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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 burden. It discloses what the tool returns (columns, numeric indicators, row count, provenance banner) but does not explicitly state that it is read-only or side-effect-free. For a zero-parameter schema-introspection tool, the risk is low, but the lack of explicit safety language keeps it at a 3.

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 output contents and ends with actionable usage guidance. Every phrase 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?

Given the tool's simplicity, zero parameters, and clear enumeration of returned values, the description is nearly complete for correct invocation. A small gap remains: it does not state the response format or explicitly confirm the operation is read-only, but these are minor for this tool type.

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 are no parameter semantics for the description to clarify. The description appropriately focuses on what the output contains rather than 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 the tool's resource (HeadcountDesk dataset) and contents (columns, numeric flags, row count, provenance banner), and frames it as the first call to learn the schema. It is distinguishable from sibling tools like dataset_row or dataset_stats, though it lacks an explicit verb like 'returns'.

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 sequencing guidance for the agent. It does not explicitly list when not to use this tool or name alternatives, but given the sibling list, the usage context is reasonably clear.

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 carry behavioral disclosure itself. It does reveal meaningful behavior: rows are returned 'in the order given' and matched if the column is 'any of the given values'. However, it does not disclose output format, handling of missing values, case sensitivity, or whether the operation is read-only; these gaps matter without 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?

A single sentence that is front-loaded with the core operation and closes with a usage context. No filler words; every clause adds meaningful 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?

The description is adequate for invoking the tool given the schema constraints, but it omits the return format implied by the title 'Compare rows side by side'. Since there is no output schema, an agent cannot know whether results are presented side-by-side, as a simple list, or in some other structure. This is a notable completeness gap for a tool with no annotations.

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 fully compensates for the two parameters. 'whose column is any of the given values' maps `column` to the dataset field and `values` to the list of match values, and 'in the order given' clarifies ordering semantics for the values array. It does not mention exact-match behavior, but the core roles are 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?

The title supplies the verb 'Compare' and the description specifies the exact resource and behavior: rows of the HeadcountDesk dataset filtered by column matching any of the given values, in the order given. The 'for X vs Y questions' phrase clearly differentiates it from sibling tools like dataset_search or 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 explicitly scopes usage to 'X vs Y' questions, which tells an agent when to select this tool. It does not mention alternative tools or describe when not to use it, but the stated purpose provides clear contextual guidance.

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 states what information is returned (source, date, licence, citation) but does not explicitly confirm it is a read-only operation or describe any side effects. For a metadata tool this is acceptable but not exhaustive.

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, tightly written sentence that front-loads the key facts (source, date, licence, citation) and the intended use (attribute a figure correctly). There is 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?

There is no output schema, so the description must convey what the agent will receive. It lists the components of the return (source, date, licence, citation), which is sufficient for a simple metadata tool. It does not specify formatting or structure, but that is a minor omission given the simplicity of the 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?

The tool has zero parameters, so schema coverage is 100% by definition. According to the rubric, 0 parameters receives a baseline score of 4. The description adds no parameter information because none is needed; the tool requires no input.

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 does: it provides the source, date computed, licence, and citation for the HeadcountDesk dataset. This is a specific verb-resource mapping that distinguishes it from siblings like dataset_columns or dataset_stats, which deal with data content 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 explicitly says 'Read this to attribute a figure correctly', giving a clear context for when to use the tool. It does not mention alternatives or when not to use it, but the purpose is so specific that an agent can infer when it applies without confusion.

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 key matching behavior (exact, case-insensitive) and implies a read-only operation via 'look up,' but it does not explicitly state that no data is modified, nor does it clarify the output format (single row vs. multiple rows) or behavior on no matches. These gaps are notable 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, focused sentence that front-loads the core action and dataset, then adds the matching condition. It contains no filler or redundant information, making it highly efficient for an agent 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?

For a simple lookup with two string parameters and no output schema, the description covers the essential selection logic. However, it leaves ambiguity about whether the result is a single row or multiple rows (title says 'a row', description says 'rows'), and it does not mention the absence of an output schema or how errors/no-matches are handled. These gaps could lead an agent to mispredict the tool's behavior.

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 only implicitly defines the parameters via the phrase 'where a column equals a value,' suggesting that 'column' is a column name and 'value' is the lookup value, but it provides no details on valid column names, value formats, or constraints beyond the schema's minLength. This is insufficient for a low-coverage scenario.

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 the action (look up rows) and the resource (HeadcountDesk dataset) with a precise matching criterion (exact, case-insensitive). This distinguishes it from sibling tools like dataset_search, which likely handles partial or fuzzy matches, so an agent can immediately tell them apart.

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 this tool—when an exact, case-insensitive match is needed—by contrasting with 'exactly' and 'case-insensitive.' However, it does not explicitly name alternatives like dataset_search or state when NOT to use it, leaving the routing decision to the agent's 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 behavioral disclosure burden. It usefully discloses case-insensitive matching, cell-level containment, and a 50-row cap. It does not explicitly state read-only behavior or result formatting, but as a simple search tool these are low-risk 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, front-loaded sentence contains the resource, matching rule, case sensitivity, and row cap with no filler or redundant phrasing. 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 two-parameter search tool, the description covers the core semantics needed to call it correctly: search by cell content, case-insensitive, maximum 50 rows. It lacks explicit clarification of the default limit and return shape, but given no output schema, the description is still reasonably 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 schema documents query as 'text to look for in any cell' but leaves limit without a description. The description adds meaning beyond the schema by specifying case-insensitive matching and the 50-row ceiling, which clarifies the overall search behavior even if the default limit is not stated.

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 (HeadcountDesk dataset) and a precise behavior: returning rows whose cells contain the query, case-insensitively, up to 50. This clearly distinguishes it from siblings like dataset_row, 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 clearly implies when to use the tool: when you want dataset rows by arbitrary cell-text matching rather than by row identity or aggregated statistics. It does not explicitly name alternatives or exclusions, but the intended use case is clear.

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 usefully reveals that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. This adds meaningful behavior beyond the simple title.

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 well-structured sentence with the core output list front-loaded and edge-case handling in parentheses. Every element earns its place with no redundancy or 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 single-parameter tool with no output schema, the description fully covers what the agent needs: which column to pass, what statistics are returned, and how non-numeric data is treated. Nothing essential for a correct invocation is missing.

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 tells the agent the sole parameter 'column' refers to a numeric column in the HeadcountDesk dataset and implies it must be a valid existing column. However, it doesn't specify exact accepted values, case sensitivity, or how to discover valid columns.

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 names the operation ('count, min, max, mean, median and sum') and the resource ('numeric column of the HeadcountDesk dataset'). It clearly distinguishes this from sibling tools like dataset_row or dataset_search by focusing on aggregate statistics.

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 clearly communicates that this tool is for computing summary statistics on a numeric column, which is a distinct use case from the listed siblings. It doesn't explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it appropriately.

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. It discloses the core behavior (highest or lowest rows, numeric column) and the question it answers, but does not mention default ordering, default limit behavior, tie handling, or what the returned result looks like. This is minimally viable but not rich.

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 sentence, front-loaded with the key behavior, and the quoted 'which is the most/least X' gives a helpful mental model. It slightly restates the title, but the added dataset name and use-case frame make it worthwhile.

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 three-parameter tool with no output schema and no annotations, the description should explain return shape and default behavior more explicitly. It mentions rows and numeric ranking, and the schema covers ascending plus limit bounds, so it is workable, but noticeable gaps remain.

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 must compensate. It adds useful meaning by requiring the column to be numeric and by mapping 'lowest' to the ascending behavior, but it does not explain the limit parameter's role or default, leaving a gap in a low-coverage 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 title's 'Rank rows by a numeric column' plus the description's 'highest (or lowest) rows of the HeadcountDesk dataset by a numeric column' gives a concrete operation, resource, and sort semantics. The 'which is the most/least X' phrasing helps distinguish it from siblings like dataset_search and 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 'which is the most/least X' phrase implies the intended use case: finding extreme rows by a numeric column. However, the description never explicitly says when to choose this over dataset_stats, dataset_search, or dataset_row, and it provides no exclusions or alternative routing.

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