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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 transparently states the full set of returned information and, as a zero-parameter metadata operation, there are no side effects to warn about. It does not explicitly say 'read-only,' but the nature of the tool makes that reasonably clear.

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 concise sentences with no filler. The content of the return value is front-loaded, and the usage instruction is placed clearly in the second sentence.

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 zero parameters and no output schema, the description is fully sufficient: it names the dataset, lists the returned information, and tells the agent to call it first. Nothing essential is missing 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 zero parameters, so the baseline is 4. No parameter explanations are needed; the description appropriately focuses on what the tool returns instead of input semantics.

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 specifies the tool's output: columns, which are numeric, row count, and provenance banner for the Limslane dataset. It also gives an actionable purpose, 'Call this first to learn the schema,' which distinguishes it from sibling tools like dataset_row, 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 phrase 'Call this first to learn the schema' provides clear context for when to use the tool before other dataset operations. It does not explicitly name alternatives or state when not to use it, so it falls just short of the strongest 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?

With no annotations, the description carries the full burden. It discloses a meaningful behavioral trait: output rows are ordered according to the order of the input values. It also implies a read-only IN-filter. However, it does not mention exact vs partial matching, case sensitivity, or behavior when values are missing, so coverage is adequate but incomplete.

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?

One compact sentence that front-loads the core behavior (rows ... whose column is any of the given values) and appends the use-case hint. There is no filler, repetition, or unnecessary detail.

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 read-only tool with two required parameters, no output schema, and no annotations, the description covers the essential points: what rows are returned, how filtering works, the ordering behavior, and the intended comparison use case. Minor gaps like exact output fields and handling of unmatched values exist, but they are not critical for selecting and invoking 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?

Schema description coverage is 0%, so the description must explain the parameters, and it does. 'column' is clearly the field to filter on and 'values' are the allowed match values; 'any of the given values, in the order given' conveys both the IN semantics and ordering. The schema only provides types and bounds, so this added meaning is genuinely useful.

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 behavior: retrieving rows from the Limslane dataset where a column matches any of the given values, in the provided order. It also frames the use case as 'X vs Y' questions, which differentiates it from siblings like dataset_row and dataset_search. However, it lacks an explicit action verb like 'retrieves' or 'returns', so it is not a perfect 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 useful context signal for when to use the tool, implying side-by-side comparison of specific values. But it does not explicitly name alternative sibling tools or state when not to use it, leaving the agent to infer boundaries from sibling names like dataset_search or dataset_top.

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 adequately indicates this is an informational, read-only tool returning provenance metadata, but it does not explicitly state that no data is modified or describe the exact shape of the citation information.

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 tight sentences with no filler. It front-loads the key content (source, date, licence, citation) and ends with a practical directive for the agent.

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 lookup, the description is complete enough: it lists the core fields and the use case. It does not describe the citation format in detail, but an agent can retrieve and inspect the actual output without risk.

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 parameter ambiguity is nonexistent. The description does not need to explain parameter meanings, and the schema confirms no inputs are required.

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 Limslane dataset. It also signals the intended use case ('attribute a figure correctly'). This clearly distinguishes it from sibling tools like dataset_columns, dataset_stats, and dataset_search, which serve data exploration 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 tells the agent when to use the tool: when attribution of a figure is needed. It does not explicitly discuss when not to use it, but the purpose is so distinct from the sibling tools that no exclusion is necessary.

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 usefully reveals case-insensitive exact matching behavior, but it does not clarify whether multiple matching rows are returned, what happens on no match, or whether the case-insensitivity applies to the column name as well as the value.

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 plus a clear title conveys the core behavior with no filler. The description is front-loaded with the operation and resource, making it easy to scan.

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-string-parameter lookup, the description is mostly adequate, especially with the case-insensitive detail. However, the absence of an output schema and annotations means the agent still lacks information about return behavior, multiple matches, and failure cases, leaving minor but real 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?

Schema description coverage is 0%, so the description must compensate for parameter meaning. It only restates that a column equals a value, offering minimal additional semantic detail beyond the raw property names. The case-insensitive note adds some value for the value parameter, but column semantics are not enriched.

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: return rows from the Limslane dataset where a column exactly equals a value, with case-insensitivity. The title reinforces the exact-key lookup intent, and this distinctively contrasts with the more general dataset_search sibling.

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 clear context: use this tool for exact, case-insensitive equality matches on a column/value pair. It does not explicitly name alternatives or exclusions, but the exact-match wording sufficiently conveys when this tool is appropriate versus a broader search.

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 behavioral disclosure burden. It does disclose case-insensitive matching, cell-level containment, and the 50-row cap, but it does not describe the return shape, default limit behavior, ordering, or what happens with no matches. This is adequate but has clear gaps.

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?

One concise sentence with no filler; the core search behavior, case sensitivity, target resource, and result cap are front-loaded and immediately actionable.

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 essential operation, matching semantics, and maximum result size. It lacks an explicit return format and any mention of default limit or empty-result behavior, but the tool's low complexity makes the definition largely sufficient for an agent to invoke 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?

The query parameter already has a schema description ('text to look for in any cell'), and the description adds the important case-insensitive nuance. The limit parameter has no schema description, and while 'up to 50' hints at its effect, the description does not explicitly define limit's default or how it caps results. With only 50% schema coverage, more compensation would be better.

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—searching the Limslane dataset for rows containing a query—and adds precise matching semantics (case-insensitive, cell containment) and a result cap. This clearly distinguishes it from siblings like dataset_stats, dataset_columns, or dataset_row, which serve different retrieval/derivation 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 behavior is specific enough to imply when to use it: whenever a substring search across cell values is needed. It does not explicitly name alternatives or exclusion criteria, so it stops short of a 5, but the context 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, the description carries the full burden and provides meaningful behavioral details: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. This goes beyond a simple statement of purpose and helps the agent predict how messy data will be 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, information-dense sentence with the core functionality front-loaded and the data-cleaning caveats neatly appended in parentheses. No redundant or filler content is present.

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 tool, the description covers the operation, parameter semantics, and edge-case behavior. It does not specify the output structure or error behavior when a column is missing, but the enumerated statistics give a clear picture of what will be returned.

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 only defines 'column' as a non-empty string, but the description adds crucial semantics: the column belongs to the Limslane dataset, is treated as numeric, and its values may include formatted numbers (commas, currency) that are handled. Non-numeric row handling is also clarified, giving the parameter real meaning 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 explicitly enumerates the computed statistics (count, min, max, mean, median, sum) and targets a specific resource: a numeric column of the Limslane dataset. This clearly distinguishes the tool from siblings like dataset_row, dataset_search, and dataset_top by stating exactly what it produces.

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 by the description—computing summary statistics for a numeric column—but there is no explicit guidance on when to choose this tool over alternatives. No sibling tools are mentioned, and no exclusionary conditions are given.

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 conveys that the tool orders rows by a numeric column and can return highest or lowest, which is meaningful but does not address tie-breaking, handling of missing or non-numeric values, or the exact shape of the returned rows.

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 entire description is one tight sentence that front-loads the core behavior and then illustrates the intended question. No filler or repetition; every clause 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?

The tool is simple enough that the description covers the main use case, but with no output schema and no annotations, an agent still lacks details about return format, column selection behavior, and edge cases. It is minimally adequate but leaves several operational questions unanswered.

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 needs to compensate. It adds the important constraint that 'column' must be numeric and that 'ascending' controls highest vs lowest, but it says nothing about the 'limit' parameter beyond what the schema's numeric bounds provide.

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: returning the highest or lowest rows of the Limslane dataset by a numeric column, which distinguishes it from generic retrieval or stats tools. It lacks an explicit sibling comparison, but 'top rows by numeric column' is specific enough to identify this as a ranking 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?

The phrase 'which is the most/least X' implies the intended use case: finding extreme rows by a numeric field. However, there is no explicit guidance on when to use this tool instead of dataset_row, dataset_search, or dataset_stats, nor any exclusions.

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