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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 behavioral disclosure burden. It discloses what the tool returns, which is useful, but does not explicitly state that it is a read-only, side-effect-free operation. However, the nature of a schema-discovery call makes this risk low.

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 with no filler. The output contents are front-loaded, and the usage directive is concise and actionable.

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, no-output-schema introspection tool, the description is complete. It explains what the tool returns and when to invoke it relative to other dataset operations, which is all an agent needs to call 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 the baseline is 4. The description adds meaning by describing the output contents even though parameter semantics are not applicable.

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 (the Dsarvo dataset) and the specific outputs: columns, numeric flags, row count, and provenance banner. It also frames the tool as a schema-learning step, which helps distinguish it from siblings like dataset_stats or dataset_provenance, though it does not explicitly name them.

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' gives clear contextual guidance on when to use the tool. It does not explicitly state when not to use it or name alternatives, but for a zero-parameter discovery tool this is sufficient 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, the description carries the burden of behavioral disclosure. It does disclose one important behavior: rows are returned in the order of the supplied values. It remains silent on other behaviors such as exact-match semantics, handling of duplicate values, empty results, and output shape, but as a read-only filter tool this is less critical.

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, compact sentence with no unnecessary detail. The typo 'Dsarvo' and the slightly awkward noun-phrase structure prevent a perfect score, but every phrase contributes meaning.

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 2-parameter read tool, the description gives enough to construct a correct call: it identifies the dataset, the filtering column, the values, and ordering semantics. It does not specify the output format or edge cases (e.g., missing values), and there is no output schema to fill that gap, so the completeness is moderate.

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 maps 'column' and 'given values' to the two parameters, but mostly repeats their names. It adds the semantic that the order of values determines the output row order, and that any match (OR) qualifies. However, with 0% schema coverage, it does not compensate for all missing parameter details (e.g., no explanation of min/max constraints beyond 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 identifies the resource (the Dsarvo dataset) and the operation: returning rows where a given column matches any of the provided values, preserving the given order. It distinguishes itself from siblings by emphasizing 'X vs Y' comparison questions, though it lacks an explicit verb like 'retrieves' or 'compares'.

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 use case: comparing specific rows side by side. However, it does not explicitly state when to avoid this tool or mention sibling alternatives, leaving the routing decision mostly 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 provided, the description carries the full burden of behavioral disclosure. It indicates this is a read-only informational tool and lists the fields returned, which is helpful. However, it does not explicitly state that no side effects occur, describe the response format, or mention any access considerations.

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, front-loads the core content areas, and ends with a clear usage directive. Every sentence contributes meaning; there is no redundant or filler text. The minor typo 'Dsarvo' does not undermine overall conciseness.

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 provenance/metadata tool, the description covers the main content—source, date, licence, and citation—and the intended use. With no output schema, a bit more detail about the return format or citation format could be added, but the tool is simple enough that the current description is largely 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 tool has zero parameters, so the baseline of 4 applies. There is no parameter information needed in the description, and the description appropriately focuses on the tool's purpose and output.

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 provides: source, computation date, licence, and citation for the Dsarvo dataset. It is distinguishable from sibling tools like dataset_columns or dataset_search because it is about provenance and attribution, not data access or analysis. It lacks an explicit operational verb like 'returns' or 'fetches', but the intent is 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 phrase 'Read this to attribute a figure correctly' gives a clear use case for when to invoke this tool. It does not explicitly name alternative tools or provide exclusion criteria, but the provenance-focused context is sufficient to guide selection among the dataset_* siblings.

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 behavioral burden. It discloses the key behavior of case-insensitive exact matching, but does not state whether multiple matches are returned, what happens with no match, or whether the operation is read-only.

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 focused sentence that directly states the operation and its key constraint. There is no filler or redundant explanation.

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 with only two required string parameters, and the core lookup behavior is understandable. However, the lack of an output schema and the absence of guidance about multiple matches or return format leave some ambiguity for an agent invoking it.

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 clarifies that 'column' is a dataset column and 'value' is the value to match exactly, which adds meaning beyond the bare schema. However, it does not explain possible value type coercion or case-handling nuances beyond 'case-insensitive'.

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 this tool returns rows from the Dsarvo dataset where a column matches a value exactly, with case-insensitivity explicitly noted. This distinguishes it from dataset_search, which implies broader/fuzzy matching, and from stats/columns 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 exact-match semantics imply this tool is for precise key-based lookups, but the description does not explicitly say when to use it versus dataset_search or other sibling tools. It gives no when-not-to-use guidance or alternative routing.

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. It discloses search case-insensitivity and the 50-row cap, which are useful behavioral traits. However, it does not state any rate limits, whether the search is blocking, error behavior, tokenization, or what happens if more than 50 matches exist (only that results are capped).

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 that states the essential behavior and the limit. Every word earns its place, and the core mechanism is presented first.

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 has no output schema, so the description should clarify what the response looks like, but it doesn't. For a search tool, an agent would benefit from knowing the result format (e.g., rows, objects, count, truncated indicator). The description is adequate for basic invocation but lacks guidance on handling cap or 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%, which means the 'limit' parameter is undocumented in the schema. The description adds the critical constraint 'up to 50', which effectively documents the limit behavior. However, it does not explain the semantics of the query parameter beyond the schema, though the schema already describes it adequately.

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 verb ('search'), a resource ('Dsarvo dataset'), and a key behavior (case-insensitive substring match, max 50 rows). This clearly distinguishes the core action from a generic list or filter. However, it does not explicitly contrast with sibling tools, so it loses one 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?

The description implies when to use it: when you need rows containing a query string, up to 50 results. It does not mention alternatives, exclusions, or when not to use it (e.g., for exact-match lookups, broad listing, or pagination). No sibling differentiation is 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 no annotations, the description carries the full behavioral disclosure burden. It adds meaningful context: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond the schema and helps set expectations about data cleaning. It does not cover error behavior or return format, but for a read-only stats tool this is solid.

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, focused sentence that front-loads the statistics list and then adds relevant edge-case behavior in a parenthetical. Every word contributes value, with no filler 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?

For a tool with one parameter, no annotations, and no output schema, the description is largely complete: it names the target column type, lists the exact returned statistics, and discloses handling of non-numeric and formatted values. It lacks explicit usage guidance and error-handling details, but these are not critical for a simple stats operation.

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 string with minLength 1 and no description (0% schema coverage). The description compensates by clarifying that the parameter is a numeric column of the Dsarvo dataset and hints that non-numeric rows are tolerated. For a single parameter, this adds substantial meaning beyond the bare 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 states a specific operation (computing count, min, max, mean, median, and sum) on a specific resource (a numeric column of the Dsarvo dataset). This clearly differentiates it from siblings like dataset_columns or dataset_top, 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?

The description implies when to use the tool: when summary statistics for a numeric column are needed. However, it does not explicitly mention alternatives, exclusions, or conditions that would route an agent to another tool. With sibling names available and unmentioned, the guidance remains implicit 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, so the description carries the full burden of behavioral disclosure. It mentions highest/lowest rows and numeric columns, but does not disclose default ordering, behavior when values are tied or missing, whether the result is sorted output or ranked with rank values, or what the return shape looks like. These are important gaps for a tool with no annotation safety context.

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 with a useful exemplar phrase. It is not bloated, though the odd 'Dsarvo' wording and the em-dash aside add mild clutter without much extra information.

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 lack of annotations and output schema, the description is incomplete. An agent cannot tell the default limit, how ascending interacts with 'highest/lowest' beyond the schema's brief note, or what the output rows contain. The description works for basic selection but leaves important operational details unspecified.

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?

With only 33% schema description coverage, the description partially compensates by clarifying that 'column' must be numeric and that highest/lowest corresponds to ranking direction. However, the 'limit' parameter is not described at all, and the description does not explain how limit interacts with the ranking or what the default is.

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 it returns the highest or lowest rows of the Dsarvo dataset by a numeric column, and the title reinforces the ranking action. It is distinguishable from sibling tools like dataset_search, dataset_stats, and dataset_row, though it does not name them explicitly. The typo 'Dsarvo' and informal phrasing slightly weaken precision.

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 use case is implied through the phrase 'which is the most/least X', which guides an agent toward ranking questions. However, there is no explicit guidance on when to use this tool instead of siblings such as dataset_search or dataset_stats, and no when-not-to-use conditions are provided.

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