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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 burden. It discloses the return shape (columns, numeric-ness, row count, provenance banner), which implies a safe read of dataset metadata, but never states that it is read-only or that it makes no changes. Adequate but not rich behavioral context.

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

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

Two sentences, both front-loaded with the most important content (what it returns, then when to call it). Zero filler, though the phrase "which of them are numeric" is slightly awkward.

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

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

With no output schema, the description must convey the return values, and it does enumerate them concretely. For a zero-parameter schema-inspection tool this is complete enough to call correctly.

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

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

The tool takes zero parameters, which sets the baseline at 4 per the rubric. There is nothing further for the description to clarify about inputs.

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 the specific resource (the Wedding Cost Checker dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and the provenance banner. It is distinguishable from dataset_stats or dataset_row by its schema-inspection role, though it does not explicitly name a sibling to contrast against.

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" gives an explicit ordering instruction that routes the agent to this tool before others. It stops short of naming conditions or alternatives (e.g., vs. dataset_provenance), so it is clear context without 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 behavioral burden. It discloses row filtering and ordering behavior, which is useful beyond the schema, but omits read-only safety, matching semantics, limits, and return 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?

The single sentence is front-loaded and efficient, covering purpose, output ordering, and usage context without waste. It is appropriately sized, though slightly terse.

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 filter tool with no output schema or annotations, the description is minimally adequate: it explains what rows are returned and in what order. However, it lacks details on matching semantics, value limits, and return format that would make it fully complete.

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

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

Schema description coverage is 0%, so the description must compensate. It names both required parameters implicitly ('column', 'values') and adds the ordering constraint, but does not explain value formats, matching rules, or the 2–10 item bounds.

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 resource (rows of the Wedding Cost Checker dataset) and the filtering/ordering behavior (rows whose column is any of the given values, in the order given). It does not explicitly differentiate itself from siblings like dataset_search or dataset_row, so it falls 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?

It provides clear context for when to use the tool: 'for X vs Y questions.' However, it does not name alternatives or state when not to use it, which prevents a higher score.

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 supplied, the description carries the full behavioral burden, and it does disclose the four content categories returned (source, date, licence, citation). The read-only nature is implicit in a zero-parameter metadata lookup, but the description says nothing about return format or whether the dataset identifier is fixed.

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 the returned content and closed with the actionable use case. No filler and nothing that could be trimmed.

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?

Absent an output schema, the description reasonably compensates by listing the fields a caller receives, plus the purpose for reading them. It could be slightly more complete by hinting at the response shape or whether the date is ISO-formatted, but for a trivial zero-param metadata tool this is nearly sufficient.

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 of 4 applies. The description correctly implies no input is needed, mentioning only the dataset it operates on.

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 dataset's provenance metadata) and enumerates exactly what it returns: source, computation date, licence, and citation. This is clearly distinguishable from the sibling tools, which deal with columns, rows, stats, and search rather than attribution metadata.

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 using the tool. It stops short of naming alternatives or stating when not to bother (e.g., when no citation is needed), so it falls just short of fully explicit routing.

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 disclosure burden. It does contribute one genuine behavioral detail the schema cannot express: matching is case-insensitive and exact. It says nothing about permissions, result limits/pagination, or behavior when no row matches, which for an unannotated tool is a notable gap but not a fatal one given the read-only nature implied by 'look a row up'.

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 wasted words, and the key matching constraint (exact, case-insensitive) is placed at the end where it reads as a qualifier. Slightly awkward nominal phrasing ('The rows of the ... dataset where ...') but tight overall.

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 exact-lookup tool with no output schema and no annotations, the description covers what it returns (rows) and the matching rule, which is most of what an agent needs. It omits no-match behavior and return formatting, leaving the definition adequate but not complete.

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

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

Schema description coverage is 0% and the two params are documented only by name and type. The description compensates meaningfully by mapping them to semantics: 'column equals a value', and by adding the case-insensitive comparison rule. It stops short of giving column-name format or valid column examples.

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 concrete operation (retrieve rows where a column exactly equals a value) on a named resource (the Wedding Cost Checker dataset). The 'exactly' qualifier implicitly separates it from the fuzzier dataset_search sibling, though it never names that alternative.

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

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

The word 'exactly' (with case-insensitivity) implies this is the exact-match lookup as opposed to a search-style sibling, so usage is inferable. However, there is no explicit when-to-use/when-not statement or named alternative, so guidance remains implied.

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 real traits: matching is case-insensitive substring-anywhere, results are capped at 50 rows, and the scope is fixed to one named dataset. It omits ordering, whether the limit is silently applied or assumed, and what happens on no matches, so it is partially transparent but not complete.

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, front-loaded with the resource and match condition, ending with the size constraint. No filler words and nothing redundant with the tool 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?

There is no output schema and no annotations, so the description is the only source of behavior. It conveys the shape of results loosely ('rows ... up to 50') but says nothing about returned fields, ordering, or behavior with an empty result set. Adequate to invoke, thin for interpreting the response.

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 as 'text to look for in any cell' while 'limit' carries only bounds with no description. The description partially compensates by restating the 50-row ceiling, which maps to the limit maximum, but it never explains that limit controls result count. Baseline 3 is appropriate given the schema does roughly half the work.

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 gives a specific verb (search/rows matched by content) tied to a named resource and scope: 'Rows of the Wedding Cost Checker dataset whose cells contain the query'. It also states the matching semantics (case-insensitive, any cell) and the cap (up to 50), so an agent knows this is a full-text scan rather than a field lookup. However, it never distinguishes itself from siblings like dataset_row, dataset_top, or dataset_compare, 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?

There is no explicit when-to-use statement, no when-not, and no mention of the sibling alternatives (dataset_row for a single record, dataset_top for ranked results, dataset_columns for schema). Usage is only implicitly derivable from the phrasing 'cells contain the query'. An agent must guess which of the six dataset_* tools is appropriate.

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

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

With no annotations, the description carries the full burden, but it does disclose real behavioral traits: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. It leaves the return format, the handling of an all-non-numeric or nonexistent column, and the destination of the 'counted' non-numeric rows unexplained.

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 that front-loads the returned metrics before the parenthetical data-handling caveats. Dense but every clause carries information; 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-only aggregation tool with no output schema, the description covers the key facts: which metrics are returned and how dirty data is treated. Minor gaps remain around output shape and failure modes, but nothing essential for correct invocation 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 description coverage is 0% for the single 'column' parameter, so the description must compensate. It does add meaning by specifying the column must be numeric and that currency/comma formatting is tolerated, but it doesn't clarify name matching, case sensitivity, or error behavior for an invalid column.

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 (compute count/min/max/mean/median/sum) on a specific resource (a numeric column of the Wedding Cost Checker dataset), so the agent knows exactly what it will get. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, but the enumerated metrics make its niche reasonably distinct.

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?

There is no statement of when to use this tool versus alternatives such as dataset_top, dataset_compare, or dataset_search, and no prerequisites or exclusions. Usage is only implied by the metric list.

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

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

No annotations are provided, so the description carries the full burden. It implies a read-only ranking operation but discloses nothing about return format, row cap behavior, ordering ties, or dataset assumptions beyond the numeric-column hint, leaving meaningful behavioral gaps.

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 compact sentence with the ranking intent front-loaded and no filler. It is efficient, though the parenthetical aside adds modest 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 three-parameter read tool with no annotations and no output schema, the description establishes the core operation but does not describe the shape of the returned rows or the limit semantics. It is minimally adequate rather than complete.

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

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

Schema description coverage is only 33% (only 'ascending' is documented), but the description adds the non-obvious constraint that the column must be numeric and that results are highest-first by default. It says nothing about the limit parameter or its 50-row ceiling, which the schema documents structurally.

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: returning the highest/lowest rows of the Wedding Cost Checker dataset ranked by a numeric column. This clearly separates it from dataset_row, dataset_stats, and dataset_search, though it leaves the phrase 'top' to be inferred from the title rather than restating it in the body.

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 analytical question the tool answers, giving implied usage context. However, it never states when to prefer this over dataset_stats or dataset_search, nor any exclusion or prerequisite, so guidance remains 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, so the description carries the full disclosure burden. It discloses the subject matter of what comes back (who receives details, consent wording, confirmation method), which is the most important behavioral context for a zero-parameter describe tool, but it never explicitly states that enquiry_describe itself is a safe, side-effect-free read with no mutation of any enquiry state. Most of the text describes submit_enquiry's behavior rather than this tool's own semantics.

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 short sentences, front-loaded with the 'Read first' directive. The 'not a purchase, not a guaranteed quote' negatives largely duplicate the title rather than adding new information, which is minor redundancy but not bloat.

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 no parameters, the description is the only source of information, and it does cover what the tool returns (recipients, consent wording, confirmation method) as well as what it does not do. The main residual gap is the absence of any explicit statement about the tool's own side-effect-free nature, though that is implied by '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 input schema has zero properties and 100% coverage by definition, so there are no parameters to document. The baseline of 4 applies: nothing in the description is needed to compensate for undocumented inputs.

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 that this tool explains what submit_enquiry does on the Wedding Cost Checker, i.e., it is a documentation/describe tool for the enquiry flow, and 'Read first' signals its role. It distinguishes the subject from a purchase or guaranteed quote, and the title reinforces this boundary. It never states outright 'this tool returns descriptive info about the enquiry process' in those words, leaving a small inference step, but the intent is discoverable.

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' is an explicit ordering instruction relative to submit_enquiry, giving the agent a clear when-to-use cue. It doesn't name enquiry_fields as an alternative or spell out when-not to call it, but the sequencing guidance is concrete and actionable.

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 disclosure burden, and it does state what the call returns and how the values are to be reused downstream. It never implies mutation or side effects, which is consistent with a schema-introspection tool. It stops short of 5 because it says nothing about ordering, pagination, or failure modes.

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 load-bearing: the first enumerates the returned field attributes, the second routes the result into submit_enquiry. No filler, and the resource being described 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?

With no output schema, no annotations, and no parameters, the description must explain the return shape, and it does list the per-field attributes returned. A consumer can call it and know how to feed the result forward, though the absence of any mention of typical option counts, ordering, or error behavior 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 the baseline is 4; there is nothing for the description to disambiguate on the input side. It appropriately spends its words on output shape instead of inventing parameter guidance.

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 Wedding Cost Checker enquiry' — and enumerates exactly what each field entry contains (key, label, type, required, help text, options). That is far more precise than a tautology. However, it does not differentiate itself from the sibling enquiry_describe, whose role is presumably adjacent, leaving some ambiguity an agent must resolve on its own.

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 a clear consumption path: 'Pass answers to submit_enquiry keyed by field key', which tells the agent this tool feeds submit_enquiry. There is no explicit statement of when not to use it or how it differs from enquiry_describe, so it stops short of full when/when-not 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?

No annotations are provided, so the description carries the full burden and does so: it discloses validation behavior, the returned artifacts (summary, consent line, confirmation token), the human-in-the-loop requirement, the double opt-in email link, and the critical downstream effect that details are shared with suppliers who may contact the person.

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 crucial disambiguation ('NOT a purchase') and every sentence carries operational information, but the consent sentence is quoted verbatim in the description and then again in the schema's consent property, which is a small amount of redundancy for a single dense paragraph.

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

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

With no output schema, the description compensates by describing exactly what step 1 returns and what step 2 produces, including the email confirmation step. Given the nested answers object and the two-phase flow, 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; the description still adds sequencing meaning the schema cannot express — that the confirmation token comes only from step 1 and is only valid after the person approves the summary, and that answers are keyed to enquiry_fields field keys.

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 Wedding Cost Checker') and immediately rules out what it is not ('NOT a purchase, NOT a guaranteed quote'). It also anchors itself relative to siblings by referencing field keys from enquiry_fields, so an agent can separate it from enquiry_fields/enquiry_describe without reading schemas.

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

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

Gives an explicit two-step protocol: step 1 validates with answers+consent=true, step 2 re-calls with the confirmation token only after the person approves. The gating condition (person must agree) and the intermediating action (show the summary and consent line) are spelled out rather than left to inference.

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