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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 behavioral disclosure burden. It reveals the data fields it returns (columns, numeric flags, row count, provenance banner) and implies a read-only metadata operation, but it does not explicitly state side-effect-free behavior, output formatting, or error behavior.

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 contain all essential information with no filler. The first sentence enumerates the return components that matter, and the second sentence gives the usage directive.

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 parameterless metadata tool, the description covers the main expected outputs and the primary usage timing. It does not describe the exact output structure or explicitly guarantee read-only behavior, but given the sibling set and the lack of an output schema, 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 tool takes zero parameters, so the description has no parameter meanings to add beyond the schema. Per baseline for no-parameter tools, 4 is appropriate; the description's mention that this is the schema-learning call adds mild contextual value.

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?

Description states a specific resource ('Ppmly dataset') and concrete returned content: columns, numeric flags, row count, provenance banner. It distinguishes itself from 'dataset_row', 'dataset_search', and 'dataset_stats' by framing its output as the schema overview to request first.

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 explicit instruction 'Call this first to learn the schema' gives a clear use case. It does not name sibling alternatives or when-not-to-use it, but with zero parameters and an overview purpose, the positioning as the initial schema call 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose the key behavior: filtering rows by a column matching any of the given values, and preserving the order of the values. However, it does not mention error handling (e.g., missing column), return format details, or potential side effects (though likely read-only). The description is functional but lacks depth beyond the core filtering and ordering.

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 efficiently states the core operation and the intended use case. There is no redundant information, and every word earns its place. It is well-structured for quick comprehension.

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 main functionality but leaves gaps. It does not explain the return format (though the title hints at 'side by side'), nor does it address error conditions or edge cases (e.g., what happens if a value is not found). For a tool with no output schema, more detail on what the agent should expect in the response would be helpful, but the core filtering and ordering are specified.

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 clarify the parameters. It implicitly explains 'column' as a column of the dataset and 'values' as the list of values to match, and that the order of values matters. This adds meaning beyond the raw schema, but it could be more explicit about what 'column' refers to (e.g., a column name) and whether matching is exact or case-sensitive. It partially compensates for the lack of schema descriptions.

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 rows from the Ppmly dataset filtered by a column matching any given values, in the provided order. It distinguishes itself from siblings like dataset_row (which likely fetches a single row) by indicating a multi-row comparison use case ('for X vs Y questions'). However, it doesn't explicitly name a sibling or contrast the tool's specific role beyond the title.

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 context with 'for X vs Y questions', which is a clear use case. But it does not explicitly state when to use this tool instead of alternatives like dataset_search or dataset_row, nor does it mention when not to use it. 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, so description carries the burden. It describes the content returned (source, date, licence, citation), making the behavior transparent. Doesn't explicitly state read-only behavior, but it's implicit with a metadata retrieval 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?

Two concise sentences that deliver the essential info without fluff. 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 no parameters and no output schema, the description fully covers what the tool does and what it returns. Nothing is missing for a caller to use 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?

No parameters exist (schema coverage 100% with empty schema), so the description doesn't need to elaborate on any. Perfectly clear.

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?

Clearly states the purpose: provides dataset provenance info (source, date, licence, citation). Distinct from sibling tools like dataset_columns or dataset_stats, which handle other aspects.

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 says 'Read this to attribute a figure correctly', giving a clear when-to-use scenario. Although it doesn't contrast with siblings, the intended use is unmistakable.

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 states the matching rule (exact, case-insensitive) but omits what happens when multiple rows match, whether one or all are returned, or the behavior for no match. This is a minimal but non-tautological 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?

A single sentence that leads with the core action and includes the key qualifier (case-insensitive). No filler or redundant phrasing; every word contributes to 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?

The tool is simple, but the description lacks explicit differentiation from siblings and does not describe the return format or edge-case behavior. Given no annotations and no output schema, a bit more context (e.g., all matches returned, empty result handling) would make it more 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 description coverage is 0%, so the description must explain the parameters. It does by phrasing 'a column equals a value', clarifying that 'column' is the field name and 'value' is the lookup value. However, it doesn't add format details or 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 states the tool returns rows from the Ppmly dataset where a column exactly equals a value, and the case-insensitive qualifier adds specificity. It is distinct from siblings like dataset_search (which implies fuzzy search) and dataset_compare, so an agent can readily tell what it does.

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 exact matching but does not explicitly state when to use this tool over siblings. It never mentions 'use this for exact matches, use dataset_search for fuzzy', leaving the decision to inference from the wording.

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 adds meaningful behavior: matches are case-insensitive and results are capped at 50 rows. It does not mention ordering or pagination, but for a simple read-oriented search these are not critical omissions.

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, tightly worded sentence that front-loads the core capability and includes key constraints. Every element 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 simple two-parameter search tool with no output schema, the description provides the essentials: what is searched, how matching works, and the result cap. It could mention result ordering or behavior on empty matches, but these are minor and not necessary 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 50%: the query parameter is documented, while limit only has type/min/max without a description. The description partially clarifies limit by stating 'up to 50', but does not specify how the limit parameter interacts with the default or user-specified cap. It adds some value but doesn't fully compensate for the gap on limit.

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 Ppmly dataset containing the query in any cell, with case-insensitivity and a cap of 50. This is a specific verb/resource pairing that distinguishes it from siblings like dataset_columns, dataset_stats, and dataset_top.

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 the tool should be used when you need to find rows by free-text cell content, but it does not explicitly state when to prefer it over siblings or mention exclusions. It provides functional context but leaves alternative selection 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, the description carries the full behavioral burden. It discloses two non-obvious behaviors: grouping commas/currency are parsed, and non-numeric rows are excluded and counted. The phrase 'excluded and counted' is slightly ambiguous, and return format is not described, but the disclosed edge-case handling is strong.

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 compact, front-loaded sentence: the computed statistics appear first, with parsing edge cases in a parenthetical. There is no filler or redundant restating of the tool name.

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 statistics tool, the description covers the target column, the computed values, and important parsing behavior. The main gap is that no output schema exists and the description does not explicitly describe the return shape, but the enumerating of output statistics partially compensates.

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 schema only says 'column' is a string. The description adds that the column must be numeric and that formatted values are handled, which helps, but it does not specify whether the value should be an exact column name, display label, or key, nor how ambiguity is resolved.

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 exactly what the tool does: it computes count, min, max, mean, median, and sum for a numeric column of the Ppmly dataset. This enumerated output list clearly differentiates it from search, compare, provenance, and top-value 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 usage context is implied: call this when aggregate numeric statistics for a column are needed, and the description notes handling of formatted numbers. However, it never explicitly contrasts this with sibling tools or states when not to use it, leaving routing 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 conveys the core behavior (sorting by a numeric column, ascending or descending) and the constraint that the column must be numeric. However, it does not disclose the output format, tie handling, default sort order (though implied by schema), or behavior when the column is non-numeric. This is a moderate gap given no annotations.

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 is front-loaded with the core purpose. It contains no filler and every word contributes to understanding the tool's function, making it highly 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?

The tool is relatively simple with three parameters, but there is no output schema and no annotations. The description explains the ranking behavior but omits details about the return value (e.g., does it return full rows or just the column?), handling of ties, and default ordering. While adequate for basic use, an agent might need more information to fully anticipate results.

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), so the description must compensate. It adds the important semantic that the column must be numeric, which is useful. It does not elaborate on 'limit' or the required 'column' beyond what the schema already specifies, but the numeric constraint is a meaningful addition.

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 purpose: returning the highest or lowest rows of a dataset ordered by a numeric column. It uses a specific verb ('highest/lowest rows') and identifies the resource ('Ppmly dataset') and the key attribute ('numeric column'). This distinguishes it from siblings like dataset_search or dataset_stats, which address different queries.

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 ranking questions ('which is the most/least X') but does not explicitly state when to use this tool versus alternatives. There is no mention of when not to use it or any comparison to sibling tools, 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.