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

With no annotations, the description carries the full burden. It discloses the data returned (columns, numeric flags, row count, provenance banner) and implies a read-only operation. However, it does not mention potential failure modes, cost, or whether this is a lightweight metadata call, leaving some behavioral context 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?

A single sentence front-loads the core purpose ('The columns...') and ends with an actionable usage directive. No filler or redundant wording; every element 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, no-output-schema tool, the description explains the return values and when to call it, covering both major agent needs. It could elaborate on the format or content of the provenance banner, but the essentials are present.

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 has zero parameters, so the baseline is 4. The description adds value by clarifying what the tool returns, even though no parameter explanations are needed. It fully compensates for the empty 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 verb-resource relationship: it returns columns, numeric flags, row count, and provenance banner for the Rollupvo dataset. It also provides a clear use case ('Call this first to learn the schema'), which distinguishes its purpose from search/stats tools, though it does not explicitly name 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?

'Call this first to learn the schema' is an explicit instruction about when to use this tool, positioning it as the initial schema-discovery step. It does not mention when not to use it or name alternatives, but the usage context is clear enough for an agent.

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 reveal key behavior: it filters rows where the column matches any of the given values and returns them in the order of the values. However, it omits edge cases like case sensitivity, exact-match semantics, handling of missing values, duplicates, or any limits. This is a moderate disclosure but leaves significant gaps.

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 front-loads the core behavior and includes a usage hint. There is no fluff or repetition; every word adds value. It is well-structured and 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?

Given the tool's simplicity (2 params, no output schema, no annotations), the description covers the essential behavior: filtering and ordering. However, it lacks details about the output format (e.g., whether full rows are returned), error handling (e.g., no matches), and exact matching rules. These gaps are not severe but an agent might need additional information to call it confidently in all scenarios.

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 explains the role of 'column' (the field to filter on) and 'values' (the list of values to match) in context, and adds the ordering behavior. However, it does not clarify whether matches are exact or case-sensitive, or that values are a list (though the schema defines array type). The description adds meaning but not comprehensive detail.

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 a specific dataset (Rollupvo) filtered by a column matching any of the given values, preserving the order of the values. The verb is implicit (retrieve/filter) but the resource and action are clear. It does not explicitly differentiate from siblings like dataset_row or dataset_search, but the 'X vs Y' hint gives context that distinguishes it from generic retrieval.

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 hints at usage with 'for "X vs Y" questions,' implying it is for comparing specific values. However, it does not explicitly state when not to use it, mention alternative tools, or describe scenarios where another sibling would be preferred. 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, so the description carries the full burden. It discloses the specific information returned (source, date, license, citation) and implies a read-only operation. Since there are no side effects or prerequisites, this is sufficient for a simple metadata tool.

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, with two short sentences that front-load the key information (what it provides) and then the use case. There is no waste or repetition.

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 parameters and no output schema, the description fully explains what it provides and why it should be used. Nothing essential is missing for an agent 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?

There are zero parameters, so the baseline is 4. The description does not need to explain parameters, and the schema is trivially covered.

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 provides the source, computation date, license, and citation for the Rollupvo dataset. This distinguishes it from sibling tools that handle data content (columns, rows, stats, etc.), and the verb 'Read this' implies a read/informational operation.

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 a clear use case: 'Read this to attribute a figure correctly.' It provides context for when to use the tool but does not explicitly mention alternatives or when not to use it. The purpose is distinct from siblings, so the context is clear without exclusions.

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 must disclose behavior. It states case-insensitivity and exact matching, but is ambiguous about whether it returns a single row (title says 'a row') or multiple rows (description says 'rows'). It does not disclose return format, error behavior, or pagination.

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 concise sentence with no extraneous information, effectively communicating the core operation.

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 lookup, the description lacks critical context: it doesn't specify the dataset (though it mentions 'Rollupvo' in passing), doesn't clarify if multiple matches are returned, and provides no output schema or return type information. An agent cannot be certain of the 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?

The schema has no descriptions, and the description only implies that 'column' and 'value' are the column name and the value to match. It adds minimal semantic context but does not explain the dataset reference or value formatting, 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 description clearly states the tool retrieves rows from the Rollupvo dataset where a column matches a value exactly, with case-insensitivity. It distinguishes itself from general search and other siblings by emphasizing exactness, though it does not explicitly name an alternative.

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 implies exact-match use but does not state conditions, exclusions, or alternatives, leaving the agent to infer.

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 specifies case-insensitive matching, a maximum of 50 rows, and that any cell is searched. It does not mention sorting, pagination, or handling of no matches, but the core behaviors are transparent. Given the absence of annotations, this is a solid disclosure.

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 immediately states the primary function and constraints. Every phrase adds value: the dataset name, the search behavior, case-insensitivity, and the limit. No fluff 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 simple 2-parameter search tool with no output schema, the description covers the essential aspects: what it searches, how it matches, and the limit. It does not explain return format or edge cases like no results, but given the tool's simplicity, these are minor omissions. Overall, it is adequate for an agent to call 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?

Schema description coverage is 50%: only 'query' has a description ('text to look for in any cell'), while 'limit' has none. The tool description reinforces the query semantics (case-insensitive, any cell) but does not explain the 'limit' parameter's behavior beyond the schema's min/max. It adds some value for query but not enough to fully compensate for the missing limit 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 the tool's function: returning rows of the Rollupvo dataset where any cell contains the query, case-insensitive, up to 50 rows. This is specific with a clear verb ('rows... contain'), a resource (dataset), and a constraint (limit), which distinguishes it from siblings like dataset_row (likely a specific 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 implies usage for content-based searching but does not explicitly state when to prefer this over siblings or mention exclusions. It lacks guidance on alternatives, such as using dataset_row for a known row ID or dataset_top for top rows, and does not clarify whether it is the only way to filter by content.

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 usefully discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted. However, it does not describe the return format, empty-column behavior, or error cases.

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 sentence with no filler; the core statistics list is front-loaded, and the data-cleaning caveats are added at the end. Every clause adds useful 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?

For a simple one-parameter tool with no output schema, the description covers the essential invocation details: target dataset, column type, computed values, and handling of non-numeric data. The lack of an explicit return shape is a minor gap but not a blocker 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 coverage is 0%, so the description must compensate. It adds meaning beyond the bare 'column' string parameter by specifying that the column must be numeric and must belong to the Rollupvo dataset, plus how formatting is preprocessed. For a single parameter, this is sufficient guidance.

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 exact operations (count, min, max, mean, median, sum) and the target resource (a numeric column of the Rollupvo dataset). It is clear and specific, but it does not explicitly differentiate from sibling tools such as dataset_top 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 implies usage: it is for computing summary statistics on a numeric column. However, it does not state when not to use it or mention alternatives, so the agent has to infer the appropriate context from the tool name and sibling list.

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 must carry the full burden of behavioral disclosure. It only states the core operation (rank rows) but does not mention output format, handling of ties, non-numeric columns, or any side effects. This is a significant gap for a tool with no annotation support.

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 front-loads the primary purpose. It avoids redundancy with the title while adding the key detail of 'highest or lowest' and an illustrative example. It is efficient but could benefit from a bit more structure.

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 must explain what the return value looks like. It only says 'rows' without detailing format or content. Additionally, with low schema coverage and no parameter explanations, the description is not complete enough for an agent to call 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% (only 'ascending' has a description). The tool description adds a hint that 'column' must be numeric, but it does not explain the meaning or usage of 'limit' or 'column' beyond that. The description fails to compensate for the low 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 states the verb 'rank' (via 'highest/lowest rows') and the resource 'Rollupvo dataset by a numeric column', making the tool's function explicit. It differentiates from siblings like dataset_row or dataset_search by focusing on ranking rather than fetching or searching.

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 'which is the most/least X', giving clear context for when to use this tool. However, it does not explicitly mention alternatives or when not to use it, so it falls 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.