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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 full burden of explaining behavior. It discloses the output scope (schema shape, numeric flags, row count, provenance banner) and implies a safe read-only operation. It does not explicitly state that it performs no mutations, but the nature of the tool makes that 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 sentences with no filler. The first sentence enumerates the returned information, and the second gives a directive. 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 zero-parameter, schema-inspection tool, the description is nearly complete. It states what is returned and when to call it. It does not describe the exact response format or how this differs from sibling tools, but the absence of parameters and output schema lowers the burden enough that these 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 tool has zero parameters and schema coverage is 100%, so the parameter dimension is trivial. The baseline of 4 applies because there are no parameter semantics to explain.

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 returns: the columns, which are numeric, row count, and provenance banner for the Xlifflane dataset. It goes beyond the title 'Dataset columns and shape' with concrete output components, though it does not explicitly distinguish itself from sibling 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 directive 'Call this first to learn the schema' provides a clear usage sequence, implying it should be used before other dataset tools. It does not mention alternatives or when not to use it, but for a schema-introspection tool with no parameters this is reasonably 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 provided, the description carries the full burden of behavioral disclosure. It mentions that rows are returned 'in the order given', which is a behavioral detail, but it is ambiguous whether that order refers to the input values or the dataset's natural order. It does not disclose what happens when no rows match, whether all columns are returned, case sensitivity, or any error behavior. For a read-only filter tool, this is a significant transparency 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?

The description is a single sentence that is front-loaded with the main action ('The rows of the Xlifflane dataset...') and efficiently conveys the filtering and ordering behavior. There is no waste, though the phrase 'in the order given' could be more precise. Overall, it is appropriately 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?

Given the tool has only 2 parameters and no output schema or annotations, the description is expected to cover essential usage context. It does explain the core function but fails to describe the return format (e.g., does it return full rows? side-by-side as the title suggests?) or any edge cases. The title mentions 'Compare rows side by side', but the description does not elaborate on the output structure, leaving the agent uncertain about what to expect. This is a notable gap for a simple 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 schema has 0% description coverage, so the description must compensate. It does explain the roles of 'column' (the field to filter on) and 'values' (the values to match) by stating 'whose column is any of the given values'. This adds meaning beyond the bare schema. However, it does not explicitly define the constraints (e.g., minimum 2 values) or clarify that values must be strings, which are left to the schema. It partially compensates but not fully.

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 tool returns rows from the Xlifflane dataset filtered by a column matching any of the given values, in the specified order. It conveys a specific verb (returns rows) and resource (dataset), and hints at a comparison use case ('for X vs Y questions'). However, it does not explicitly differentiate from sibling tools like dataset_search, which also filters, so it misses a clear distinction.

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 usage context: 'for X vs Y questions' implies comparing two or more specific values of a column. This gives a clear situational cue. However, it does not mention when not to use this tool, nor does it name alternatives or exclusions, so the guidance is incomplete.

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 does reveal what content is returned (source, date, licence, citation), but it does not state whether the operation is read-only, what the output structure looks like, or whether any network or licensing checks occur. For a simple metadata getter this is acceptable 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, no filler. The content list is front-loaded and the usage instruction is placed at the end, 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?

Given the tool has no parameters and no output schema, the description covers the essential information an agent needs: what data it returns and when to use it. It could mention output format or that no arguments are required, but those are minor gaps for such a simple 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 and the input schema is empty with 100% coverage, so there is nothing for the description to add. The baseline for zero-parameter tools is 4, and the description adequately implies no inputs 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 exactly what the tool provides: source, computation date, licence, and citation for the Xlifflane dataset. It clearly separates this from sibling data-manipulation tools like dataset_row or dataset_stats by focusing on provenance and attribution.

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?

'Read this to attribute a figure correctly' gives an explicit use case for when to call this tool. It does not mention exclusions or alternatives, but among the siblings only this tool concerns provenance, so the context is clear 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?

With no annotations, the description must convey behavior on its own. It discloses exact matching and case-insensitivity, which are important behavioral traits. However, it does not explain whether multiple rows can be returned, what happens when no row matches, or how results are ordered or limited.

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 compact sentence with no filler. The key behavior, exact match on a column, is front-loaded, and the case-insensitive qualifier is appended efficiently.

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 with only two parameters and no output schema, and the description covers its core purpose and matching semantics. Still, with no annotations, it leaves behavioral gaps around return cardinality, empty results, and edge-case handling.

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 clarify parameter roles. It does so implicitly by saying a column equals a value, mapping 'column' to the field name and 'value' to the lookup value, and adds case-insensitive semantics. It still lacks detail such as accepted column names or value formatting.

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 'Look a row up by an exact key' and description 'rows of the Xlifflane dataset where a column equals a value exactly' clearly identify the tool as an exact-match row lookup. It distinguishes itself from siblings like dataset_search and dataset_top by emphasizing exact equality, though it does not explicitly name a 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 a clear use condition: use this tool when you need rows where a column equals a value exactly, with case-insensitivity noted. It does not mention alternatives or exclusions, but the exact-match context strongly implies when this tool is 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?

There are no annotations, so the description carries the behavioral disclosure burden. It adds useful behavioral details: case-insensitive matching and a 50-row cap. However, it does not state default limit behavior, output shape, or behavior when no rows match.

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 compact sentence with no filler. The resource and core matching behavior are front-loaded, and every clause contributes 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 search tool, the description covers the main semantic points: what is searched, how matching works, and the result cap. The absence of an output schema and explicit sibling routing keeps it slightly short of complete, but the core call is well 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?

The schema covers query semantics via its description, and the description adds case-insensitivity for query and 'up to 50' context for limit. Because schema description coverage is only 50%, the description partially compensates but does not clarify whether limit is optional or what the default is.

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 resource (rows of the Xlifflane dataset), the matching behavior (cells contain the query), and the case-insensitive rule. It does not explicitly differentiate itself from siblings like dataset_row or dataset_top, but the filtering semantics are specific enough to be understood independently.

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 no guidance on when to use dataset_search versus sibling tools such as dataset_row, dataset_top, or dataset_columns. It implies a search use case but offers no contextual conditions, 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?

No annotations are provided, so the description carries the full burden. It discloses handling of grouping commas/currency and exclusion/counting of non-numeric rows, which adds value. However, it does not explicitly state that the operation is read-only, mention permissions, or describe error behavior for invalid columns, leaving some transparency 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?

The description is a single, dense sentence that lists the statistics upfront and adds two relevant handling notes. No waste, and the most important information 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 simple one-parameter tool with no output schema, the description adequately conveys the output by enumerating the computed statistics. It also covers edge cases like formatting and non-numeric rows. It could mention return format or error handling, but overall it is sufficient for an agent to use the tool 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?

Schema coverage is 0%, so the description must compensate. It does by specifying the parameter is a 'numeric column' of the dataset, clarifying that the string must be an existing column name with numeric data. This adds meaningful semantic context beyond the schema's generic string type.

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 tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Xlifflane dataset. It uses a specific verb and resource, and the title reinforces the purpose. It is clearly distinct from siblings like dataset_columns or 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 description implies its use for statistical summaries but does not explicitly state when to choose it over alternatives or when not to use it. No sibling tools are mentioned, so the agent must infer the appropriate context from the tool's purpose alone.

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 full responsibility for behavioral disclosure. It mentions highest/lowest ordering but does not explain what a returned row looks like, how ties are handled, what the default limit is, or whether full rows or just the ranking column are returned. This is a significant gap for a ranking 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, short sentence that front-loads the core action. The appended quote 'which is the most/least X' is somewhat redundant, but overall the description has no wasted 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 tool with three parameters, no output schema, and no annotations, the description is too sparse. It omits return value format, default limit behavior, tie-breaking, and error conditions, so an agent may call it correctly but cannot anticipate results reliably.

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%, with only 'ascending' having a description. The description adds that 'column' must be numeric and clarifies highest/lowest ordering, but it does not explain the 'limit' parameter's meaning or default behavior. It only partially compensates for the low schema coverage.

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 tool ranks rows by a numeric column and identifies highest/lowest rows, which is a specific verb-resource pairing. It is distinct from sibling tools like dataset_search or dataset_stats, 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 usage context is implied: use this when you want the most/least values of a numeric column. However, there is no explicit guidance about when not to use it or which sibling tool would be a better alternative for other ranking or filtering needs.

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