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

There are no annotations, so the description carries the burden of behavioral disclosure. It does disclose what the tool returns (columns, numeric indicators, row count, provenance banner), but it does not state whether the tool is read-only, whether results are cached, or whether there are any side effects. For a simple zero-parameter schema tool, this is 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?

The description is two short sentences with no wasted words. The returned content is front-loaded, and the usage instruction follows immediately. Every phrase 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?

For a zero-parameter tool with no output schema, the description names all key return categories: columns, numeric flags, row count, and provenance banner. It does not specify exact field names or serialization format, but it is sufficient for an agent to know what to expect and why to call it first.

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 has zero parameters and schema description coverage is 100%, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because no parameter documentation is needed.

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

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

The description clearly identifies the resource (KnownVehicle dataset) and the content (columns, numeric flags, row count, provenance banner), and the instruction to call it first distinguishes it as the schema-introduction tool. It lacks an explicit verb like 'returns' or 'gets', but the meaning is unambiguous.

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 'Call this first to learn the schema' gives an explicit usage context and ordering recommendation. However, it does not name sibling tools like dataset_stats or dataset_provenance or state when this tool should not be used.

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

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

With no annotations, the description carries the full burden. It discloses the ordering behavior ('in the order given') and the filtering logic, but does not mention whether the operation is read-only, what the return format is (e.g., row objects), or edge cases like missing values. The core behavior is clear, but significant behavioral details are absent.

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 a single, efficient sentence that front-loads the core behavior (which rows are returned and in what order) and then adds a concise usage hint. There is no redundant or filler content.

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?

The tool has only 2 parameters and no output schema. The description explains the filter and order but does not specify the return structure (e.g., array of row objects) or handle cases like no matches or invalid column names. For a simple read tool this is adequate but incomplete for an agent that needs to know what to expect in 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 0%, so the description must explain parameters. It does: 'column' is the field to filter on, and 'values' are the list of strings to match. It also adds the ordering semantics tied to the values array. This compensates well for the lack of schema descriptions, though it doesn't elaborate on type constraints (which the schema already provides).

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 tool fetches rows from the KnownVehicle dataset filtered by a column matching any of the given values, preserving the order of values. This distinguishes it from siblings like dataset_row (single row) or dataset_search (broader search) by its specific filtering and ordering behavior.

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 'for "X vs Y" questions' explicitly indicates when to use this tool: comparing specific values side by side. However, it does not name alternative tools or provide exclusions, leaving some ambiguity for agents deciding between this and dataset_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?

Since no annotations are present, the description carries the burden of behavioral disclosure. It names the returned content (source, date, licence, citation) and implies a read-only metadata lookup, but it does not state side-effect-freeness or describe the return shape. That is a gap, though minor for a zero-parameter metadata getter.

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

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

Two short sentences with no filler: the first lists exactly what the tool provides, and the second explains when to use it. Key information (dataset name and attribution purpose) is front-loaded.

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

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

For a zero-parameter tool with no output schema, the description covers the essential behavior: what provenance fields are returned and the intended use. It does not specify how the citation is formatted, but an agent can invoke the tool safely without that detail.

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 schema has no parameters, so the description need not explain parameter meaning; 0 parameters earns the baseline 4. The description adds semantics about what the dataset is rather than about call arguments, which is sufficient.

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 identifies a specific resource — provenance metadata for the KnownVehicle dataset (source, computed date, licence, citation) — and its purpose is clear from the title. It lacks an explicit operation verb such as 'retrieve' or 'return', but it is unmistakably distinct from the sibling data-manipulation tools.

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 use case and tells the agent when to invoke the tool. It does not explicitly exclude alternatives, but none of the siblings return provenance/citation details, so the usage context is clear.

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 must carry the behavioral disclosure. It does disclose the case-insensitive exact-match semantics, which is useful. However, it is silent on cardinality (title says 'a row' but description says 'rows'), what happens on no match, and whether it is read-only.

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 a single, information-dense sentence that front-loads the core matching rule. Every phrase earns its place; no filler.

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

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

For a two-parameter lookup with no output schema, the description is mostly sufficient but leaves a critical ambiguity: the title promises one row, while the description returns 'the rows' (plural). It also omits the return shape and no-match behavior, which an agent invoking this tool would need.

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?

With 0% schema description coverage, the description's phrase 'where a column equals a value exactly' gives natural-language meaning to both `column` and `value`. It does not, however, explain valid column names, value formatting, or case sensitivity of the column name itself.

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 title and description together state the operation ('look up'), the resource ('rows of the KnownVehicle dataset'), and the matching rule ('equals a value exactly (case-insensitive)'). It is clear this is an exact-match lookup rather than a fuzzy search, but it does not explicitly name a sibling for 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?

The description implies a use case: find dataset rows matching a key exactly. It provides no when-to-use or when-not-to-use guidance relative to sibling tools such as dataset_search or dataset_compare, and it does not list 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?

The description discloses case-insensitivity and the 50-row limit, but does not mention sorting, pagination, behavior with no results, or whether all columns are searched. There are no annotations to contradict, so the disclosed behavior is not misleading, but important edge cases are omitted.

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 a single, efficient sentence that conveys the essential search behavior, scope, and limit without extraneous detail. It is well-structured and immediately understandable.

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?

While the description is clear for a simple search, it lacks contextual information such as return format, potential error conditions, or relationship to sibling tools. Given the simplicity of the operation, the missing details are notable but not critical.

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 schema provides a description for 'query' ('text to look for in any cell'), but 'limit' has no description in the schema. The description partially covers 'limit' by mentioning 'up to 50', but the parameter's exact semantics (e.g., default value, bounds) are not fully specified.

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 tool searches rows of the 'KnownVehicle' dataset for a query in any cell with case-insensitivity, and specifies a limit of 50 results. This fully identifies the tool's purpose and differentiates it from other dataset operations.

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

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

The description gives no guidance on when to use this tool versus alternatives like dataset_row, dataset_columns, or dataset_stats. There is no mention of scenarios where search is preferred or how it fits into a larger workflow.

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?

Since no annotations are present, the description carries the burden of explaining behavior. It discloses that non-numeric rows are excluded and counted and that formatting like grouping commas and currency is handled, which gives useful transparency about data processing. It does not explicitly state that the operation is read-only, but the statistical nature strongly implies no modification.

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 a single concise sentence with a parenthetical that adds relevant edge-case behavior. There is no redundant or irrelevant text.

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?

The description identifies the input type, the dataset, and the exact statistics returned, which is sufficient for a simple stats tool without an output schema. It does not detail output formatting, but the listed statistics make the expected result clear.

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 schema only lists a 'column' string with no description, so the description compensates by indicating it must be a numeric column of the KnownVehicle dataset. It also clarifies how values with commas and currency symbols are parsed, adding meaning beyond the bare schema.

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

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

The description clearly identifies the tool's function: computing count, min, max, mean, median, and sum for a numeric column in the KnownVehicle dataset. It distinguishes this from sibling dataset tools by focusing on aggregate statistics rather than rows, columns, or comparisons.

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

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

The description implies the tool is appropriate when summary statistics for a numeric column are needed and notes that grouping commas and currency are handled. It does not explicitly mention alternative tools or state when not to use it, so usage guidance is only 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?

No annotations are provided, so the description carries the full burden. It discloses the core behavior — returning the highest or lowest rows by a numeric column and the highest-first default — but is silent on edge cases such as non-numeric column values, null handling, tie-breaking, and the shape of the returned rows. Partial but not complete behavioral disclosure.

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 a clarifying parenthetical. The intent is front-loaded ('highest or lowest rows') and nothing is wasted. It earns its place, though it is too brief to also serve the guidance role the tool needs.

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?

The tool is moderately complex — three parameters, one required, no output schema, and no annotations — so the description must carry substantial weight. It covers the what (rank by numeric column) adequately but omits tool-selection guidance and edge-case behavior. For a ranking tool amidst a large sibling set, this leaves meaningful gaps the agent must guess at.

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' has a schema description), so the description should compensate for 'column' and 'limit'. It does not: the only parameter hint it adds is the title's 'numeric column' constraint. The meaning of 'limit' bounds and the column type requirement are left to inference rather than spelled out.

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 tool ranks rows of the KnownVehicle dataset by a numeric column, with the memorable framing 'which is the most/least X'. It names a specific verb (rank/top), a specific resource, and the operative dimension, and it aligns with the title. It doesn't, however, explicitly contrast itself with siblings like dataset_stats or dataset_search, 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?

No guidance is given on when to use this tool versus its alternatives. With nine siblings operating on the same dataset (dataset_row, dataset_search, dataset_stats, dataset_compare), the agent must infer that ranking is the right operation. No exclusions or conditions are offered.

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 burden and it covers key behavior: no purchase/order/payment, no guaranteed quote, free, human providers quote directly, and it returns recipient details, consent wording, and confirmation method. It does not explicitly label enquiry_describe itself as read-only/no-side-effect, but 'Read first' and 'Returns' imply it is informational.

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 efficiently front-loaded with 'Read first' and each sentence adds distinct information. It is slightly verbose due to restating title concepts like 'not a purchase/not a guaranteed quote', but there is no wasted 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?

For a 0-parameter, no-output-schema describe tool, it is complete: it tells the agent what submit_enquiry does, what consequences it does not have, and what the returned explanation covers. An agent can correctly choose and invoke the tool with no additional context.

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 has zero parameters and schema coverage is 100%, so there is nothing for the description to add on parameter meaning. Baseline for 0-parameter tools is 4.

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

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

The description states a clear, specific purpose: this tool 'states plainly what submit_enquiry does on KnownVehicle', making it an explainer meta-tool. It distinguishes itself from the sibling submit_enquiry by describing rather than executing, and the title reinforces the 'what you get' framing.

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 opening 'Read first' is an explicit placement instruction: use this before submit_enquiry. The description also clarifies the conditions of the underlying enquiry (free, no purchase, no guaranteed quote), so an agent can decide whether to proceed to submit_enquiry.

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

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

With no annotations, the description carries the behavioral disclosure burden. It explains what data is returned (key, label, type, required, help text, options), but does not explicitly state that this is a read-only lookup, whether authentication is required, or how the result is structured. Adequate but not deeply 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?

The description is two purposeful sentences: the first enumerates exactly what the tool returns, and the second tells the agent how to use that result. No wasted words and the core content 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?

Since there is no output schema, the description adequately communicates the return content and the downstream use of the keys. It does not specify the exact structure for options/help text, but for a zero-parameter metadata lookup this is sufficient for an agent to call the tool confidently.

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 has zero parameters and schema coverage is 100%, so the baseline is 4. The description adds no parameter information because none is needed; the only relevant guidance is the final sentence about using field keys with 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 clearly identifies the resource ('Every field of the KnownVehicle enquiry') and enumerates the returned metadata: key, label, type, required, help text, and allowed options. It also names submit_enquiry as the downstream counterpart, which helps differentiate it from at least one sibling, though it does not explicitly contrast with enquiry_describe.

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

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

The instruction 'Pass answers to submit_enquiry keyed by field key' makes the intended workflow explicit: retrieve the field definitions first, then submit answers using those keys. It provides clear usage context but does not explicitly state when not to use this tool or point to alternatives like enquiry_describe.

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 and discharges it well. It discloses validation, the summary/token return, the two-step consent gating, the email with a mandatory click before providers see the enquiry, and the exact consent wording. Nothing contradicts the annotations because no annotations exist.

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 long but every sentence earns its place: it front-loads the 'not a purchase' warning, numbers the two steps, and includes the exact consent line. The structure is ordered so an agent can follow the workflow without re-reading the 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?

Even with no output schema and no annotations, the description is complete enough to invoke correctly: it specifies step-1 return values (summary, consent line, confirmation token), the exact step-2 payload, the email/link behavior, and references enquiry_fields for answer keys. The only minor absence is the exact step-2 response shape, which is not essential for correct invocation.

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 schema already covers all parameters (100% coverage), so the baseline is 3, but the description adds workflow meaning beyond the schema: answers are keyed by field keys from enquiry_fields, consent must be true only after agreement, and confirmation is the step-1 token to be reused after approval.

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 opens with a specific verb and resource ('Submits an enquiry to KnownVehicle') and immediately qualifies it as 'NOT a purchase, NOT a guaranteed quote', distinguishing it from the sibling enquiry_describe and enquiry_fields tools. The title itself also scopes the action as a two-step submission to human providers.

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

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

The description gives explicit two-step usage instructions: call first with answers and consent=true, then call again with the same answers and the confirmation token only after the person agrees. It also states clear when-not conditions ('not a purchase', 'not a guaranteed quote'), though it does not explicitly name alternative tools such as enquiry_describe or enquiry_fields.

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