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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 does explain what information is returned and implies a read-only metadata operation, but it does not confirm side-effect-free behavior, output structure, or any edge cases. This is acceptable 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 short sentences with no filler. The output contents are listed first, and the actionable guidance 'Call this first to learn the schema' is front-loaded and memorable. 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 zero-parameter, low-complexity introspection tool, the description covers the key information an agent needs: what the tool returns and when to call it. The lack of an output schema means more exact return-format details are not specified, but the description is sufficient for correct initial selection and 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 for the description to clarify about arguments. The schema is an empty object with 100% coverage, and the baseline of 4 applies 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 names a specific resource (Capanix dataset) and enumerates the exact output content: columns, numeric flags, row count, and provenance banner. It also tells the agent to 'Call this first to learn the schema,' which clearly distinguishes it from siblings like dataset_row 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 gives explicit usage guidance: 'Call this first to learn the schema.' This tells the agent when to use the tool, though it does not explicitly name alternatives or state when not to use it. Still, the sequencing advice 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, the description carries the behavioral burden. It discloses the core selection logic (column value membership) and the non-default behavior that results are returned in the order the values were given. It does not mention edge cases such as matches not found or exact/case-sensitive matching, but the read/comparison nature is clear.

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 result and appends the use case, with no redundant words. The wording 'whose column is any of the given values' is grammatically awkward but still efficient.

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 tool with no output schema, the description covers the query logic and intended use, but omits what the returned rows look like, the matching precision, and behavior for empty or partial results. This is adequate for invoking the tool but leaves interpretation of results somewhat underspecified.

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 links 'column' to the field being matched and 'given values' to the value list, and adds the ordering semantics tied to the values parameter. It does not explicitly name the parameters or describe constraints like maxItems, which remain only 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 identifies the tool's function as returning dataset rows filtered by a given column matching any of the supplied values, with order preservation. Although phrased as a noun phrase rather than an explicit verb, it clearly distinguishes from siblings like dataset_search and dataset_row via the 'X vs Y' comparison intent.

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' tells the agent when this tool is appropriate: comparing two or more specific rows. It does not explicitly name sibling tools as alternatives, but the visible sibling list plus the targeted use case provide adequate context.

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 enumerates exactly what information will be returned and implies a read-only metadata lookup through 'Read this'. It does not specify output formatting or whether the provenance is static, but for a zero-parameter metadata tool the disclosure 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?

Two short, useful sentences: the first states the contents of the provenance information, and the second states the intended use. There is no redundant or filler language.

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, no-output-schema tool, this description is complete. It tells the agent what data is returned, why to call the tool, and the context in which it is appropriate, without needing more detail.

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 an empty schema, so there is no parameter semantics to explain. The description correctly focuses on what the tool returns rather than how to configure it.

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 the source for provenance information—source, computation date, licence, and citation—and ties it to the concrete task of attributing a figure. This is distinct from sibling tools like dataset_columns or dataset_search, which operate on data content rather than origin.

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 says to use this tool to attribute a figure correctly, giving a clear trigger for when it is relevant. It does not mention exclusions or alternative tools, but the provenance/citation purpose is naturally disjoint from the data-access-oriented sibling 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, the description carries the full burden of behavioral disclosure, and it does disclose meaningful matching behavior — exact, case-insensitive comparison. But it leaves a genuine ambiguity: the title says 'a row' (singular) while the description says 'The rows' (plural), so an agent cannot tell whether one or many matching rows are returned, and no-match behavior is unmentioned. That ambiguity keeps this at a 3 rather than higher.

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?

One compact sentence plus a direct title, with the key matching semantics (exact, case-insensitive) front-loaded. Minor awkwardness in the grammar ('The rows of the Capanix dataset where...') and some redundancy between the title's 'exact key' and the description's 'equals a value exactly' prevent a 5.

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, the description covers the core behavior and identifies the dataset. Yet with no output schema and no annotations, it should also state whether a single row or multiple rows are returned and what happens when no match is found; it does neither. These two missing details make the definition adequate but not 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?

Schema coverage is 0% and the parameters have no descriptions, so the description must supply the relationship between the two parameters. It does, by stating that a column equals a value, implying column is the match field and value is the target. It stops short of examples, formats, or clarifying whether value acts as a primary key, so it only partially compensates for the schema gap.

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 a specific operation — looking up a row by exact key — and the description narrows the semantics with 'equals a value exactly (case-insensitive).' This differentiates it from search-like siblings such as dataset_search by committing to exact matching. It earns a 4 rather than 5 because the description is slightly tautological ('rows... where a column equals a value') and never names a sibling it is not.

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 'equals a value exactly (case-insensitive)' implies the tool is for exact-match lookups and implicitly contrasts with broader search. However, there is no explicit guidance about when to prefer this tool over sibling tools like dataset_search or dataset_top. Usage is left largely to inference, which is the definition of an implied-but-not-stated guideline.

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 well by stating that matching is case-insensitive, that the search applies to all cells, and that the result is capped at 50 rows. It does not mention return format or ordering, but these are not critical for a simple read-only search 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?

A single dense sentence that front-loads the resource and includes the key behavioral modifiers: case-insensitivity and result cap. Every word earns its place, and there is no repetition of schema fields or redundant 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 two-parameter search tool with no output schema, the description covers input semantics, matching behavior, and the result cap. It lacks explicit return-value details and sibling-tool comparison, but the tool is simple enough that an agent can invoke it correctly with the given 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 query parameter is already documented in the schema as 'text to look for in any cell,' and the description reinforces that while adding case-insensitivity. The limit parameter has only min/max constraints in the schema; the description's 'up to 50' partially clarifies its effect but does not explicitly explain how limit controls the result count or what happens when it is omitted. With 50% schema coverage, the description compensates only moderately.

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 defines the tool's behavior: it returns rows of the Capanix dataset where any cell contains the query, case-insensitively. This unmistakably distinguishes it from sibling tools like dataset_row, dataset_columns, and dataset_stats without needing to inspect schemas. Though there is no explicit verb, the meaning is unambiguous and resource-specific.

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 guidance on when to use dataset_search versus sibling tools such as dataset_row or dataset_top, and it gives no exclusions or alternative conditions. The intended use is implied by the tool name and the phrase 'rows whose cells contain the query,' but the description never explicitly states when this tool should be preferred over others.

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?

There are no annotations, so the description carries the burden. It discloses that non-numeric rows are excluded and counted, which is useful behavioral context. It doesn't mention edge cases like all-non-numeric columns or potential errors, but the disclosed behavior is a solid start.

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, information-dense sentence that front-loads the list of statistics and then clarifies data handling. It's concise and every clause earns its place, though it could be slightly clearer with a period after the parenthetical.

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, the description covers the key computation and data cleaning behavior. It lacks details on return format, but with no output schema, a brief mention of what the output looks like could improve completeness. Overall, it's adequate for a simple stats 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 description coverage is 0%, but the description explains the meaning of the column parameter by stating it must be a numeric column and mentions handling of formatting. This adds meaning beyond the schema's minimal type and minLength, but doesn't fully specify the expected format or 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 clearly states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column, which is a specific verb and resource. It could be distinguished from siblings like dataset_top or dataset_search, though it doesn't name them explicitly, so it loses a point for not differentiating.

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 numeric columns and mentions handling of currency and grouping commas, giving context. However, it doesn't explicitly state when to prefer this over sibling tools or provide exclusions, so guidance is only implied.

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 adds useful context that rows are ranked by a numeric column and supports both highest and lowest directions. However, it does not disclose tie-breaking behavior, treatment of non-numeric or missing values, the default limit when omitted, or the shape of the returned 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?

The description is compact, with the core idea front-loaded and no filler. The em-dash quote is slightly stylized and the grammar is a bit off, but it earns its place by illustrating the intended query. 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 tool with no annotations, no output schema, and only 33% parameter coverage, the description is too thin. It omits important operational details such as what the returned rows look like, the default limit behavior, how ties are resolved, and how invalid columns are handled. An agent would need to infer or probe these behaviors.

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% (only 'ascending' is described). The description partially compensates by clarifying that 'column' must be numeric and implying that 'limit' controls how many top/bottom rows are returned. It does not explain the default limit or the exact expected column-name format, so compensation is incomplete.

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 the highest (or lowest) rows of the Capanix dataset ranked by a numeric column, and the 'which is the most/least X' framing conveys the query intent. It does not differentiate from sibling tools, but no sibling tools were provided. The phrasing 'rows ... is' is slightly awkward but does not obscure meaning.

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 frames the user scenario this tool answers: 'which is the most/least X'. This gives clear context for when to invoke it. It does not provide when-not-to-use guidance or mention alternatives, but with no sibling tools listed, there is limited opportunity to do so.

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