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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 discloses the exact data returned—columns, numeric indicators, row count, and provenance banner—and implies a read-only schema inspection. It omits explicit side-effect or error details, but for a no-parameter schema tool that is a minor gap.

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 with no filler: the first lists the returned content, the second gives a clear usage directive. It is front-loaded and every sentence 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 tool with no output schema, the description covers what the agent will receive and when to call it. It leaves the exact return format and the relationship to dataset_provenance unspecified, but the essential context is 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 is empty, so the baseline is 4. The description appropriately adds context about what the dataset schema contains rather than needing to explain parameters.

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 lists what the tool returns—columns, which are numeric, row count, and provenance banner—for the Handovra dataset. It lacks an explicit verb like 'returns', but 'learn the schema' makes the purpose unmistakable.

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 usage instruction that establishes this tool as the entry point before other dataset tools. It does not name alternatives or state when not to use it, so it stops 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.

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

With no annotations, the description must carry the behavioral disclosure. It clearly states that rows are selected by column-value membership and that ordering follows the input values. It does not mention edge cases like missing values or exact-match semantics, but the core behavior is 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, lean sentence with no redundant content. The key behavior is front-loaded, and the use-case tag is appended efficiently. Every part 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?

Given the simple two-parameter schema and no output schema, the description covers the essential calling semantics. Minor gaps include not explaining the return format or how to handle no matches, but the description is sufficient for a correctly scoped call.

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 maps the parameters to behavior: 'column' is the field to match on, and 'values' are the values to match, with their order affecting the result. This adds meaning beyond the raw schema, though it could be more explicit about parameter roles.

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 uses specific verbs and resources: it returns rows from the Handovra dataset filtered to a column by given values, preserving the provided order. The 'X vs Y' phrasing gives a clear use case and distinguishes it from sibling tools like dataset_row 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 provides a clear context for use: 'for "X vs Y" questions' indicates when one would want to compare specific rows. It does not explicitly name alternatives or state when not to use the tool, but the use case is concrete enough.

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, but the description conveys that this is a read-only metadata lookup and enumerates the returned information: source, date, licence, and citation. It does not mention authentication or rate limits, but those are minor for a zero-parameter informational 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 two sentences long, front-loaded with the key content, and contains no filler. Every word 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 zero-parameter provenance metadata tool, the description is complete: it identifies the dataset, the specific metadata fields available, and the purpose (attribution). No output schema or deeper behavioral details are 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?

The tool has no parameters, so the schema is trivially complete and there are no parameter semantics to document. The description appropriately focuses on what the tool returns 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 states what the tool provides: source, computed date, licence, and citation for the Handovra dataset. It also gives the intended action ('Read this to attribute a figure correctly'), which distinguishes it from the sibling data-access 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 description gives a clear usage context: use this tool when attribution or citation is needed. It does not name alternatives or explicitly state when not to use it, but for a provenance metadata tool the guidance is sufficient.

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. It does disclose the core matching behavior (exact and case-insensitive) and that results are rows of the dataset. However, it does not cover edge behavior such as no-match results, multiple matches, or column-name validation.

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 compact sentence with no filler; the core condition is stated directly. It is efficient, though the phrasing is a noun phrase rather than an imperative.

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 lookup the core invocation is clear, and the exact/case-insensitive behavior is stated. But there is no output schema, no mention of count/return shape, and no routing to sibling dataset_columns for discovering valid column names, so an agent has some gaps to resolve.

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 relate 'column' and 'value' to the equality condition, and adds the case-insensitive nuance. It does not tell the agent where valid column names come from (e.g., dataset_columns) or value formatting, so compensation is partial.

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 a lookup operation on the Handovra dataset by exact column-value equality, and the 'exactly (case-insensitive)' qualifier distinguishes it from the fuzzy dataset_search sibling. Minor inconsistency: title says 'a row' while description says 'rows', but the meaning 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 description implies the right use case (exact-match lookups), but it never explicitly tells an agent when to prefer this tool over dataset_search or other siblings, nor does it mention when not to use it. The exact-match qualifier is the only routing signal.

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 disclosure burden. It does disclose case-insensitive matching and a 50-row cap, which are useful. However, it does not mention output format, ordering, pagination, or whether the query is a literal substring, leaving some behavioral uncertainty.

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 front-loads the main operation ('Rows ... contain the query') and appends key constraints. 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?

The tool is simple and the description conveys the core search behavior, resource, case-sensitivity, and result cap. However, without an output schema, it leaves out what the returned rows look like, whether all columns are included, and behavior around the default limit. For a basic search tool this is adequate but not fully 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 already describes the 'query' parameter well. The description adds the case-insensitive behavior and a 50-row maximum, which helps clarify 'limit', but the 'limit' parameter itself is not explicitly described as controlling the number of returned rows. With 50% schema coverage, the description partially compensates but does not fully define both parameters.

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: return rows of the Handovra dataset that contain the query text. Specific behaviors such as case-insensitive matching and the 50-row cap are included, and the search function is clearly distinct from sibling tools like dataset_stats or dataset_columns.

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 you need to find dataset rows containing a certain text query. However, it does not explicitly compare with alternatives or state when not to use it, so the guidance is more implicit 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?

With no annotations, the description carries the behavioral burden and does disclose meaningful processing: grouping commas and currency are tolerated, and non-numeric rows are excluded and counted. It could go further by describing output format or behavior on missing/empty columns, but the key preprocessing traits are present.

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 dense sentence leads with the computed statistics and adds the key parsing caveats. There is no filler or repetition of schema fields.

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 one-parameter tool, the description is nearly sufficient: it lists the returned statistics and the data-cleaning behavior, and the schema defines the single input. It lacks an explicit output shape, but the enumerated statistics effectively communicate what the agent will receive.

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 provides only a required string named `column` with no description, so the description must add meaning. It does partly: the column is expected to contain numeric values, and it may include grouping commas or currency symbols. It does not specify valid column names or how to obtain them, but the sibling dataset_columns can fill that gap.

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 the resource (numeric column of the Handovra dataset) and the exact operation (compute count, min, max, mean, median, sum). This clearly distinguishes dataset_stats from siblings such as dataset_columns, dataset_top, and dataset_row.

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 intended use is implied: for summary statistics of a numeric column. However, no explicit guidance is given about when to prefer this tool over dataset_top, dataset_columns, or dataset_search, and no exclusions are stated.

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, but it only states high/low ranking by a numeric column. It omits default limit behavior, handling of ties or missing values, and the read-only nature of the operation.

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 front-loads the core behavior with no filler. It is appropriately sized and 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 tool with no annotations and no output schema, the description leaves out important context: the default limit, output format, and when to choose this over sibling tools. It is minimally viable but under-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 only 33% (only 'ascending' is documented). The description adds that the column must be numeric and that results are highest or lowest, which helps map the column and ascending parameters. However, it does not clarify limit defaults or explicitly connect 'highest/lowest' to the ascending flag.

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 clear action (rank/top rows) on a specific resource (Handovra dataset) and explains the 'highest or lowest' semantic with an example question. It does not explicitly differentiate from sibling tools like dataset_stats, but the core purpose 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?

No guidance is given on when to use this tool versus alternatives. The phrase 'which is the most/least X' implies a use case but never mentions when dataset_stats or dataset_search 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.