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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 main return contents (columns, numeric flags, row count, provenance banner), which is useful. However, it does not explicitly state that this is a read-only operation, nor does it mention response format, potential errors, or whether it accesses external data.

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, information-dense sentence lists exactly what the tool returns and adds a clear call-to-action. There is no filler or repetition of the title or 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?

For a simple zero-parameter metadata tool with no output schema, the description is complete: it names the dataset, lists the returned information, and positions itself as the first call. An agent has enough context to select and invoke it correctly.

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

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

The tool has zero parameters and schema description coverage is 100%, so there are no parameter semantics for the description to add. Per the rubric baseline, a score of 4 is appropriate 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 (Intakez dataset) and the specific outputs: columns, numeric flags, row count, and provenance banner. The title adds 'columns and shape'. It does not explicitly use a verb like 'returns' or 'lists', but 'Call this first to learn the schema' 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 explicit usage context: 'Call this first to learn the schema.' This tells an agent when to invoke it relative to other dataset tools. It does not name alternatives or state when not to use it, but for a zero-parameter schema-introspection tool this is adequate 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?

With no annotations, the description carries the full behavioral burden. It meaningfully discloses that rows are matched against any of the listed values and are returned in the order given, but it does not state whether results are shown side-by-side, how matching treats exactness or case, or what the response structure looks like.

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, front-loaded sentence with no wasted words. It efficiently communicates the resource, the filtering logic, the ordering behavior, and the intended use case.

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

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

For a simple two-parameter retrieval tool, the description covers the essential invocation logic. However, since there is no output schema and no annotations, it would be more complete if it explicitly described the side-by-side output format hinted at by the title.

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 compensate. It does by explaining the relationship between column and values: rows are selected when the column equals any provided value, and the values array controls output ordering. It does not detail string-matching nuances, but the core semantics are present.

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

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

The description clearly states what the tool does: it returns rows of the Intakez dataset filtered by a column matching any of the given values, in the supplied order. The 'X vs Y questions' framing distinguishes it from siblings like dataset_row and dataset_search, which serve different retrieval needs.

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 when-to-use signal, and the multi-value ordered inputs imply a comparison-oriented use case. However, it does not explicitly name alternative tools or state when not to use this tool.

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

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

No annotations are provided, so the description carries the behavioral disclosure burden. It clearly states what the tool returns (source, computation date, licence, citation) and implies a read-only metadata lookup. It does not explicitly guarantee no side effects, but the content and phrasing make this a safe, non-mutating operation.

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 title and description are concise, front-loaded, and free of filler. Every phrase adds value: the title frames the purpose, and the description lists exactly what information the agent can expect.

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 lookup, the description is reasonably complete: it names the dataset and enumerates the returned provenance fields. It does not specify the exact output format, but no output schema is provided and the expected content is clear enough for an agent to use the result for attribution.

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 accepts zero parameters, so the input schema provides no parameter documentation. With no parameters, the description does not need to explain argument semantics; the baseline of 4 applies because there is nothing to compensate for.

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 this tool provides provenance information: source, computed date, licence, and citation for the Intakez dataset. This is specific and distinct from the sibling tools, which cover columns, rows, search, statistics, comparisons, and top values.

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 explicitly tells the agent when to use this tool: 'Read this to attribute a figure correctly.' It gives a clear use case, though it does not explicitly discuss when not to use it or mention alternatives. Since the sibling tools are clearly unrelated to provenance, this 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose a non-obvious behavior—case-insensitive matching—which adds value. It does not mention how multiple matches are returned, edge cases, or any access/usage limitations.

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, front-loaded sentence with no filler. It states the core behavior and the important matching semantics efficiently, making it easy for an agent 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 straightforward two-parameter lookup, the description conveys the essential matching behavior. However, with no output schema and no annotations, it leaves return format and sibling differentiation (notably from dataset_search) implicit, which an agent would need to infer.

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 descriptions (0% coverage), so the description must compensate. It successfully explains that 'column' is the field to check and 'value' is the value to match exactly, which goes beyond the bare schema. It omits details like column name syntax or how the case-insensitivity affects the values, but for two simple string parameters this is adequate.

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 function: returning rows from the Intakez dataset where a column equals a value exactly, with case-insensitive semantics. It conveys a specific verb-resource combination and the term 'exactly' helps differentiate it from likely fuzzy search alternatives, though it does not explicitly name sibling 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?

The usage context is implied through phrases like 'equals a value exactly' and 'case-insensitive', suggesting this tool is for exact-match lookups rather than searching. However, it does not explicitly state when not to use the tool or mention alternatives such as dataset_search, leaving the decision partly 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 behavioral disclosure. It usefully states case-insensitive matching and a 50-row cap, but it does not mention result ordering, behavior when no cells match, whether matching is substring-based, or how the optional limit parameter interacts with the cap.

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 packs the dataset name, matching scope, case-insensitivity, and result cap with no filler or redundancy. Key operational detail is front-loaded before the cap limitation.

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 nearly complete: it states what is searched, what is returned, and key matching rules. The main remaining gaps are lack of explicit default for limit and absence of ordering guarantees, but these are minor for a tool this simple.

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 describes only the query parameter, leaving the limit parameter undocumented. The description adds useful semantics ('case-insensitive', 'up to 50') but does not explicitly map 'up to 50' to the limit parameter or clarify its default behavior, so it only partially compensates for the 50% schema coverage.

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 specific verb-resource pair: it searches the Intakez dataset and returns rows containing the query. Its unique focus on text matching in any cell distinguishes it from sibling tools like dataset_row, dataset_stats, or dataset_compare without ambiguity.

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 when you need rows whose cells contain a given text string. However, it gives no explicit guidance about when not to use it or which sibling tool to prefer for other operations (e.g., exact row retrieval with dataset_row or statistics with dataset_stats).

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 disclosure burden. It does disclose meaningful preprocessing behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it leaves unclear whether the reported 'count' includes excluded rows, what happens if all rows are non-numeric, and what the exact output format is.

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 with the computed metrics front-loaded and edge-case behavior in a parenthetical. There is no redundant wording or boilerplate.

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

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

For a simple one-parameter aggregation tool, the description names all expected results and key data-handling behaviors. Even without an output schema, an agent knows what metrics to expect. Minor ambiguity about the exact meaning of 'counted' and lack of error behavior are acceptable given the low complexity.

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 no description for the single 'column' parameter beyond string constraints. The description adds that the value must be a numeric column of the Intakez dataset and that numeric formats are normalized. It does not explain how to discover valid column names or whether matching is case-sensitive, but for one parameter this is acceptable.

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 lists six concrete summary metrics (count, min, max, mean, median, sum) tied to a numeric column of the Intakez dataset, making the tool's purpose explicit. It is clearly distinguishable from siblings like dataset_top or dataset_row because it returns aggregate statistics rather than rows or top values.

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 does not state when to prefer dataset_stats over sibling tools such as dataset_top, dataset_search, or dataset_columns. Usage context must be inferred from the phrase 'numeric column', but no explicit alternatives or exclusions are 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?

With annotations absent, the description carries the burden of behavioral disclosure; it does state the core ordering behavior (highest/lowest) and the numeric-column requirement. However, it does not mention default limit behavior, tie-handling, nulls, or the exact response shape, leaving some behavioral uncertainty.

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 tightly-written sentence that front-loads the core behavior and uses a memorable intent phrase ('most/least X'). Every word earns its place and there is no redundant restating of 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?

For a simple three-parameter ranking tool this is close to sufficient, but with no output schema and no annotations the agent is left guessing about the default row count, the exact return shape, and edge-case ordering. The description plus schema enable a basic call, but not fully confident invocation in all common phrasing.

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%, and the description compensates partly by specifying that the column must be numeric and that ascending/descending maps to lowest/highest. It adds no explicit semantics for the limit parameter, whose default and effect on row count are left to inference from its name and schema 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?

The description clearly identifies the operation: return the highest or lowest rows ranked by a numeric column, and frames it as the 'most/least X' question. This precise ranking function is visually and semantically distinct from siblings like dataset_stats, dataset_search, and dataset_row, so an agent can select it without opening schemas.

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 quoted intent 'which is the most/least X' provides a clear trigger for when to call this tool, and 'highest or lowest rows' tells the agent the expected query shape. It does not explicitly name alternatives or exclusion conditions, so it stops short of a 5.

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