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

No annotations are present, so the description carries the burden. It implies a read-only schema inspection but does not explicitly state side-effect-free behavior or any other behavioral details beyond the returned items.

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, direct sentence lists the exact outputs and usage. No fluff or redundancy, and the structure is well-suited for a schema-introspection tool.

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 covers the essential output elements (columns, numeric indicators, row count, provenance banner) and provides clear usage context ('first'), making it sufficiently complete for a simple tool with no output schema.

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?

There are zero parameters, so schema coverage is trivially 100%. The description appropriately adds no parameter details, matching the baseline for no-parameter tools.

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 column list, numeric flags, row count, and provenance banner, explicitly distinguishing it from sibling tools like dataset_stats or dataset_provenance. It also positions itself as the schema-learning entry point.

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 first to learn the schema,' providing clear when-to-use guidance and implying it precedes other operations on the dataset.

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, and the description does not disclose any behavioral traits such as side effects, permissions, rate limits, or whether the operation is read-only. The description carries full burden but fails to address these aspects.

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, succinct sentence that directly conveys the tool's functionality without any unnecessary words or filler. It is well-structured 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's simplicity and the absence of an output schema, the description provides sufficient context for basic usage. Minor details like behavior when no rows match or exact output format are omitted but can be inferred, so it's mostly 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 description explains that 'column' is used to filter rows and 'values' are the set of values to match, adding meaning beyond the schema's basic types and constraints. It also clarifies that the result order follows the order of 'values', which is not explicit in the 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 the tool retrieves rows from the dataset based on a column matching one of the given values, with explicit ordering. It differentiates from sibling tools like dataset_row (single row) and dataset_search (query-based) by specifying side-by-side 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 phrase 'for "X vs Y" questions' explicitly indicates when to use this tool, providing clear usage context. However, it does not mention when not to use it or directly name alternative tools, so it falls short of fully explicit 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 behavioral disclosure burden. It implies a read-only informational operation and lists the content returned, but it does not explicitly state that no data is modified or describe any side effects. This is adequate but not fully 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 sentences long and front-loads the core content (source, date, licence, citation) before the usage instruction. Every part adds value, and the title reinforces the purpose without redundancy.

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, informational provenance tool, the description is complete: it names the dataset, the key metadata fields, and the intended use case. No additional context is necessary for an agent to 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, so there is nothing for the description to clarify about inputs. The schema is fully covered by being empty, and the description appropriately focuses on what the tool returns 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 states the tool provides provenance metadata—source, computed date, licence, and citation—for the RoughInDesk dataset. It also names the intended action ('attribute a figure correctly'), making its purpose distinct from sibling tools like dataset_row, dataset_stats, or dataset_search.

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 identifies when to use the tool: when attribution is needed. It does not enumerate alternatives or exclusions, but the sibling tool names make the differentiation clear, and the use case is unambiguous.

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 reveals only the case-insensitive exact-match behavior and mentions 'The rows', implying multiple rows may be returned. It does not disclose whether all matching rows are returned, the output format, error behavior, ordering, or whether the column must exist in the dataset.

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 communicates the core behavior efficiently. It is front-loaded with the resource and includes the key matching constraints. It lacks any wasted words, though it could have included a brief note about return value without becoming bloated.

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 exact-match lookup tool, the description is adequate to make a basic call, but gaps remain. There is no output schema or annotations, and the description does not clarify the return shape (full rows vs. projections), whether multiple matches are allowed, or edge cases like no match. Given the sibling tools and the simplicity, it is minimally complete but not robust.

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 only parameter names and types with 0% description coverage. The description adds meaning by stating that 'a column equals a value exactly', which implies 'column' is the field to match on and 'value' is the comparison value. However, it does not elaborate on valid column identifiers, case-sensitivity of column names, or behavior when no match is found, leaving some ambiguity.

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 states the action ('Look a row up by an exact key') and the description clarifies the resource ('RoughInDesk dataset') and the matching rule ('where a column equals a value exactly'). The phrase 'exactly (case-insensitive)' distinguishes it from the sibling dataset_search, which likely implies broader or fuzzy matching. It is clear but lacks a direct verb in the description itself.

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 this tool is for exact-match lookups by emphasizing 'equals a value exactly' and 'case-insensitive'. However, it does not explicitly state when to prefer this over dataset_search or any other sibling, nor does it provide exclusions. Usage must be inferred rather than explicitly guided.

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 key runtime behavior: case-insensitive substring matching across all cells, and a result cap of 50. It does not mention error handling or return format, but given the absence of annotations, the description carries the burden and does so reasonably.

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, directly to the point, with the key information (query matching, case-insensitivity, limit) front-loaded. No unnecessary words or redundancy.

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 nature of a search tool with no output schema or annotations, the description provides sufficient context: what it returns (rows), how matches occur, and the result limit. It does not need to explain return field details or error scenarios for this simplicity level.

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 description adds minimal meaning beyond the schema: it reiterates query matching and explicitly states the result cap of 50, which clarifies limit's purpose. However, it does not explain default values or edge cases for either parameter, and the schema's limit parameter lacks a description.

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 dataset where any cell contains the query, with case-insensitive matching and a limit of 50. This distinguishes it from sibling tools like dataset_row (single row), dataset_stats (aggregates), and dataset_top (top rows).

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 usage for text-based searching, but it does not explicitly contrast with sibling tools or state when not to use it. There is no mention of alternatives or exclusions, so guidance is only implicit.

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 discloses meaningful behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not describe the return structure, but for a read-only statistics tool the disclosed preprocessing behavior is valuable.

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 entire description is one front-loaded sentence: the statistic names come first, followed by a compact parenthetical covering data-cleaning behavior. Every clause earns its place 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?

For a single-parameter, no-output-schema tool, the description covers what the tool computes, which column it applies to, and how dirty data is handled. It stops short of specifying the exact response shape or behavior for a fully non-numeric column, but those are minor gaps.

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 only defines column as a non-empty string, and schema description coverage is 0%. The description compensates by explaining that the column must be numeric and that comma/currency formatting is normalized, adding real semantic meaning to the parameter.

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 (computing count, min, max, mean, median and sum) and the resource (a numeric column of the RoughInDesk dataset). It does not explicitly differentiate from siblings like dataset_top or dataset_compare, but the listed statistics make the tool's 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 implies this tool is for numeric columns and that non-numeric rows will be excluded, giving the agent a sense of when it applies. It does not name alternatives or state when to prefer dataset_top or dataset_compare, 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.

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

No annotations are provided, and the description does not state whether the operation is read-only, what side effects exist, or what the return format is. It is implied that this is a safe query, but transparency is minimal without explicit mention of behavior, such as whether it returns full row objects, just identifiers, or includes any limits on result set size beyond the numeric limit parameter.

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 relatively short, but the use of a dash and an embedded quoted phrase feels slightly awkward and less formal. It conveys the core idea concisely, but the structure could be improved for clarity (e.g., 'Returns the top or bottom N rows of the dataset sorted by a numeric column.'). The formatting is not harmful, but it lacks polish.

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 does not mention the return value format, which is critical because there is no output schema. It also leaves the dataset name ambiguous ('RoughInDesk' could be a placeholder or typo). For a tool named dataset_top, the description should clarify whether it returns the full rows, a subset of columns, or a summary, and ideally state the default order when ascending is not provided.

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 descriptions for only ascending (33% of parameters), while column and limit lack descriptions. The tool description adds that the column should be numeric, giving some context for the column parameter. However, limit is left unexplained, and the interaction between limit and ascending is not elaborated. The partial coverage is partially compensated by the description, so a middle score 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 indicates the tool returns the highest or lowest rows based on a numeric column, which distinguishes it from the sibling tools like dataset_row (single row), dataset_search (search), and dataset_stats (aggregates). The quoted question 'which is the most/least X' reinforces the ranking purpose, though the phrase 'RoughInDesk' appears to be a typo or unclear dataset name.

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 explicit guidance on when to use this tool versus the alternatives. The quoted question provides an implicit hint for top-N queries, but it does not mention any sibling tools or contrast cases (e.g., when to use dataset_stats instead). The description could be clearer about selecting this tool for ordering rows rather than aggregating or searching.

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