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

With no annotations, the description carries the behavioral disclosure burden. It discloses the informational output (columns, numeric flags, row count, provenance banner) and implies a read-only inspection use case, but it does not explicitly state side effects, permissions, or that it is non-mutating. The output-focused description 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 concise, front-loads the core output, and ends with a practical usage directive. All content earns its place, with 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?

Given the simple zero-parameter design and the presence of sibling tools, the description covers the key return contents and the recommended invocation order. It does not specify the exact return shape, but for a schema-discovery tool this is a minor gap.

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 baseline is 4. There are no parameter semantics to clarify, and the description adds no unnecessary parameter-related noise.

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 the dataset's columns, numeric column indicators, row count, and provenance banner, and frames it as the schema-learning entry point. It is not a tautology and offers more specificity than the title, though it doesn't explicitly contrast with all 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 instruction 'Call this first to learn the schema' provides clear contextual guidance on when to use the tool. It does not explicitly name alternatives or exclusion criteria, but the 'first step' framing is sufficient for a schema-discovery 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?

With no annotations, the description carries the behavioral burden. It usefully discloses the matching semantics ('any of the given values') and that output order follows the values order. However, it omits important behavioral details such as exact-match/case sensitivity, handling of missing or duplicate values, and the shape of the returned rows.

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

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

A single sentence with no filler, front-loading the core behavior and ending with a concise usage cue. Every part 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 two-parameter read-style tool with no output schema, the description covers the core semantics, parameter roles, ordering behavior, and intended use case. It could be more complete with explicit differentiation from sibling tools like dataset_search or dataset_row, but it is essentially sufficient 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?

Schema description coverage is 0%, so the description must compensate. It defines both parameters in context: 'column' is the field to match and 'values' are the allowed matching values whose order controls the output. This adds real meaning beyond the raw parameter names and schema constraints.

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: return rows from the Rotazo dataset where a column matches any provided values, preserving the given order. The title adds a clear comparison intent, and the 'X vs Y' framing distinguishes it conceptually from general search or single-row tools, though it does not explicitly name a sibling.

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" provides a clear context for when this tool is appropriate, namely comparing rows side by side. It does not explicitly state when not to use it or mention alternatives, so it falls short of full routing 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 behavioral disclosure burden. It transparently lists the returned provenance fields and implies a safe read-only operation via 'Read this'. It does not mention edge cases like missing licence data, but none are suggested by the tool's scope.

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

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

Two sentences, no filler. The core content is front-loaded, and the usage instruction follows immediately. Every sentence 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 simple, no-parameter provenance tool, the description is complete: it states what data is returned, identifies the dataset, and explains when to use it. No missing information would prevent an agent from invoking 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 the schema already reflects that, so no parameter documentation is needed. The description adds value by explaining what the tool returns rather than parameter details.

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

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

The description names exactly what the tool provides: source, computed date, licence, and citation for the Rotazo dataset. This clearly 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?

'Read this to attribute a figure correctly' gives a clear, concrete use case. It does not explicitly contrast with sibling tools, but the use case is specific enough that an agent can identify when provenance is needed.

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 does disclose the case-insensitive exact match behavior, but it does not state whether the operation is read-only, what happens if no rows match, whether multiple rows are returned, or any pagination/limits. For a query tool, these are important but 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 concise sentence that conveys the core matching rule. It is not padded with irrelevant information and gets to the point quickly. However, the sentence structure is slightly awkward ('The rows of the Rotazo dataset where...'), which could be more direct.

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's simplicity and the lack of annotations or output schema, the description should cover at least the return behavior, edge cases, and read-only nature. It only covers the matching semantics, leaving the agent to guess about multiple matches, no-match behavior, and whether this is a safe operation. This is incomplete for a practical tool.

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

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

Schema coverage is 0%, so the description must explain the parameters. It implicitly defines 'column' as the field to match and 'value' as the target value, but it gives no additional details such as whether column names are validated, how value formatting works, or examples. The description adds minimal semantic value beyond the parameter names.

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 the tool returns rows from the Rotazo dataset where a specified column equals a value exactly (case-insensitive). This is a clear verb-resource pair (look up rows) with a specific matching rule, and the title reinforces the intent. It is distinct from dataset_search which likely implies broader/fuzzy search, but the description does not explicitly contrast with siblings.

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

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

There is no guidance on when to use this tool versus alternatives like dataset_search or dataset_top. The description only explains the behavior, not the appropriate context or exclusions. An agent would have to infer when an exact, case-insensitive match is preferred over other search 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?

With no annotations provided, the description carries the behavioral disclosure burden. It usefully discloses case-insensitive matching, searching across all cells, and the 50-row cap. However, it does not describe output shape, ordering, or behavior when no rows match.

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. It front-loads the core behavior and includes the most important constraint immediately.

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 core search behavior plus row cap are present. However, with no output schema, the description leaves the return format and the meaning of 'rows' somewhat underspecified, and it does not clarify whether the 50-row limit is a default or maximum.

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 query parameter is already described in the schema, and the description adds case-insensitivity and 'any cell' semantics. The limit parameter's meaning is only weakly implied by 'up to 50' and its schema constraints, leaving room for more explicit documentation.

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 of the Rotazo dataset matching a case-insensitive query across cells. This identifies the resource and core operation, though it does not explicitly contrast with 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 description gives no guidance on when to use this tool versus dataset_row, dataset_columns, dataset_top, or the other siblings. There is no mention of exclusions, prerequisites, or alternative tool selection.

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 behavioral disclosure burden. It usefully reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It could also mention edge cases like empty columns or output structure, but it is transparent about the main behaviors.

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 compact sentence that front-loads the result metrics, then adds the input domain and behavior caveat. Every clause provides necessary information with 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?

There is no output schema, so the description compensates by listing the exact statistics the tool produces and key cleaning behavior. It does not explicitly state the return object shape or behavior on fully non-numeric input, but for a simple one-parameter stats tool it is nearly 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 zero percent description coverage for the single `column` parameter, so the description must add meaning. It clarifies that the parameter should identify a numeric column of the Rotazo dataset and implies how non-numeric values are treated. It does not enumerate valid column names, but for a single string parameter this 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 names the resource (a numeric column of the Rotazo dataset), the operation (computing summary statistics), and the exact metrics returned. This clearly distinguishes it from sibling tools like dataset_columns or dataset_row.

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 makes the intended context clear: use this tool when you need count, min, max, mean, median, and sum for a numeric column. It does not explicitly name sibling alternatives or state when not to use it, 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.

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

With no annotations, the description carries the full burden of behavioral disclosure. It states the basic operation but does not explain limit defaults, sort direction behavior, how ties are handled, whether only a subset of rows is returned, or what the output looks like. This leaves important behavioral details unspecified.

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 immediately communicates the core behavior and includes a concrete example ('which is the most/least X') without unnecessary elaboration.

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 absence of annotations and output schema, the description is too thin. It does not clarify the default limit, how ascending interacts with the ranking, or what the returned rows/fields include. These details are necessary for an agent to invoke the tool correctly without additional inference.

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 does add useful meaning by specifying that the column must be numeric and that 'top' means highest or lowest values, but it does not explain the limit parameter's role or its default behavior. The coverage gap is only partially filled.

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 state a specific action ('rank rows') and the resource (a numeric column in the Rotazo dataset). The 'highest (or lowest)' and 'most/least X' phrasing makes the tool's purpose unmistakable and distinguishes it from sibling tools like dataset_row or 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 gives clear context for when to use this tool: to find the most or least extreme rows by a numeric column. However, it does not explicitly name sibling alternatives or state when not to use this tool, so it stops short of full routing guidance.

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