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

With no annotations and no output schema, the description carries the burden and largely meets it by enumerating the returned fields (columns, numeric classification, row count, provenance banner). It is implicitly a read-only inspection call with no parameters, so there is little risk surface left undisclosed, though permissions or caching behavior are unmentioned.

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 delivers the full return inventory, the second delivers the usage cue. Every clause earns its place and the most actionable guidance (call first) is not buried.

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 no output schema exists, the description must convey return contents, and it does so at field granularity. What it omits is the shape/format of that output (e.g., ordering, how 'numeric' is flagged), which is a minor gap for a zero-parameter introspection tool.

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

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

The input schema has zero parameters, so there is nothing for the description to disambiguate; baseline is 4. No misleading parameter information is 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 Answering Service Quotes dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and provenance banner. It is distinguishable from most siblings, though the overlap with dataset_provenance (banner) and dataset_stats (row count) is not explicitly disambiguated.

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 explicit sequencing guidance that tells the agent when to reach for this tool ahead of the others. It does not, however, name which sibling to use afterward or what to do if the schema is already known.

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 two real traits: the operation is scoped to one named dataset and result order follows the order of the supplied values. It still omits read-only/mutation status, return shape, and whether changes are possible, which keeps it below a 4.

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 tight sentence with no filler, front-loading the dataset and the filter condition. The grammar is slightly awkward (a verbless noun phrase), which keeps it from a 5.

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, 0%-coverage tool with no output schema, the description covers the filter logic adequately but never states what is actually returned (row objects? which fields? ordering of columns?), leaving a meaningful gap for an agent trying to interpret 'compare' results.

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 explain the core semantics — 'column' designates the field, 'values' is matched with any-of (OR) logic, and ordering is significant — but it omits constraints present in the schema (2-10 values, non-empty strings) and says nothing about column naming format or matching case sensitivity.

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

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

The description names the specific dataset (Answering Service Quotes) and the filtering operation (rows whose column matches any given value), which is a concrete resource + operation. It does not differentiate itself from siblings such as 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?

The trailing clause 'for "X vs Y" questions' implies the intended scenario, giving the agent a usable usage cue. However, there is no explicit when-not guidance and no pointer to alternative tools (dataset_search, dataset_top) that might also retrieve rows.

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 discloses the returned fields and implies a read-only operation via 'Read this,' but does not state permissions, side effects, or output structure beyond the listed fields.

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

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

The description is two compact sentences with no filler. It front-loads the returned content and then states the intended use, making it easy to scan.

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

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

For a zero-parameter, read-only provenance lookup with no output schema, the description is complete enough: it explains both what is returned and why an agent would call it. 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?

There are zero parameters, so there is nothing for the description to clarify about argument semantics. The baseline score for a no-parameter tool 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 names the exact metadata fields returned (source, computation date, licence, citation) for a specific dataset, making the resource unambiguous. It also distinguishes this from data-retrieval siblings by framing the purpose as attribution rather than querying rows, columns, or statistics.

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 use case: read this to attribute a figure correctly. It does not explicitly name alternatives or when not to use it, but the context is specific enough for an agent to know this is the citation/provenance tool rather than the data-access tool.

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

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that matching is exact and case-insensitive, but says nothing about what happens when no row matches, whether all matches are returned or only the first, result ordering, or whether the column name must come from dataset_columns. For a lookup tool with zero annotation coverage, key behavioral questions are unanswered.

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 tight sentence with no filler, and the dataset scope plus match rule are front-loaded. The phrasing opens with a noun phrase ('The rows of...') rather than the operation verb, which slightly slows parsing, but nothing is wasted.

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 stand alone; it covers what is selected but not the shape or size of the response, nor how to discover valid column names. For a two-parameter read tool this 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 0% for both parameters, so the description must compensate and it partially does: it establishes that one parameter names a column and the other is the value compared against it, and that comparison is exact and case-insensitive. It does not clarify that column must be a valid dataset column name or that value is a string with a minimum length of 1.

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

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

The description states the specific resource (rows of the Answering Service Quotes dataset) and the exact selection rule (a column equals a value, case-insensitive), so an agent can tell it retrieves matching rows rather than aggregating or listing schema. It does not name any sibling, though the word 'exactly' implicitly contrasts with dataset_search. Clear purpose, but no explicit sibling differentiation.

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

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

There is no explicit when-to-use or when-not-to-use statement and no alternative is named. However, 'equals a value exactly (case-insensitive)' implies this is the exact-match lookup path as opposed to dataset_search's fuzzy matching, which is enough for implied routing. 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 provided, so the description carries the full burden. It does disclose two useful behavioral facts: matching is case-insensitive and results are capped at 50 rows. However, it does not say what happens on truncation (silent cap vs. flag), whether results are ordered, or what a result row contains.

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 states scope, filter semantics, and result bound with no wasted words.

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

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

For a simple two-parameter search with no output schema and no annotations, the description covers the core contract but omits return-shape and no-match behavior. It is adequate but leaves the agent guessing about what a returned row looks like.

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, limit is not. The description partially compensates by establishing case-insensitive substring matching for query and the 'up to 50' ceiling for limit, but gives no default for limit or indication that the cap and the parameter maximum are the same constraint.

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 Answering Service Quotes dataset whose cells contain the query) with a clear matching rule and result cap. It implicitly distinguishes from row-fetching siblings like dataset_row or dataset_top, but never names an alternative or contrast 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?

There is no when-to-use guidance and no mention of the alternatives in the sibling set (dataset_row, dataset_top, dataset_compare). The agent must infer that this is the free-text lookup tool rather than a structured query tool.

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

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

With no annotations, the description carries the full burden and does disclose non-obvious data-handling behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded but counted. This matters because the agent learns results may be computed over a subset. It does not cover permissions, failure modes, or the shape/ordering of the returned statistics.

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

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

A single dense sentence that front-loads the returned statistics before the scoping and data-cleaning caveats. No filler, though the parenthetical packs three distinct behaviors and could be split for easier scanning.

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, no-output-schema tool with no annotations, the description covers the returned metrics and the data-cleaning rules, which is most of what an agent needs. Missing only explicit routing to sibling tools and any statement about output ordering or edge cases like empty result sets.

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 schema only says 'string, minLength 1' and provides no semantic constraint. The description compensates by making clear the column must hold numeric data (with currency/comma handling) and that non-numeric rows are excluded, which is the key precondition for a correct call.

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

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

States a specific computation (count, min, max, mean, median, sum) over a clearly bounded resource (a numeric column of the Answering Service Quotes dataset). The 'numeric column' scoping separates it from siblings like dataset_row or dataset_search, 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?

There is no explicit when-to-use statement or named alternative among the nine dataset_* siblings. Usage is only implied by the requirement that the target column be numeric and by the note that non-numeric rows are dropped, which tells the agent the tool is inappropriate for text columns.

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 says nothing about the limit cap (max 50), tie-breaking, or the shape of the returned rows, so an agent cannot predict behavior beyond the basic ordering intent.

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 that front-loads the operation and its natural-language use case. It is appropriately sized for a simple ranking tool, though the em-dash aside is slightly informal.

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 3-parameter tool with no annotations and no output schema, the description should cover the limit cap and return shape, and it does neither. An agent could invoke it, but cannot confidently predict the number of rows or the result structure.

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

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

Schema coverage is only 33% (only 'ascending' is documented), so the description must compensate. It does add value by constraining 'column' to a numeric column and restating the highest/lowest direction, but it says nothing about 'limit', leaving a required-for-correctness parameter (row count cap) entirely undocumented.

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 a named dataset ordered by a numeric column, captured by the 'which is the most/least X' framing. It is clearly distinct from dataset_row and dataset_stats by implication, but never explicitly names a sibling to route against, which keeps it from 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 parenthetical 'which is the most/least X' implies the question shape this tool answers, giving implied usage guidance. However, it never states when to choose this over dataset_compare, dataset_stats, or dataset_search, and provides no exclusions or prerequisites.

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 usefully discloses side-effect-relevant facts for the enquiry process — nothing is bought, ordered, or paid, no quote is guaranteed, and it is free — and confirms it returns consent wording and confirmation details. It does not, however, explicitly state that this descriptor tool itself has no side effects or how the returned content is scoped.

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?

It is compact and front-loaded with 'Read first' as the directing statement, followed by a dense but single-purpose block covering subject, scope, and returns. Minor ambiguity about whether it describes itself or submit_enquiry, but no padding.

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 must explain what comes back, and it does: the recipients of details, the consent wording, and how the person confirms. For a zero-parameter descriptor over a complex enquiry flow, this is largely adequate.

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. The description correctly adds no parameter noise and instead spends its words on what the tool conceptually describes.

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 frames itself as a read-only explainer ('Read first') and details what it returns, but it describes its subject in terms of 'what submit_enquiry does' rather than clearly stating 'this tool returns a description of the enquiry flow.' An agent can infer the purpose, but the verb+resource pairing is muddled and it leans on a sibling's behavior rather than 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?

'Read first' gives an implied ordering relative to submit_enquiry, which is a genuine usage cue. However, it never explicitly names submit_enquiry as the alternative to call next, nor states when-not to call this tool, leaving selection largely to inference.

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

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

With no annotations, the description carries the full behavioral burden. It discloses what metadata is returned and how the result is consumed, but never states that the tool takes no arguments, is side-effect free, requires no auth, or returns a stable list — all reasonable inferences left to the agent.

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, and the payload description is front-loaded before the usage hint. Every clause carries information an agent would otherwise have to guess.

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, so the description must describe return values — and it does, naming all six returned attributes plus the allowed-options case, and linking to submit_enquiry. It omits the exact response shape (list vs. object, ordering), the only 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 and the schema is empty, so there is no parameter semantics to document; the description correctly avoids inventing any. Baseline for a no-param tool 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?

States a concrete resource — 'Every field of the Answering Service Quotes enquiry' — and enumerates the metadata returned (key, label, type, required, help text, options), so an agent knows this is a field-introspection tool. It does not, however, distinguish itself from the sibling enquiry_describe, which an agent could reasonably confuse it 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?

Gives a clear contextual workflow: 'Pass answers to submit_enquiry keyed by field key,' which ties this tool to a specific downstream sibling and implies the discovery-then-submit pattern. It stops short of stating when to prefer this over enquiry_describe or dataset_columns, so no explicit 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 disclosure burden and does so well: it explains that step 1 only validates and returns a summary, consent line and token, that nothing is submitted until step 2, and that the submitter must click an emailed link before any provider sees the enquiry. It also defines exactly what consent means. Only minor gaps remain (idempotency/rate behavior) but the critical side effects and gating are covered.

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

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

Front-loads the most decision-relevant fact ('NOT a purchase, NOT a guaranteed quote') and then walks the two steps in order. It is information-dense and every clause is load-bearing, though the enumerated consent sentence and bolded emphasis make it longer than the minimum.

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 tool with no output schema, the description compensates by describing what step 1 returns (summary, consent line, token) and what happens after step 2 (confirmation email before providers see it). Combined with the nested `answers` object, an agent has everything needed to perform the interaction correctly.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters, making 3 the baseline. The description reinforces the meaning of consent and the confirmation token in the workflow, but adds little beyond what the schema's own descriptions and the 'field key from enquiry_fields' note provide.

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 specific verb and resource ('Submits an enquiry to Answering Service Quotes') and immediately carves it away from adjacent concepts by stating it is NOT a purchase and NOT a guaranteed quote. An agent knows exactly what this tool does before reading the schema, and its two-step nature separates it from the read-only dataset_*/enquiry_* discovery siblings.

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

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

Spells out the calling sequence: Step 1 with answers plus consent=true to validate, Step 2 only if the person agrees, passing the same answers plus the confirmation token. It also states the consent precondition explicitly, so the when-to-use and when-not-to-proceed conditions are unambiguous.

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