read it to attribute a figure correctly,' signalling when this tool is appropriate. It does not name exclusions or alternatives, but given that no sibling fulfils this provenance role, the guidance is sufficient.

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 burden. It does disclose the key behavior: matching rows where a column equals a value exactly, case-insensitively. However, it does not describe the return format, whether multiple rows can match, or what happens with invalid columns/missing values.

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 short, front-loaded sentence with no filler. It states the dataset, the matching operation, and the main behavioral caveat (case-insensitivity) without any 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?

The tool is simple and has only two string parameters, so a one-sentence description is mostly adequate. But with no output schema, the description should at least explicitly state what the call returns; 'the rows...' makes the result inferable but not explicit.

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?

With schema description coverage at 0%, the description tries to compensate by tying 'column' and 'value' to an equality condition, and it adds the case-insensitive behavior. It stops short of explaining which column names are valid or what the 'value' format should be, so semantic coverage is still incomplete.

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 title and description identify a specific verb ('look up') and resource ('Card Machine Pricing dataset'), and the exact-match/case-insensitive phrasing clearly differentiates this from search-oriented siblings like dataset_search. An agent can tell it is an exact-equality lookup rather than a general search.

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 that this tool is appropriate for exact, case-insensitive matching and not for fuzzy or partial search, but it never explicitly names the alternative tool or states when not to use it. The usage context is inferable but not spelled out.

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 case-insensitive matching, all-cell coverage, and the 50-row cap, but it does not describe ordering, default limit behavior, or the exact shape of returned rows. It is adequate for a simple search tool but 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?

One sentence communicates the operation, dataset, matching rule, and result cap with no filler. The key behavioral detail, case-insensitive cell matching, 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?

For a low-complexity read/search tool with no output schema and no annotations, the description covers the essential invocation knowledge: what is searched, how matching works, and the result limit. Minor gaps like default limit and return-row shape do not prevent correct use.

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%: query has a description while limit does not. The description compensates by clarifying that the query matches case-insensitively in any cell and that results are capped at 50, adding meaning beyond the bare parameter names and constraints.

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 verb/resource ('Rows of the Card Machine Pricing dataset') and states the matching semantics: cells containing the query, case-insensitive. This clearly distinguishes it from sibling tools like dataset_stats, dataset_columns, or dataset_row.

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 clear: use it to find rows by arbitrary text in any cell. However, it does not explicitly name alternatives or exclusion conditions, so the agent must infer when this tool is preferred over sibling tools rather than being told.

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 disclosure burden and does it well: it states that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. These are meaningful edge-case behaviors beyond the title. Minor ambiguity remains about how 'count' relates to excluded rows, and no return format is described.

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 delivers the core purpose, the dataset scope, the output list, and the key preprocessing behaviors with no filler. The most decision-relevant information appears up front.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter statistics tool, the description is near-complete: it defines the input semantics, the dataset, the computed values, and the handling of malformed numeric rows. It could be slightly more complete by specifying the exact return shape or behavior when no rows remain, but these are minor given the low complexity.

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 defines 'column' as a string with minLength 1 and has 0% description coverage. The tool description compensates by clarifying that the parameter must identify a numeric column and that the tool tolerates formatted values like commas and currency. It does not enumerate valid column names, but it gives the agent enough semantic grounding for a single 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 names the exact resource (Card Machine Pricing dataset), the specific action (compute summary statistics), and enumerates the outputs: count, min, max, mean, median, and sum. This distinguishes it from sibling tools like dataset_top or dataset_row.

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 it clear that the tool applies to a numeric column of the Card Machine Pricing dataset, which implies when to use it. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, so the usage guidance remains implicit rather than directive.

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 safety and behavior burden. It does disclose that the result is the highest or lowest rows by a numeric column, but it omits how limit defaults behave, how ties or non-numeric values are handled, and what the return payload looks like. The core ordered-retrieval behavior is clear, but the behavioral context is not rich.

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 one-sentence description is compact, puts the operation first, and adds a short illustrative use-case without redundancy. It is slightly informal and the em-dash phrasing could be cleaner, but there is no wasted 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?

For a simple 3-parameter tool with no output schema and no annotations, the description covers the core invocation but leaves gaps: limit's default value, invalid column behavior, and the result row shape are not addressed. Sibling dataset_columns could supply valid column names, but the description doesn't point to it.

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 ascending has a description), so the description should compensate for column and limit. It adds that column must be numeric, but limit has no meaning or default discussed anywhere, and ascending is only indirectly reflected by 'highest (or lowest)'. Thus parameter semantics are under-specified.

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 title and description both anchor on a specific operation: ranking rows of the Card Machine Pricing dataset by a numeric column, with the 'most/least X' phrasing making the ordering intent explicit. This clearly distinguishes it from sibling tools like dataset_row (single row retrieval) and dataset_stats (aggregations).

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 a use case ('which is the most/least X') but never states when to prefer dataset_top over siblings such as dataset_search or dataset_stats, nor gives exclusions. An agent must infer usage from the name/title rather than from explicit routing guidance.

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 and does a good job: it states that nothing is bought, ordered, or paid, that no quote is guaranteed, that it is free, and that it returns who receives the details, the consent wording, and how confirmation happens. It could still more explicitly say that `enquiry_describe` itself performs no side effects, but the read/explanation nature is clearly communicated.

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 well front-loaded with the 'read first' directive and then gives the core transaction, the caveats, and the return contents in compact order. It is economical, though the title already conveys part of the 'not a purchase, not a guaranteed quote' point.

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 no-parameter, no-output-schema tool, the description gives enough for a caller to understand what it will return and what the related `submit_enquiry` action does. It is not a 5 because it does not provide a fully explicit outline of the returned content's format or structure, but it is sufficient for correct use.

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 no parameters, so there are no parameter semantics for the description to supplement. The 0-parameter baseline of 4 applies here because nothing additional is needed.

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 that this tool explains what `submit_enquiry` does: it is an explainer rather than the submission action itself. It is specific about the subject and outcome, though it relies on the tool name and phrasing rather than explicitly contrasting itself with siblings like `enquiry_fields` or `submit_enquiry`.

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 opening 'Read first' gives useful contextual guidance: this tool is meant to be used before acting on an enquiry. However, it stops short of explicit when-to-use versus when-not-to-use guidance or naming alternatives, so the usage direction is implied rather than fully stated.

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

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

No annotations are provided, so the description carries the burden. It discloses that the tool returns field metadata and that answers should be keyed by field key, which implies a read-only introspection behavior. However, it doesn't describe the return format, whether it includes all fields or only some, or any edge cases like conditional fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no wasted words. The first sentence lists the exact contents, and the second provides the actionable usage instruction. It is front-loaded with the core purpose.

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 zero-parameter introspection tool, the description is largely complete. It explains what the tool returns and how to use the result. The only minor gap is not describing the exact output structure (e.g., array vs object), but with no output schema, the description does enough.

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 tool has zero parameters, so the schema is empty. The description compensates by explaining what the output contains (key, label, type, required, help text, options) and how to use it with submit_enquiry. With 0 params, baseline is 4, and the description meets 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 clearly states the tool's purpose: it returns every field of the Card Machine Pricing enquiry, including key, label, type, required status, help text, and allowed options. This is specific and distinguishes it from sibling tools like submit_enquiry and enquiry_describe.

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 explicitly tells the agent to pass answers to submit_enquiry keyed by field key, which provides clear usage context. It doesn't explicitly state when not to use this tool or name alternatives, but the instruction to use it as a precursor to submit_enquiry is strong guidance.

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 of behavioral disclosure. It thoroughly details the validation step, the consent requirement, the email notification, the delayed provider visibility, and the fact that this is not a purchase or guaranteed quote, so an agent understands the side effects and constraints.

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?

Although the description is long, it is densely packed with necessary procedural detail and clearly structured by Step 1 and Step 2. The quoted consent line is included verbatim, and 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?

There is no output schema, but the description fully compensates by stating what each call returns: a summary, consent line, and confirmation token in step 1, and submission plus email follow-up in step 2. The two-step flow, consent semantics, and provider-visibility condition are all specified, leaving no critical gap for an agent to 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?

The schema already covers all three parameters, which sets a baseline of 3, but the description adds significant meaning beyond the schema. It explains that answers must be keyed by field key from enquiry_fields, that consent must match the exact quoted consent line, and that the confirmation token must come from step 1 and be reused in step 2.

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 and resource: it submits an enquiry to Card Machine Pricing. It explicitly distinguishes itself from a purchase and a guaranteed quote, and the two-step nature is made clear from the start, which separates it from sibling tools like enquiry_describe and enquiry_fields.

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 an explicit two-step usage protocol: first call validates and returns a summary and confirmation token; second call is only made after the person agrees, reusing the token. It also clarifies that the enquiry is not visible to providers until the emailed link is clicked, giving clear guidance on when and how to proceed.

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