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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 disclosure burden. The read-only, side-effect-free nature is implied by the information-returning phrasing and the zero-argument schema, but the description never states it explicitly, nor does it mention pagination, cost, or auth. Adequate but not rich.

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

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

Two tightly packed sentences with no filler; the return-value list is front-loaded and the action cue follows. The output enumeration is somewhat dense but every item earns its place.

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

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

With no output schema and no annotations, the description must convey what comes back – and it does, listing the columns, numeric flags, row count, and provenance banner. Missing only explicit read-only affirmation and any note on the sibling provenance overlap.

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. Its enumeration of returned fields is output information, not parameter semantics, so no extra credit is warranted.

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 resource (the Payroll Services Quotes dataset) and enumerates exactly what is returned: columns, which are numeric, row count, and provenance banner. It is clearly not dataset_row, dataset_stats, or dataset_search. It does not, however, differentiate itself from the sibling dataset_provenance, which the 'provenance banner' clause overlaps 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?

'Call this first to learn the schema' gives an explicit ordering cue relative to the other dataset_* tools, which is genuine usage guidance. It stops short of stating any when-not condition or naming a specific alternative for overlapping needs (e.g. dataset_provenance).

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 at all, the description carries the full behavioral burden. It does disclose two real traits: the dataset is fixed to Payroll Services Quotes, and result order follows the supplied value order. It says nothing about unmatched values, errors on a bad column name, case sensitivity, or the shape of the returned rows.

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

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

A single sentence with no filler, and the core selection rule is front-loaded ahead of the usage hint. The phrasing is slightly convoluted with the embedded 'whose column is any of the given values' clause, 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?

There is no output schema, no annotations, and no parameter documentation, yet the description is only one sentence. An agent cannot tell what a matched row looks like, what happens if a value matches nothing, or what happens if the column does not exist, which are exactly the gaps this description needed to fill.

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 functionally: 'column' is the field being matched and 'values' is the set to match against, with ordering of the output tied to the order of the given values. It still leaves ambiguities such as whether 'column' is a display name versus an id, and it says nothing about the 2-to-10 item 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?

The description pins down the exact resource (rows of the Payroll Services Quotes dataset), the selection rule (rows whose column matches any given value), and the output ordering (in the order given). That is a specific, testable purpose, though it never explicitly names or contrasts itself with siblings like dataset_search or dataset_row, so an agent must infer the routing.

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 phrase 'for "X vs Y" questions' gives a usable trigger condition, which is more than nothing. However, it offers no when-not guidance and never names an alternative tool, so the agent has to infer how this differs from dataset_search or dataset_row.

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 operation ('Read this') and lists the returned fields, but it does not explicitly state that the tool is read-only, has no side effects, or requires no special permissions. For a no-parameter metadata lookup this is adequate but not fully transparent.

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

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

Two sentences with no wasted words, front-loading the returned information and following with a clear usage instruction. The structure is efficient and easy to parse.

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

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

Given no output schema and no parameters, the description lists the exact fields returned (source, date, licence, citation), which is sufficient for an agent to understand the result. It could mention that the tool is read-only or that the dataset is fixed, but overall it is complete for this simple tool.

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

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

The tool takes zero parameters, so the schema cannot add meaning. The description appropriately does not discuss parameters, and the baseline for 0 params is 4.

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

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

The description states exactly what provenance information is returned (source, computed date, licence, citation) for the named dataset, which distinguishes it from data-querying siblings like dataset_columns or dataset_stats. An agent can immediately tell this tool is for attribution, not data retrieval.

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 sentence 'Read this to attribute a figure correctly' gives a clear when-to-use condition. However, it does not name any alternative tool or explain when not to use this one, leaving some routing inference to the agent.

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 case-insensitive exact matching and that multiple rows ('The rows') may match, but says nothing about behavior for unknown columns, empty results, or any result caps.

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 identifies the resource and the match rule with no filler. It is efficient, though it reads as a terse fragment rather than a complete instruction.

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 annotations and no output schema, and the description does not explain the return shape (full rows vs. projected fields) or how to discover valid column names via dataset_columns. Adequate for the basic call but leaves real gaps.

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 two bare parameters (column, value) rely on the description. The description does convey that one operand is a column name and the other a value compared case-insensitively, partially compensating, but gives no guidance on valid column names or value formatting.

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 (look up rows) and resource (rows of the Payroll Services Quotes dataset) plus the filtering condition, so the operation is unambiguous. It does not explicitly name a sibling such as dataset_search to sharpen the distinction, so it stops 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 phrase 'equals a value exactly' implies this is for exact-match lookups rather than fuzzy retrieval, which implicitly contrasts with dataset_search and dataset_compare, but no when-to-use or when-not-to-use guidance is stated.

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

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

With no annotations provided, the description carries the full behavioral burden. It does disclose two useful traits: matching is case-insensitive and results are truncated at 50 rows, which tells the agent result sets are capped. It omits anything about ordering, permissions, or whether the 50-row cap is a default or a hard ceiling.

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 efficient sentence with the resource and cap front-loaded and no filler. It reads as a fragment rather than a full sentence, 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?

For a two-parameter read-only search with no output schema, the definition is adequate but thin: it does not say what shape the returned rows take or how the 50-row cap interacts with the limit parameter. Annotations are absent, so more behavioral detail would have been warranted.

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 50% (query documented, limit not). The description adds meaning by stating the match is case-insensitive and that results go 'up to 50', which implicitly explains the limit parameter's ceiling rather than leaving it purely to the schema's maximum.

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

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

The description names a specific verb+resource ('Rows of the Payroll Services Quotes dataset whose cells contain the query'), so an agent knows exactly what is searched and over what scope. It does not, however, distinguish this tool from siblings such as dataset_top or dataset_compare, so sibling differentiation is missing.

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 guidance on when to use this over alternatives like dataset_compare or dataset_top, nor any stated prerequisites. Usage is only implied by the word 'search'.

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 usefully discloses that grouping commas and currency are parsed and that non-numeric rows are excluded and counted, which is real value. It does not say what happens on a nonexistent column, an all-non-numeric column, or what form errors take.

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 output metrics, with the dataset scope and parsing caveats tucked into a parenthetical. Dense and largely waste-free; the parenthetical is long but each clause adds information.

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

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

With no output schema, enumerating the returned metrics in the description is exactly the right compensation, and the parsing/exclusion notes cover the main data-quality surprises. Missing only error/edge-case behavior for invalid or fully non-numeric columns.

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 is a single parameter at 0% schema description coverage, so the schema gives only type and minLength. The description indirectly constrains it (must be a numeric column, currency/grouping tolerated) but never defines the column identifier format, casing, or header naming.

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 operation (summary statistics), enumerates the returned metrics (count, min, max, mean, median, sum), and scopes it to a numeric column of a named dataset. That is specific enough to separate it from siblings like dataset_columns or dataset_row, though it does not explicitly name the sibling it is not.

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

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

Usage is implied rather than stated: the agent can infer this is for numeric-column aggregation, and the note that non-numeric rows are excluded implies the column should be numeric. There is no explicit when-to-use/when-not guidance or routing to alternatives such as dataset_top or dataset_compare.

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, and it does not state that this is a read-only operation, that the result is capped (limit max 50), how ties are broken, or what the returned rows look like. It adds only the ascending/descending semantics implicit in 'highest (or lowest)'.

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 and the ranking operation come first, followed by the interpretive gloss. It is efficient, though it is arguably too sparse for a tool with no annotations or output schema.

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 33% parameter coverage, the description does not supply enough to call the tool confidently: return shape, ordering/tie behavior, and the limit cap are all unaddressed. It identifies the tool but leaves the operational details blank.

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 clarify that 'column' must be numeric and that direction determines highest vs. lowest. However, it says nothing about the 'limit' parameter or the 50-row cap, leaving half the parameters to inference from the schema's min/max 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?

States a specific operation (return the highest/lowest rows) on a named resource (Payroll Services Quotes dataset) keyed by a numeric column, and the parenthetical 'which is the most/least X' makes the intent unmistakable. It does not name a sibling tool, but the 'dataset_' family and 'top' semantics are clear enough to distinguish it from dataset_stats or 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 'which is the most/least X' framing implies the use case (extremal-value questions), but there is no explicit when-to-use vs. alternatives guidance and no exclusions — an agent might reasonably wonder whether dataset_stats or dataset_search is the better fit for rankings. Usage is inferable rather than stated.

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

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

With no annotations, the description carries the full burden, and it does disclose meaningful outcome semantics: nothing is bought/ordered/paid, no quote is guaranteed, and it is free. However, it says little about this tool's own behavior (side effects, permissions, return shape beyond a content list), so coverage is partial.

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 directive 'Read first.' is front-loaded and the remainder is compact, with each clause adding a distinct fact. The second sentence is dense and slightly run-on, which keeps it short of a perfect score.

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, annotation-free read tool with no output schema, the description supplies the essentials: what triggers it, what it returns, and the key semantic caveats (free, non-binding). It is largely self-sufficient, with only minor gaps around confirmation/permission 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, so the schema is trivially complete and there is nothing for the description to disambiguate. Baseline 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 establishes this as a read-only informational tool that explains the enquiry process behind submit_enquiry, and it names concrete returned content (who receives details, consent wording, confirmation method). It is distinguishable from the submit_enquiry sibling, though the framing ('states plainly what submit_enquiry does') is slightly meta and describes the payload rather than a crisp verb+resource.

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 directive that positions this ahead of submit_enquiry and other enquiry tools. It implies the read-before-write pattern but does not spell out when NOT to use it or name an explicit alternative, so it stops short of a full 5.

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 it does disclose the payload shape (key, label, type, required flag, help text, allowed options) in the absence of an output schema. It also reveals the behavioral contract that answers are keyed by field key. It does not mention permissions, caching, or stability of the field set, but for a zero-parameter metadata read that is a minor gap.

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

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

Two sentences, no filler, with the resource definition front-loaded and the downstream usage constraint placed second. Every clause earns its place.

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

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

With no output schema, the description usefully enumerates the returned fields and explains how the result feeds submit_enquiry, which is what an agent needs to complete the enquiry workflow. It is nearly complete; the only missing piece is how it relates to enquiry_describe if both exist.

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 implies there is nothing to supply and instead explains how the returned keys are consumed downstream by submit_enquiry.

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 resource ('Every field of the Payroll Services Quotes enquiry') and enumerates exactly what each field record contains (key, label, type, required, help text, options), so an agent knows it is a metadata/schema-listing tool. The title reinforces this. It falls short of a 5 because there is no verb ('returns'/'lists') and no explicit differentiation from the sibling enquiry_describe, which an agent must guess at.

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

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

Usage is implied rather than stated: 'Pass answers to submit_enquiry keyed by field key' tells the agent this tool is the prerequisite step for submit_enquiry, which is real workflow guidance. However, there is no explicit when-to-use versus enquiry_describe, no exclusions, and no statement that this is a pure read step to call before answering.

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 richly: it discloses validation behaviour, the returned summary/consent line/token, that submission requires a second call, that an email link must be clicked before any provider sees the enquiry, and the exact consent text. This is exactly the behavioural context an agent needs for an irreversible submit.

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 flow is dense and runs long, but it is front-loaded with the critical negative framing and cleanly segmented into Step 1/Step 2. Length is justified by a genuine two-call workflow, though the embedded consent quote adds bulk.

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 the description compensates by describing what each step returns (summary, consent line, confirmation token) and what the end user receives (email with a click-through link). 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 baseline is 3, but the description adds meaning by tying 'answers' to field keys from enquiry_fields and by spelling out that 'consent' means the person has read and agreed to the quoted statement. The confirmation token's origin (step 1 output) is also clarified beyond the schema.

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

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

States a specific verb and resource ('Submits an enquiry to Payroll Services Quotes') and immediately disambiguates with 'NOT a purchase, NOT a guaranteed quote.' An agent can distinguish this from siblings like enquiry_describe and enquiry_fields 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 an explicit two-step protocol: Step 1 with answers and consent=true, then Step 2 only if the person agrees, passing the confirmation token. It states the condition that selects each call and what must happen between them (show the summary and consent line), 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.