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

No annotations are supplied, so the description carries the disclosure burden. It does describe the shape of the return (columns, numeric flags, row count, provenance banner), which implicitly signals a harmless read, but it never states that the call is side-effect free, cheap, or that it takes no arguments. Adequate but thin for a tool with zero annotation coverage.

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 short sentences, front-loaded with what is returned and closed with the action guidance. No filler and nothing that could be cut without losing meaning.

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

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

With no output schema, no annotations, and no parameters, the description is the agent's only information source, and it does name the returned fields plus the recommended call order. It leaves the boundary with dataset_provenance and dataset_stats unexplained, which is the remaining 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 tool takes zero parameters, which is the baseline-4 case per the rubric. The description's enumeration of returned fields is helpful orientation but is not required to compensate for any schema 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 names a specific resource (the Yacht Charter Quotes dataset) and enumerates what it returns: columns, which are numeric, row count, and the provenance banner. That is a clear, non-tautological purpose. It stops short of 5 because it never distinguishes itself from siblings like dataset_provenance (also provenance) or dataset_stats (also row counts), so the agent must infer the boundary.

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?

"Call this first to learn the schema" is explicit sequencing guidance that tells the agent when to reach for this tool relative to the exploration workflow. It gives no exclusions or named alternatives, so it sits just below the top band.

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 exist, so the description carries the full behavioral burden. It discloses one behavioral trait — output rows follow the input value order — but says nothing about read-only safety, result size limits (the schema's 10-value cap implies caps on returned rows), or output format.

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 with no filler, so length is appropriate. But it opens with a relative clause ('The rows of the ... dataset whose column ...') rather than front-loading the action or the use case, making it slightly harder to parse on first read.

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

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

With no annotations, no output schema, 0% parameter coverage, no enums, and a sibling (dataset_columns) that presumably enumerates legal columns, the description is too thin. It never explains what 'compare' yields (full rows? side-by-side pairing?), how more than two values behave, or what column names are valid.

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 0% and the schema gives only types, so the description must compensate. It usefully clarifies that 'column' is the matching field and 'values' is an any-of set whose order drives output order, which is real meaning beyond the schema; it still omits valid column names, the 2–10 value bounds, and value format.

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 specifies the resource (rows of the Yacht Charter Quotes dataset) and the selection rule (column equals any given value, order preserved), plus the intended question type ('X vs Y'). However, it is a noun phrase with no action verb, and the title's claim of 'compare rows side by side' is never reconciled with the description's filter-and-return semantics, leaving the actual operation somewhat inferred.

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?

"for "X vs Y" questions" gives one concrete usage condition, which implies when this beats dataset_search or dataset_row. But no alternative sibling is named and no when-not guidance is offered, so an agent must infer the boundary itself.

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 supplied, so the description carries the full burden. It implies a safe read by describing static metadata, but never states the tool is read-only, whether the provenance reflects the live or a cached snapshot, or whether any prerequisite (e.g. dataset access) applies. Adequate but 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?

Two short sentences with no filler: the returned content is front-loaded in the first sentence and the usage cue follows. 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?

Because there is no output schema, the description must describe the return surface, and it does list the four things the agent will get. It leaves minor ambiguity about the exact format/field names returned, but the essentials for correct invocation are 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 tool takes zero parameters, so by the rubric the baseline is 4. Nothing in the description contradicts or confuses the empty input 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 enumerates exactly what the tool returns (source, computation date, licence, citation) for a named dataset, which is far more specific than a restatement of the name. It implicitly differentiates from siblings like dataset_stats or dataset_columns by being the only metadata/attribution tool, though it never explicitly contrasts with them.

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?

"Read this to attribute a figure correctly" gives a concrete triggering condition for calling the tool. It stops short of naming alternatives or exclusions, so the agent still has to infer there is no overlap with the other dataset_* 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?

With no annotations, the description carries the full behavioral burden. It does disclose two meaningful traits — matching is exact and case-insensitive, and results are plural rows — but says nothing about result count limits, ordering, pagination, or what happens when no row matches.

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 front-loaded sentence with no filler; the dataset scope and the match semantics both land immediately.

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 2-parameter lookup with no output schema, the description covers what the tool returns (rows), but leaves out how many rows come back, their ordering, and whether an unknown column name is an error — details an agent would want before calling 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 0% for the 2 required parameters, so the description must compensate. It maps the parameters semantically (column = which column, value = the exact value to match, matched case-insensitively) but does not say that 'column' must be an existing column name (the dataset_columns sibling) or give value format hints.

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 (look up rows), the resource (rows of the Yacht Charter Quotes dataset), and the precise predicate (column equals value exactly, case-insensitive). It is clearly distinct from a stats/top/columns tool, though it never names the sibling it is closest to (dataset_search) to sharpen the boundary.

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 only implied: 'where a column equals a value exactly' signals this is for exact-match retrieval rather than the search/compare siblings, but there is no explicit when-to-use, when-not-to-use, or named alternative. The agent must infer the routing from the word 'exactly'.

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 disclosure burden. It does add real behavioral facts — matching is case-insensitive and results are capped at 50 — but it says nothing about what happens when more than 50 rows match (truncation vs error), ordering of results, or permissions.

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 front-loaded sentence with no filler; the matching rule and the cap are delivered together without 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 simple two-parameter read tool with no output schema and no annotations, the description covers what is returned (dataset rows), how matching works, and the result ceiling. Minor omissions remain around ordering and behavior beyond 50 matches.

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%: 'query' is documented in the schema, 'limit' is not. The description partially compensates by noting the 50-row cap and the case-insensitive cell matching, which clarifies both parameters, but it doesn't state whether limit defaults or how it interacts with the cap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Rows of the Yacht Charter Quotes dataset whose cells contain the query'), plus matching semantics (case-insensitive) and a result cap. It is clear what the tool does, but it never contrasts itself with lookalike siblings such as dataset_row or dataset_top.

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 when-to-use statement, no prerequisites, and no named alternative among the many sibling tools. The agent must infer that this is the free-text lookup tool from the phrase 'cells contain the query' alone.

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, and it does disclose useful preprocessing behavior: commas/currency are normalized and non-numeric rows are excluded and counted. It does not say what happens when the column is entirely non-numeric (error vs empty result), nor how nulls are treated, leaving meaningful gaps for a read-only analytics 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?

A single well-formed sentence that front-loads the returned metrics and tucks the parsing/exclusion caveats into one compact parenthetical. No redundant padding, though the parenthetical could be split for readability.

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 reasonably takes on the return-value burden by listing the six metrics produced. It is still incomplete for a no-annotation, zero-coverage-schema tool: error behavior for non-numeric columns and null handling are unstated.

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% and the single 'column' parameter has no description in the schema. The description partially compensates by specifying that the column belongs to the Yacht Charter Quotes dataset and must be numeric, but it does not clarify name format, case sensitivity, or whether a dataset qualifier 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?

Names a concrete operation (compute summary statistics) on a specific resource (a numeric column of the Yacht Charter Quotes dataset) and enumerates the returned metrics. It is distinguishable from siblings like dataset_columns or dataset_top, though it never explicitly contrasts itself with them.

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 by the constraint that the column must be numeric, which steers the agent toward numeric columns only. However, there is no explicit when-to-use guidance and no named alternative among the many dataset_* siblings.

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 and does not meet it. It never says whether full rows or just the column value are returned, what the default limit is, or what happens if a non-numeric column is supplied.

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, front-loaded sentence with no padding, and the 'most/least' example phrasing is compact and useful. It is efficient though it wastes no space on the parameters that actually need explaining.

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 three-parameter ranking tool the description is roughly adequate, but with no output schema and no annotations it should say what a result row looks like and whether a default limit applies. Those gaps leave the agent guessing about the return shape.

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 33% and only 'ascending' is documented in the schema, so the description must compensate. It partially does — 'numeric column' tells the agent the column must be numeric and 'highest (or lowest)' reinforces the ascending flag — but the 1-50 'limit' parameter is never addressed.

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 operation (rank/select highest or lowest rows) on a named resource (Yacht Charter Quotes dataset) keyed by a numeric column, which is clear enough to distinguish it from dataset_stats or dataset_search. It does not explicitly contrast itself with the closest sibling (dataset_compare), so it falls just short of a 5.

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 quoted framing 'which is the most/least X' implies the intended use case, so an agent can infer when to reach for it. However, there is no explicit when-not guidance or mention of the alternative sibling tools for ordering/comparison tasks.

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. It discloses domain facts (free, no purchase, no guaranteed quote) and what it returns, but does not explicitly state that the tool itself is read-only, has no side effects, or requires no special permissions. Tool-specific behavioral traits are left to inference.

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 front-loaded with 'Read first' and then efficiently covers the purpose and return values in a few sentences. It is mostly concise, though the phrasing 'States plainly what submit_enquiry does' is slightly meta and could be more direct.

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 no output schema and no parameters, the description adequately explains what is returned (who receives details, consent wording, confirmation method). It could additionally note that the tool is static or requires no input, but it is largely complete for a simple informational 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 tool takes zero parameters, so there is nothing for the description to clarify beyond the schema. Baseline for zero parameters is 4.

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 verb (states/returns) and resource (what submit_enquiry does on Yacht Charter Quotes, plus consent details). It distinguishes itself from submit_enquiry by being the informational tool, but does not mention the sibling enquiry_fields, so differentiation is incomplete.

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 only usage guidance is the front-loaded 'Read first,' implying it should be used before submit_enquiry. It does not explicitly state when to use this over enquiry_fields or submit_enquiry itself, and offers no exclusions or 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?

With no annotations, the description carries the disclosure burden, and it usefully describes the returned shape (key, label, type, required, help text, options) even though no output schema exists. It does not state that the call is a side-effect-free read, nor mention permissions, rate limits, or whether the field set is static or context-dependent.

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 defines the payload, the second tells the agent how to consume it. The most important information (what the fields are) 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 zero-parameter introspection tool with no output schema, the description supplies the return fields and the downstream usage, which covers what an agent needs to call it correctly. The only shortfall is failing to differentiate it from the sibling enquiry_describe.

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 takes zero parameters, so 4 is the baseline and there is nothing to document. The mention of 'keyed by field key' usefully ties the output keys to the input of submit_enquiry, adding a small amount of cross-tool semantics.

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 (every field of the Yacht Charter Quotes enquiry) and enumerates exactly what each entry contains (key, label, type, required, help text, options). That is concrete enough to separate it from enquiry_describe and submit_enquiry, though it never states the verb ('returns'/'lists') explicitly.

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 implies the workflow by saying answers go to submit_enquiry keyed by field key, which hints this is an introspection step that precedes submission. However it never says when to use this versus the similarly named sibling enquiry_describe, so the agent must infer the 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 full burden and does so: it discloses that data is shared with yacht charter companies and brokers, that an email confirmation link must be clicked before any provider sees the enquiry, that step 1 validates and returns a summary plus token, and it quotes the exact consent text. Side effects, gating, and the two-call contract are all explicit.

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?

Purpose and the NOT-a-purchase disambiguation are front-loaded, then the two steps follow in order with no filler sentences. The only redundancy is the consent sentence, which is repeated verbatim in the schema's `consent` description; otherwise the length is justified by the two-step contract.

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?

No output schema and no annotations exist, so the description must cover return values and side effects — and it does: it names the summary, consent line and confirmation token returned in step 1, and the email-verification flow after step 2. Nothing an agent needs to invoke this correctly 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: `answers` is keyed by field keys coming from the sibling tool enquiry_fields, `consent` is tied to a specific quoted agreement, and `confirmation` is identified as the step-1 token that must be replayed in step 2. This links parameters to a workflow the schema alone does not express.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (submit an enquiry to Yacht Charter Quotes) and immediately scopes it with two negations — NOT a purchase, NOT a guaranteed quote — which separates it from any booking/purchase sibling. The two-step framing tells the agent exactly what kind of operation this is before it reads the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit ordered protocol: Step 1 call with answers + consent=true, show the person the summary and consent line; Step 2 call again with the same answers, consent=true and the confirmation token, and only if the person agrees. The precondition for step 2 and the consent gate are stated, leaving nothing to inference.

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