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

With no annotations, the description carries the burden of explaining behavior. It discloses the exact contents of the call: column names, numeric indicators, row count, and provenance banner. This is sufficient for a zero-parameter, introspective schema-discovery tool, even though it does not explicitly state 'read-only' or 'no side effects.'

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 first sentence lists the return contents; the second gives the invocation guidance. It is front-loaded 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 the tool has no parameters and no output schema, the description is complete enough for an agent to know exactly what will happen and when to call it. It explains the output contents and adds the key contextual instruction to call it first for schema discovery.

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, so the input schema is empty and there is nothing to document. The baseline for 0-parameter tools is 4, and the description appropriately focuses on output rather than parameters.

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 enumerates what the tool returns: columns, which are numeric, row count, and provenance banner. It identifies the dataset explicitly (Consentvia) and is distinct from siblings like dataset_stats or dataset_provenance. It lacks a direct verb like 'returns', but 'Call this first' plus the listed outputs makes the purpose 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 description gives an explicit usage directive: 'Call this first to learn the schema.' This tells an agent when this tool should be used relative to other dataset tools. It does not name specific alternatives or exclusion criteria, but the guidance is clear and actionable.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully explains that rows are selected by matching any of the given values and that the result order follows the order of the provided values. It does not describe error behavior, exact-match semantics, or whether rows are deduplicated, but the core selection and ordering behavior is 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 a single, compact sentence that conveys the filtering rule, ordering behavior, and intended use case. Every phrase earns its place and there is no redundant or vague 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 tool with only two scalar parameters and no output schema, the description is largely sufficient: it states what rows are returned and in what order. It could be more complete by clarifying whether matching is exact or case-sensitive, and what happens when no rows match, but the core calling context is well covered.

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 for both parameters. It does: 'column' is the field to match against, and 'values' are the candidate values that also determine output ordering. This adds meaningful semantic context 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 states that the tool returns rows from the Consentvia dataset filtered by a column matching any of the provided values, in the given order. It is distinct from sibling tools like dataset_row or dataset_search because it targets multi-row comparison scenarios, though it lacks an explicit action verb like 'returns' or 'compares'.

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' gives a clear usage context, indicating this tool is for comparing specific rows side by side. However, it does not explicitly mention when to prefer this over sibling tools such as dataset_search or dataset_row, leaving the differentiation 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 provided, the description carries the burden of behavioral disclosure. It makes clear this is a read-only informational tool returning citation and licensing metadata, and there is no hint of side effects or mutations. It could add output format detail, but the behavior is simple and well conveyed.

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 sentences with no wasted words. It front-loads the specific contents of the provenance record and then states the practical purpose.

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 metadata tool with no output schema, the description fully conveys what the agent will receive and why to invoke it. Nothing essential is missing.

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

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

The tool has zero parameters and schema coverage is 100%, so there are no parameter semantics to explain. The baseline of 4 applies because no compensation 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 tool as returning provenance information—source, computation date, licence, and citation—for the Consentvia dataset. This distinguishes it from sibling tools that handle columns, rows, search, stats, and 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 gives a clear use case: read this to attribute a figure correctly. It does not explicitly mention alternatives or when not to use the tool, but for a zero-parameter provenance tool the context is sufficient.

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 important behavior: exact equality and case-insensitivity. However, it leaves ambiguity about whether a single row or multiple rows are returned (title says 'a row', description says 'rows'), and does not mention output shape or side-effect safety. With no annotations, this is a meaningful 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?

The description is a single, compact sentence with no padding. It front-loads the core behavior and includes the key nuance of case-insensitive exact matching.

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 simple and the description covers its core matching behavior, but sans output schema or annotations, the agent is left unsure of the return structure and whether multiple matching rows are possible. The singular/plural inconsistency between title and description adds confusion.

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 must compensate. It does relate 'column' and 'value' to the matching logic, but it adds no detail about valid column names, value formatting, or how the case-insensitive rule applies. The schema only provides string types.

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 clearly convey an exact-match, case-insensitive row lookup on the Consentvia dataset. The word 'exactly' distinguishes it from sibling tools like dataset_search, making the tool's specific role immediately clear.

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 when to use the tool (for exact matches rather than fuzzy search), but it does not explicitly state when to use it versus dataset_search or other siblings, nor does it give 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?

No annotations are provided, so the description carries the burden of disclosing behavior. It adds important behavioral details: case-insensitive matching, matching across cells, and a 50-row cap. It does not mention ordering or exact output structure, but for a simple search tool it discloses the key runtime traits an agent needs.

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 sentence with no filler. Every phrase contributes: 'Rows', 'Consentvia dataset', 'cells contain the query', 'case-insensitive', and 'up to 50'. Critical information is front-loaded and 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 two-parameter search tool with no output schema, the description is mostly complete. It tells the agent what is searched, how matching works, and the maximum result count. It could be slightly richer by naming the limit parameter or describing the exact row output, but the call can be correctly constructed from the available information.

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 describes the query parameter but leaves limit undocumented, giving 50% schema coverage. The description compensates by explaining the query semantics ('contain the query, case-insensitive') and the result cap ('up to 50'), which clarifies how both parameters affect the call. It adds value beyond the schema without fully detailing default behavior.

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 verb and resource: it returns rows of the Consentvia dataset that match a query. It specifies the matching behavior as containing the query in cells, which distinguishes it from sibling tools like dataset_columns, dataset_stats, or dataset_row. The scope 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 description implies the tool should be used when an agent needs rows by arbitrary cell text, but it does not explicitly say when to use this over alternatives such as dataset_row, dataset_top, or dataset_compare. Usage context is reasonably clear from the wording, but no explicit when/when-not guidance is provided.

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

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

No annotations are provided, but the description discloses important operational behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond a simple summary and gives the agent meaningful expectations about edge-case handling.

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, well-structured sentence that front-loads the list of statistics and includes the important caveats in a parenthetical. There is no wasted text, and every phrase contributes to understanding the tool's behavior.

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 one parameter and no output schema, the description covers the dataset context, acceptable input type, and handling of malformed rows. It does not explicitly describe the output format, but the listed statistics strongly imply what will be returned, making the description sufficiently 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?

The only parameter is 'column', and the schema simply requires a non-empty string. The description adds value by specifying that the column should be numeric and by explaining how non-numeric data is treated, which helps the agent understand what kind of input is valid.

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Consentvia dataset. This is specific enough to distinguish it from the sibling tools like dataset_search, dataset_row, and dataset_top based on name and purpose.

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 provides no explicit guidance on when to choose dataset_stats over alternatives such as dataset_top or dataset_search. There are no usage conditions, exclusions, or mentions of alternative tools, leaving the agent to infer applicability solely from the tool's name and description.

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 of behavioral disclosure. It states that the tool selects highest or lowest rows and that ordering is based on a numeric column, which is the core behavior. It does not detail edge cases such as null values, non-numeric columns, tie handling, or whether full rows are returned, but the primary behavior is 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 a single, well-structured sentence that front-loads the operation and resource. The quoted 'which is the most/least X' is a helpful semantic gloss rather than filler, and there is no redundant phrasing.

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 plus schema covers the core contract: numeric column, ordering direction, and limit bounds. However, with no output schema and no annotations, the agent is left without explicit guidance on output shape, default limit, or how this tool should be chosen over dataset_stats or dataset_search. These are meaningful gaps for a tool that returns ranked rows.

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%, so the description must compensate. It adds meaning for 'column' by specifying it must be numeric and clarifies the intent of 'ascending' through 'highest or lowest.' However, the 'limit' parameter is not explained beyond its schema constraints, and there is no mention of default limit behavior when omitted.

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

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

The description clearly names the operation: return the highest or lowest rows of the Consentvia dataset, ordered by a numeric column. It also translates the operation into a plain-language question ('which is the most/least X'). However, it does not explicitly distinguish itself from sibling tools like dataset_search or dataset_stats, so differentiation relies on the operation name rather than direct comparison.

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 clear usage context: use this when the query is about ranking rows by a numeric value, e.g., 'which is the most/least X.' It does not mention alternatives or when not to use the tool, but the context is specific enough that an agent can infer the intended use case.

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