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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 burden. It discloses the exact contents of the response (columns, numeric indicators, row count, provenance banner), which is sufficient for a simple read-only tool with no parameters. It does not describe side effects, but none are expected for a schema inspection tool.

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 fluff. The first sentence front-loads the output contents, and the second gives the usage directive. Every word 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 tool with no parameters and no output schema, the description fully covers what an agent needs: what it returns and when to call it. Nothing is missing 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 tool has zero parameters, so there is nothing to document. Per the rubric, a baseline of 4 is appropriate when there are no parameters, and the description correctly omits parameter details since none exist.

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 returns columns, numeric flags, row count, and provenance banner for a specific dataset. It also positions itself as the schema discovery tool, differentiating it from sibling tools like dataset_stats or dataset_row which serve different purposes.

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?

Explicitly instructs to call this tool first to learn the schema, giving clear temporal guidance for when to use it. This establishes it as the entry point for exploring the dataset, which is a strong usage directive.

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 provided, the description must disclose behavioral traits. It states that rows are returned in the order given, which is a useful behavioral detail. However, it does not mention that the operation is read-only (though that is implied), what happens if no rows match, whether the returned rows include all columns, or any error handling. The description is minimal and leaves several behavioral aspects unstated.

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 that is concise and front-loaded with the main action. It does not waste words and conveys the core functionality efficiently. However, it could be slightly more structured to separate the behavior from the usage hint, but overall it is appropriately sized.

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 that the tool has only two simple parameters and no output schema, the description is somewhat adequate. It states the dataset, the filtering logic, and the ordering, which covers the main functionality. However, it does not specify what fields are included in the returned rows, whether the output includes the column values, or what happens in edge cases like no matches. For a tool with no output schema, this level of detail is only partially 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 schema description coverage is 0%, meaning the description must compensate for parameter meaning. The description says 'whose column is any of the given values' which essentially restates the schema's 'column' and 'values' parameters without adding any new meaning. It does not explain what types of values are expected, how the column name is used, or any constraints beyond the schema. The description adds no value in explaining the 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 states the tool returns rows from the DamageRestore HQ dataset where a given column matches any of the provided values, and preserves the order of those values. It includes a usage hint ('for X vs Y questions') that adds specificity, though it does not explicitly differentiate from sibling tools like dataset_search or dataset_row. The verb 'compare' in the name is clarified by the description, so the purpose is understandable.

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 a contextual hint about when to use this tool (for 'X vs Y' questions), but it does not offer explicit guidance on when not to use it or mention alternatives among the sibling tools. There is no mention of dataset_search or dataset_row as alternatives, and no exclusion criteria. The hint implies a specific use case but lacks direct comparison with other tools.

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 behavioral disclosure. It indicates this is a read-only metadata retrieval (source, date, licence, citation) with no side effects or parameters. It does not detail potential variations (e.g., what if citation format differs), but for a simple provenance tool this is adequate.

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 one clear, front-loaded sentence that immediately states what the tool provides and why to use it. No wasted words; every clause adds value.

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, no output schema, and no annotations, the description provides all essential context: what it returns (source, date, licence, citation) and when to use it (for correct attribution). It is complete for a simple metadata retrieval tool. The only minor gap is that it doesn't specify the exact format of the citation, but that is likely covered by the returned data itself.

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 the schema is empty, so there is no parameter semantics to add. The description compensates by explaining what the tool returns (source, date, licence, citation), which is sufficient given the absence of parameters. Baseline 4 for zero-parameter tools is appropriate.

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 provides source, computed date, licence, and citation for a specific dataset, and explicitly tells the agent to read it for correct attribution. It distinguishes itself from sibling dataset tools (columns, compare, row, search, stats, top) by focusing on provenance and citation rather than data manipulation or inspection.

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 implicitly guides usage by stating 'Read this to attribute a figure correctly,' which tells the agent when to call it (when attribution/citation is needed). It does not explicitly exclude other tools, but given the distinct purpose, the context is clear enough.

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 burden and does add meaningful behavior: exact match with case-insensitivity. It does not disclose output shape, whether multiple rows can be returned, pagination, or error behavior, so coverage is only 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 description is a single tight sentence with no filler, and the key qualifiers "exactly" and "case-insensitive" are placed early. It is appropriately sized for the tool's simplicity.

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 core matching semantic is conveyed well enough to begin using the tool. However, unresolved singular-vs-plural behavior, lack of explicit sibling differentiation, and no return/error details leave the description minimally sufficient 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?

The schema has 0% description coverage, so the description must compensate. It only restates the column-equals-value relationship, which is close to the parameter names, and adds case-insensitivity; it does not clarify column naming, allowed values, or match formatting. The parameter documentation gap remains largely unfilled.

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 exact-match row filtering against a named dataset, and the "exactly (case-insensitive)" phrasing distinguishes it from fuzzy search siblings. However, the title says "a row" while the description says "rows," leaving singular-vs-plural scope slightly ambiguous.

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 exact-equality wording implies this tool is for precise key lookups rather than dataset_search or other comparison tools, but no alternative is explicitly named. Usage context is inferred rather than stated with when-to-use or when-not-to-use guidance.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses case-insensitive matching and the 50-row cap, but omits the output format, row ordering, pagination behavior, and what happens when the query matches non-string cells.

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 communicates the essential behavior with no filler. Every clause adds either matching semantics or a constraint.

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 is sufficient for a basic call, but with no output schema, it should clarify the response structure and pagination limitations more explicitly. The mention of 'rows' implies the return type, but an agent still lacks detail on the exact result shape.

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 50%, and the description adds useful semantics by clarifying query matching is case-insensitive and across any cell, and by stating the up-to-50 limit. However, the limit parameter's exact role beyond the cap is not elaborated, so the compensation is partial.

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 specific operation: returning rows of the DamageRestore HQ dataset that contain the query in any cell, with case-insensitivity and an upper bound. This clearly distinguishes it from siblings like dataset_columns, dataset_row, and dataset_stats.

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 intended use as a full-text row search is implied by the description, but there is no explicit guidance on when to choose this tool over alternatives such as dataset_row or dataset_top, nor any exclusions or conditions.

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 handles two non-obvious behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not describe return format or error cases, but the key data-cleaning caveats are disclosed.

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 dense sentence that front-loads the computed statistics and appends the important data-handling caveats in a parenthetical. There is no filler or repetition.

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 single-parameter tool with no output schema, the description is largely complete: it defines the primary behavior and the important edge-case handling. It would benefit from specifying the output shape or behavior when all rows are non-numeric, but the core usage is 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 says the parameter is a non-empty string, so the description adds important meaning: the column must be numeric and values may include grouped commas or currency symbols. This goes beyond the minimal schema information.

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 the tool computes—count, min, max, mean, median, and sum—for a numeric column of a specific dataset, which clearly distinguishes it from sibling tools like dataset_row, dataset_search, and dataset_top.

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 intended use is implied: use this tool when summary statistics for a numeric column are needed. However, it never names sibling tools or states when not to use it, leaving the selection decision partially 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 burden of explaining behavior. It discloses that the tool returns the highest or lowest rows by a numeric column and supports a lowest-first mode. It does not mention default limits, tie-breaking behavior, handling of invalid numeric columns, 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?

The description is one concise sentence and front-loads the core behavior. The added quote 'which is the most/least X' is slightly redundant with 'highest (or lowest) rows,' but it reinforces the intended use case without bloating the 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?

There is no output schema and no annotations, so the description should cover return behavior. It explains the ranking concept and dataset target but omits practical invocation details such as default limit behavior and what fields are returned for each row. The core purpose is clear, but an agent cannot fully predict the result shape from the description alone.

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 parameter description coverage is only 33%, so the description must compensate. It adds meaning by clarifying that 'column' must be numeric and that 'highest/lowest' maps to the ascending parameter. It does not describe the limit parameter's default behavior, though the schema does provide its range.

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 by a numeric column and returns the highest or lowest ones, which maps directly to the title 'Rank rows by a numeric column.' This distinguishes it from siblings like dataset_search and dataset_stats.

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 a clear usage context: answer 'which is the most/least X' by ranking rows. However, it does not explicitly contrast this tool with alternatives such as dataset_search or dataset_stats, so the guidance is implied rather than explicit.

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