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

No annotations are provided, so the description carries the full behavioral burden. It usefully enumerates the returned content (columns, numeric flags, row count, provenance), but never states that the operation is read-only, that it has no side effects, or whether the dataset is fixed or dynamic. Adequate but leaving safety/behavior context 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?

Two sentences, zero filler. The return contents come first and the usage instruction ('Call this first') comes last, which is the right front-loading for a discovery tool.

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 and no annotations, the description must convey both purpose and returns, and it does list the returned fields. It is close to complete for a zero-parameter introspection tool, missing only confirmation of read-only behavior.

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, and the schema is an empty object, so there is nothing to disambiguate. Baseline 4 applies; the description correctly implies no arguments are needed by framing the dataset as fixed.

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 exactly what the tool returns: column names, which are numeric, row count, and the provenance banner, scoped to the Med Spa Cost Checker dataset. That is far more specific than the title alone, though it does not say how it differs from the sibling dataset_provenance, which the mention of a 'provenance banner' could plausibly overlap with.

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 gives an explicit sequencing instruction: 'Call this first to learn the schema.' That is clear guidance on when to use it relative to the other dataset_* tools, but it names no exclusion or alternative for cases where the agent already knows the schema.

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 the non-obvious ordering behavior ('in the order given') and that it returns dataset rows. It stays silent on whether the call is read-only, what happens when a value matches nothing, and whether there is a result cap, which matters given the schema's maxItems of 10.

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?

Everything is packed into one front-loaded sentence with no filler, and the ordering constraint arrives before the use-case tag. The phrasing 'whose column is any of the given values' is slightly convoluted but not wasteful.

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 read tool with no output schema, the description covers the core contract but omits return shape details (which columns come back, one row per value or many) and the no-match/no-annotation safety profile. It is adequate to call the tool, but an agent must infer the edge cases.

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%, so the description must compensate; it does map both parameters to meaning ('column' to filter on, 'values' as the match set), which is more than the bare schema offers. It does not explain the 2-10 value bounds, the string-only value type, or how multiple matched values order the resulting rows.

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 concrete resource (rows of the Med Spa Cost Checker dataset) and the selection rule (column matches any given values, order preserved), which is a real verb+resource statement rather than a restatement of the name. It distinguishes itself functionally from a plain search by emphasizing ordering and the "X vs Y" comparison use case, but it never names a sibling tool to contrast with.

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 through the trailing clause 'for "X vs Y" questions', which hints at when the tool is appropriate. There is no statement of when NOT to use it, no prerequisites, and no named alternative (e.g., dataset_search or dataset_row) for retrieving rows that are not being compared.

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 disclosure burden. It states this is a read-oriented lookup and, importantly, lists the four kinds of information returned, which stands in for an absent output schema; nothing indicates mutation risk. Only the response format is left unspecified.

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 tight sentences: the first front-loads the returned metadata, the second front-loads the reason to call it. No filler or restatement of the name.

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, schema-less, output-schema-less read tool, the description supplies enough — it effectively documents the return payload. Only the concrete shape/format of the response is unstated, which is a minor 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, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. No misleading parameter hints are present.

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 Med Spa Cost Checker dataset) and enumerates exactly what it yields: source, computation date, licence, and citation. Siblings such as dataset_stats or dataset_columns are implicitly distinguished by the metadata/citation framing, though no sibling is named outright.

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 clear, concrete condition for invoking the tool. It does not spell out exclusions or point at an alternative, but the trigger context is unambiguous for a provenance lookup.

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 useful behavior in the case-insensitive exact match and the plural 'rows' return, but does not state read-only safety, output shape, pagination, 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 wasted words. It communicates the core behavior efficiently and is appropriately sized for a simple lookup tool.

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, the description gives enough to call the tool, but it omits how to discover valid column names, what the returned rows contain, and any output/pagination behavior since there is no output 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 description coverage is 0%, so the description must compensate. It maps the two parameters semantically ('column equals a value') and adds case-insensitive exact-matching behavior, but it does not explain valid column names, value formats, or the result set.

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 (lookup rows where a column equals a value exactly) on a named resource (the Med Spa Cost Checker dataset). The word 'exactly' distinguishes it from a fuzzy search sibling like dataset_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 exact-match condition implies when to use the tool, but the description never explicitly names alternatives like dataset_search or dataset_top, nor does it state when this tool should not 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, the description must carry the behavioral burden. It usefully discloses case-insensitive matching and the 50-row result cap, but says nothing about ordering, whether results can be paged past 50, or performance on large datasets.

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 resource, matching rule, and cap are all stated compactly.

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, so the description is the only source of truth, yet it never describes the shape of a returned row or the ordering of results. Adequate for basic invocation but leaves an agent guessing at the return payload.

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 documented, limit undocumented beyond its maximum). The description adds that the query matches any cell case-insensitively and that results cap at 50, which aligns with and slightly extends the schema, but it never states the limit's default or behavior when omitted.

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 — returning rows of the named 'Med Spa Cost Checker' dataset that match a query, with case-insensitive substring semantics. That is clear, but it offers no differentiation from many sibling tools (dataset_row, dataset_top, dataset_columns) that an agent must choose between.

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 explains the matching mechanism but never says when to reach for this tool instead of dataset_row, dataset_top, dataset_stats, or dataset_columns. No prerequisites, exclusions, or alternative routing are provided.

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, and it does disclose real data-handling behavior: grouping commas and currency are handled and non-numeric rows are excluded and counted. That tells the agent how dirty input is treated, which is more than a bare 'compute statistics' statement. It still omits edge cases (empty or all-non-numeric column) and permission/error 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?

A single front-loaded sentence that leads with the exact statistics returned and packs the parsing caveats into a trailing parenthetical. Dense but every clause earns its place; no 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 one-parameter read tool with no output schema and no annotations, the description supplies both the return contents (the six statistics) and the input-cleaning rules. The remaining gap is behavior on degenerate input and how to discover valid column names, which are minor against the tool's simplicity.

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 schema only says 'column: string, minLength 1'. The description compensates partially by constraining the argument to a numeric column of a specific dataset, but it does not explain naming conventions or how a caller finds a valid column name (e.g., via dataset_columns).

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+resource: it computes a named set of summary statistics (count, min, max, mean, median, sum) for a numeric column of the Med Spa Cost Checker dataset. The scope is precise enough that an agent can distinguish it from dataset_top or dataset_compare, though no sibling is named 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?

The phrase 'of a numeric column' implies the precondition for use (the column must be numeric) and the dataset scope, which is a real usage constraint. However, it never says when to prefer this over dataset_top, dataset_compare, or dataset_search, so the alternative-selection guidance is left 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?

No annotations are provided, so the description carries the full behavioral burden. It implies a read operation but never states that it is non-destructive, says nothing about the 50-row cap, tie handling, or what the response contains. Most of the behavioral surface is left undocumented.

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 ranking intent and the natural-language question it answers are stated up front. The em-dash aside is slightly awkward but costs little.

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, and only a third of parameters documented, the description should do more work. It never explains what a 'row' looks like on return, whether all columns or only the ranked column come back, or how 'limit' behaves, leaving an agent to guess at invocation and output 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 description coverage is only 33% (only 'ascending' documented), so the description should compensate more than it does. It does add real meaning for 'column' (must be numeric) and restates the highest/lowest polarity that maps to 'ascending', but the 'limit' parameter (range 1-50) is never mentioned or explained.

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 concrete operation (return the highest/lowest rows ranked by a numeric column) on a named resource (the Med Spa Cost Checker dataset), which is more specific than 'rank rows' alone. It does not, however, differentiate itself from plausible siblings like dataset_stats, dataset_search, 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 phrase 'which is the most/least X' gives an implied trigger question, so an agent can infer when to reach for this tool. There is no explicit when-not guidance, no mention of prerequisites such as the column needing to be numeric-enforced, and no named alternative (e.g. dataset_stats for aggregate extrema).

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 does disclose substantive domain behavior: nothing is bought/ordered/paid, no quote is guaranteed, it is free, and it returns consent wording and confirmation flow. However, it never states the tool's own operational profile (read-only, side-effect-free, safe to call repeatedly) or any auth requirements, leaving those 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?

Three sentences, front-loaded with the actionable 'Read first' directive, and each sentence adds a distinct fact (what the enquiry is, what it is not, what it returns). The colon-list construction is slightly dense but not wasteful.

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, no-output-schema informational tool, the description covers purpose, sequencing, non-committal guarantees, and return content, which is close to sufficient. The remaining gap is the tool's own safety/operational nature, which no annotation supplies.

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 the baseline is 4 and there is nothing for the description to compensate for.

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 conveys that this is a pre-read informational tool that explains the enquiry process and returns consent/recipient/confirmation details, and it names the sibling submit_enquiry it relates to. It is slightly muddied because the opening clause ('States plainly what submit_enquiry does') narrates the sibling's behavior rather than directly stating the tool's own action, so the subject of the sentence must be 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?

'Read first' gives an explicit sequencing instruction that places this tool before submit_enquiry, which is clear context for an agent. It does not name an alternative tool or state exclusions, so it stops short of the 5-level when/when-not/alternatives treatment.

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 and no output schema, the description carries the full disclosure burden and does describe the returned record shape (key, label, type, required, help text, options). It never explicitly states the call is read-only or side-effect free, but the enumeration of returned metadata makes the read-only nature unambiguous.

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, both earned: the first defines the payload, the second states the downstream contract. Front-loaded with the resource and the return contents, with no 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 zero-parameter, no-output-schema metadata tool, the description covers the resource, the returned field set, and how to use it. Nothing an agent needs to call it 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?

The tool takes zero parameters, so the baseline of 4 applies. The description adds relevant meaning by explaining that the returned keys are the exact keys expected by submit_enquiry, tying the output to a parameter contract elsewhere.

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 Med Spa Cost Checker enquiry) and enumerates exactly what it returns: key, label, type, required flag, help text and allowed options. It clearly identifies this as the field-metadata source and links it to submit_enquiry, though it never distinguishes itself from the sibling 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?

It gives concrete downstream usage — 'Pass answers to submit_enquiry keyed by field key' — which tells the agent both why to call this and how to consume the result. It does not, however, state when to prefer it over enquiry_describe or any 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 full burden and does so: it discloses the two-step protocol, that Step 1 only validates and returns a summary/consent line/token, that Step 2 performs the actual submission, that consent gates data sharing, and that a provider never sees the enquiry until a link is clicked. This is unusually rich behavioral disclosure for a mutation 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?

Front-loaded with the disambiguating 'NOT a purchase' clause and cleanly split into Step 1/Step 2. It is somewhat long because the full consent sentence is quoted verbatim, which is defensible for legal wording but is still duplication of the schema's consent 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?

No output schema exists, yet the description explains exactly what Step 1 returns (summary, consent line, confirmation token) and what the end user receives (an email with a click-through link). Combined with a 3-param nested schema that is fully documented, nothing an agent needs to call 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 beyond the schema: it ties 'answers' to field keys from the enquiry_fields sibling, and clarifies that 'confirmation' is only supplied in Step 2 after approval. The conditional ordering of parameters is not expressible in the schema, so this exceeds baseline.

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 ('Submits an enquiry to Med Spa Cost Checker') and immediately distinguishes it from adjacent concepts with 'NOT a purchase, NOT a guaranteed quote'. An agent can separate it from the dataset_* and enquiry_* siblings without opening any 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 explicit conditional routing: call Step 1 with answers and consent=true, show the person the summary and consent line, then call Step 2 only if the person agrees. It also names the gating condition (email link must be clicked before any provider sees it), 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.