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

With no annotations, the description carries the behavioral transparency burden. It clearly discloses the output contents: columns, numeric indicators, row count, and provenance banner. For a zero-parameter metadata inspection tool, this is sufficient transparency about what the agent will receive.

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 front-loaded in the first sentence, and the usage instruction is in the second, making it easy to parse quickly.

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 schema-introspection tool, the description is complete: it names the dataset, lists the return components, and tells the agent to call it first. No output schema exists, so describing the return values directly is the right and sufficient move.

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 no parameters and the schema is empty, so there are no parameter semantics to explain. The baseline of 4 for a zero-parameter tool applies, and the description does not need to compensate for any undocumented inputs.

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 what the tool returns: columns, numeric flags, row count, and provenance banner for the Dispatchzo dataset, and frames it as a schema-learning step. It does not explicitly distinguish itself from sibling tools by name, but 'Call this first' positions it as the initial schema-introspection tool.

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?

It gives explicit usage timing: 'Call this first to learn the schema.' This tells an agent when to use it in a workflow, though it does not state when not to use it or name alternative tools such as dataset_stats or dataset_provenance.

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 transparently conveys the selection rule and ordering behavior, which is useful. But it does not disclose exact-match semantics, handling of missing values, duplicate values, or how results are returned, which matters for a comparison 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 a single efficient sentence that packs the core behavior, ordering, and intended use case without wasted words. It is front-loaded with the primary action and scoping.

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 tool, the description is mostly adequate, but the lack of annotations and output schema means it should clarify more about return format and edge cases. It does not mention what happens when values are absent from the dataset or whether the output is genuinely 'side by side' as the title suggests.

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 implies both 'column' (the field to filter on) and 'values' (the values to match) and adds that results are in the order of the given values. However, it is vague about whether values must be exact strings and does not clarify the relationship between the two parameters beyond the filter concept.

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 action: return rows from the Dispatchzo dataset filtered to those whose column matches any of the provided values, preserving the given order. This distinguishes it from siblings like dataset_row (single row) and dataset_search (broader search), though it does not explicitly name alternatives.

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' gives a clear use case, implying comparison of specific rows. However, it does not state when to prefer this over dataset_search, dataset_row, or dataset_top, nor does it provide exclusions or alternatives.

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 explaining behavior. It effectively discloses the informational content returned, but it does not explicitly state read-only behavior, output format, or any side effects. For a simple metadata tool 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?

Two short sentences deliver the full purpose with no filler. The most important content—source, date, licence, citation—is front-loaded.

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 metadata tool, the description names the exact items returned and explains when to use it. It could be slightly richer by noting the output format, but nothing critical is missing for tool selection.

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 schema already fully covers the input space. 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 states exactly what the tool provides: source, computation date, licence, and citation for the Dispatchzo dataset. It is clearly distinct from sibling tools like dataset_search or dataset_stats, which focus on data access rather than provenance.

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 usage context: 'Read this to attribute a figure correctly.' It does not explicitly name alternatives or when-not-to-use cases, but no sibling tool serves this provenance/citation purpose.

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 does disclose a useful behavioral trait (case-insensitive exact match), but it does not clarify whether multiple matching rows are returned or only one, how unmatched queries behave, or whether the operation is read-only. This is a notable gap for an unannotated 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 a single sentence with no filler. It conveys the core purpose, matching mode, and case-sensitivity in minimal words, and the title reinforces the action. 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 tool with no output schema and no annotations, important contextual details are missing. The title says 'row' (singular) while the description says 'rows' (plural), creating ambiguity about the expected result set. There is no mention of error behavior, performance implications, or what happens if no row matches. The tool is simple, so this is not a severe failure, but it is incomplete.

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 effectively explains both parameters: 'column' is the field to compare against, and 'value' is the exact string to match, with case-insensitive equality. This adds meaningful semantic context that the bare schema lacks.

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: retrieve rows from a dataset by exact column match, with case-insensitivity. This clearly differentiates it from siblings like dataset_search, which implies broader/fuzzy matching, and dataset_top or dataset_stats, 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?

Usage guidance is only implied: the phrase 'exactly (case-insensitive)' suggests this tool is for precise key-based lookups rather than fuzzy searches. However, there is no explicit statement of when to prefer this tool over alternatives, nor any exclusion criteria.

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 does disclose case-insensitive matching and the 50-row upper bound, which is useful, but it does not address default limit behavior, result structure, or any side effects. For a simple search tool this is moderate 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 a single, well-structured sentence that front-loads the core behavior and includes the key constraint (case-insensitive, up to 50). Every word earns its place with no filler 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 relatively simple tool with two parameters and no output schema, the description covers the essential behavior needed to select and invoke it: what is searched, how matching works, and the result cap. It does not explain the default limit or return format, but the absence of an output schema lowers the burden somewhat.

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 documents the query parameter but leaves the limit parameter without descriptive text. The description adds meaning by clarifying that matching is case-insensitive and that results are capped at 50 rows, which maps to the limit behavior. It still omits the default limit value and whether matching is substring-based versus exact, so it is not a 5.

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: returns rows of the Dispatchzo dataset where any cell contains the query, with case-insensitivity and a 50-row cap. This clearly distinguishes it from sibling tools like dataset_columns or dataset_stats, which have 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 usage: use this tool when you need to find rows by matching text in any cell. However, it does not explicitly mention when to prefer this over alternatives, nor does it state exclusion cases or when another sibling tool would be more appropriate.

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 disclosure and does a good job: it reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It doesn't mention edge cases like empty columns or the exact output shape, but the listed statistics plus these behavioral notes are substantial.

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 tight sentence that packs the output list and key behavioral details into an efficient parenthetical. There is no redundancy, and the title complements rather than repeats the description.

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—one parameter and no output schema—the description is mostly complete: it enumerates the six statistics and explains how malformed data is treated. It would be slightly better if it specified the output format or behavior when there are no valid numeric rows, 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 provides a `column` string with no description (0% schema description coverage), so the description must compensate. It does so by clarifying that the column must be numeric and that non-numeric rows are handled. For a single simple parameter, this is sufficient to guide invocation, though it doesn't specify valid column names or case sensitivity.

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 identify the tool as computing summary statistics for a numeric column of the Dispatchzo dataset, listing the exact statistics returned (count, min, max, mean, median, sum). It is distinct from sibling tools like dataset_columns or dataset_top, 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 description implies usage by mentioning 'a numeric column', and the parenthetical about commas/currency and non-numeric rows gives context about when the tool is useful. However, it never explicitly states when to prefer it over alternatives such as dataset_top or dataset_search, nor does it provide 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?

With no annotations, the description must disclose behavior on its own. It states that it returns the highest or lowest rows by a numeric column, which covers the core behavior. However, it does not mention default ordering, the meaning of limit, or potential limitations such as ties or non-numeric column handling.

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 with a clarifying use-case phrase. It is front-loaded with the operation and resource, contains no filler, and every part adds 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 ranking tool, the description covers purpose and the numeric column requirement, and the schema covers limit constraints and ascending default. Still, there is no mention of output shape, no usage guidance versus siblings, and no handling of edge cases, leaving the overall context only minimally 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 only 33%, so the description should compensate. It adds the 'numeric column' constraint and hints at ascending/descending via 'highest or lowest,' but it does not explain the limit parameter at all, nor does it clarify how the three parameters interact. The schema alone leaves too much implicit.

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 operation precisely: 'Rank rows by a numeric column.' The description reinforces this with 'The highest (or lowest) rows ... by a numeric column' and gives a concrete use case ('which is the most/least X'). This clearly distinguishes it from siblings like dataset_search 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 finding extremes or top/bottom rows, but it never explicitly says when to prefer this tool over alternatives, nor does it mention sibling tools or exclusion conditions. The use-case phrasing gives context, but there is no direct guidance.

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