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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 clearly describes an inspection/metadata operation and lists the exact pieces of information returned. It implies non-mutating behavior through the phrase 'learn the schema', though it does not explicitly state that no data is modified.

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 wasted words. The first sentence front-loads the output contents, and the second gives a clear usage directive. Every clause 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 metadata tool with no output schema, the description fully covers what the agent can expect to receive and when to call it. It provides enough context for correct invocation without being verbose.

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 no parameters, so there are no parameter semantics to document. The phrase 'of the Disclovo dataset' clarifies that the tool operates on a fixed/predefined dataset. This matches the baseline 4 for a zero-parameter tool.

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 columns, row count, and provenance banner — for the Disclovo dataset. It lacks an explicit imperative verb like 'Returns', but the intent is unmistakable. It also helps differentiate from sibling tools by framing this as the schema-learning first call.

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 explicit usage timing: call this first to learn the schema. It does not explicitly name sibling alternatives or state when not to use it, but the sibling names and the 'first' instruction make the intended workflow 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 carries the full behavioral disclosure burden. It does reveal important behavior: exact column-value matching ('any of the given values') and output ordering ('in the order given'). However, it does not state whether the operation is read-only, how rows are returned, or how edge cases are handled.

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 that packs the resource, filter condition, ordering behavior, and use-case context with no filler. It is front-loaded and every phrase 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?

For a simple two-parameter selection tool, the description conveys the essential filtering and ordering semantics and identifies the output as rows. Since there is no output schema, the description's 'rows...' phrasing covers the return type, though row projection and edge-case behavior remain unspecified.

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 is the only semantic source for the parameters. It explains that 'column' is the field to match and 'values' are the allowed cell values, and it adds the important ordering behavior. It does not detail case-sensitivity or duplicate handling, preventing a perfect score.

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 identifies the resource (rows of the Disclovo dataset) and the filtering behavior (rows whose column matches any given values) with the order-preserving detail. It clearly signals comparison use via 'X vs Y' questions, but it lacks an explicit action verb like 'returns' or 'compares', so it stops short of a 5.

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' provides a clear situational context for when to use this tool. It does not explicitly name alternatives or exclusions, such as when to use dataset_row or dataset_search instead, so it does not fully meet the top tier.

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 disclosure burden. It reveals exactly what data the call returns and signals a read-only, side-effect-free operation via 'Read this.' It does not describe the return format, but for a zero-parameter metadata lookup 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 short sentences, front-loaded with the content, then the use case. No filler and no repetition of schema 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 parameterless provenance tool with no output schema, the description covers all essential information: source, date, licence, and citation. Nothing required for 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?

The input schema has zero properties, and the description confirms there is nothing to configure. Baseline for a parameterless tool is 4; no additional parameter semantics are 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 the exact resource (Disclovo dataset) and the exact contents returned (source, computed date, licence, citation). The title reinforces the purpose, and the sibling list makes it clear this is the provenance endpoint rather than a data-access tool. 'Read this to attribute a figure correctly' supplies the intended action.

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 tells the agent to use this tool when attribution is needed. It does not name alternatives or exclusion conditions, but none of the sibling tools handles provenance, so the guidance is still adequate.

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 burden of behavioral disclosure. It does reveal an important behavior: matching is exact but case-insensitive. It does not mention whether the operation is read-only, what happens when multiple rows match, what happens on no match, or how results are returned. This is limited but not misleading for a simple lookup 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 or redundant phrasing. It front-loads the core behavior, states the dataset scope, and adds the key case-insensitivity detail efficiently. 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?

The tool is relatively simple with only two required string parameters and no output schema, but the description still leaves important gaps. It does not clarify the return shape, whether one or many rows are returned, or how errors like no match are handled. It is minimally adequate but not fully self-sufficient for an agent invoking it without prior context.

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 zero description coverage, so the description must compensate for parameter documentation. It loosely maps 'column' and 'value' to the query semantics ('where a column equals a value'), which adds relational meaning. However, it does not explain accepted formats, data types, allowed inputs, or edge cases, leaving the two string parameters mostly undocumented.

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 that the tool returns rows from the Disclovo dataset where a column equals a given value exactly, including the case-insensitivity detail. This clearly indicates a lookup-by-exact-key function and distinguishes it from a general search tool like dataset_search. It lacks an explicit verb such as 'returns' or 'fetches', 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 phrase 'equals a value exactly (case-insensitive)' implies this tool is for exact-match lookups rather than fuzzy search, which contrasts with the sibling dataset_search. However, it never explicitly says when to use this tool over alternatives, and no exclusions or alternative recommendations are provided. The usage context is implied, not 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, the description carries the burden of disclosure. It does reveal useful behavior: case-insensitive, full-cell matching, and a result cap of 50. However, it does not mention output format, ordering, empty-result behavior, or error 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 dense sentence with no filler. It front-loads the primary behavior and packs the important matching details and limit into minimal words.

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, the description covers core semantics and the result cap. But with no output schema and no annotations, it omits return-value details and sibling guidance, leaving some ambiguity for an agent deciding how to interpret or compare 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 50%: query has a schema description, but limit does not. The tool description compensates by explaining the query matches any cell case-insensitively and implying the 50-row cap via limit. This adds meaningful semantic value beyond the raw 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 precise behavior: return rows whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes dataset_search from siblings like dataset_row, dataset_top, and 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?

No explicit guidance is given about when to use this tool versus alternatives like dataset_row or dataset_top. The intended use case is only implied by the description, with no exclusions or comparison to siblings.

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 burden. It discloses meaningful parsing behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This is useful beyond the schema, though it stops short of describing behavior for empty or all-non-numeric columns.

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 states the core metrics first and adds the parsing caveat in a tight parenthetical. No redundant or filler content.

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 low-complexity, one-parameter stats tool, the description is largely complete: it lists the computed metrics and data-cleaning behavior. However, the meaning of 'count' after non-numeric rows are 'excluded and counted' is slightly ambiguous, and behavior for an empty or entirely non-numeric column is not stated.

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 column parameter only has a minLength constraint. The description compensates by specifying that the column must be numeric and that values with grouping commas or currency are handled. It does not specify exact column-name matching rules, but for one parameter this is adequate.

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 lists the exact statistics returned (count, min, max, mean, median, sum) and identifies the resource as a numeric column of the Disclovo dataset. This goes beyond the title and distinguishes it from sibling tools like dataset_columns or dataset_row, though it does not use an explicit verb such as '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 description implies the tool is for computing summary statistics on a numeric column, and the edge-case note clarifies acceptable input values. It does not explicitly say when to prefer this over siblings like dataset_top or dataset_search, nor does it state 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 carry behavioral disclosure. It conveys the core behavior (selecting extremal rows by a numeric column) but does not mention limit defaults, tie handling, null behavior, or whether the result is a ranking. The schema supplies ascending and limit constraints, but that is structured data, not behavioral context.

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 with no wasted words. The em-dash aside 'which is the most/least X' is slightly redundant with 'highest (or lowest)', but overall it remains concise.

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 covers the core purpose and clarifies the column type, and the schema covers limit bounds and ascending's default. However, it lacks an explicit statement of what the tool returns (row list vs. ranked table) and edge-case behavior, which is more consequential given no output schema.

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% (ascending is described). The description adds value by clarifying that column must be numeric, but it does not explain the limit parameter's behavior or defaults, leaving a gap that the schema does not fill.

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 operation: returning the highest or lowest rows of the dataset by a numeric column, with the user intent 'which is the most/least X'. This specifies a verb (rank/select), a resource (rows), and a dimension (numeric column), setting it apart from siblings like dataset_stats (aggregations) and dataset_search (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 phrase 'which is the most/least X' implies a use case, but the description provides no explicit guidance on when to choose this tool over dataset_stats, dataset_search, or dataset_row, and names no exclusions or alternatives.

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