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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 behavioral disclosure. It does disclose the tool's informational content and implies a read-only schema-introspection purpose. But it does not explicitly state that the tool has no side effects, does not mutate data, or what the response structure looks like beyond listing the data elements. For a zero-parameter metadata tool, this is adequate 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 two short sentences: the first states exactly what information is returned, and the second gives a crisp usage directive. Every word earns its place, and the key phrase 'Call this first' is prominently placed at the end of the second sentence for actionability. This is an example of efficient, front-loaded writing.

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, no-output-schema introspection tool, the description is largely complete: it lists all substantive return elements and provides an explicit calling order. The main minor omission is that it does not describe how the returned column info is formatted (e.g., array of objects vs. simple lists), but given the tool's low complexity and simple purpose, this is a small gap rather than a critical one.

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 per the rubric. The schema is empty and fully described by its coverage of 100%, and the description correctly avoids inventing parameter details. There is nothing missing in parameter semantics because there are no parameters to document.

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 exposes: columns, numeric columns, row count, and provenance banner for the Focusvo dataset. It also signals its role as the schema-discovery entry point, which distinguishes it from the sibling tools like dataset_stats or dataset_row. The lack of an explicit verb ('returns', 'lists') is a minor gap, but the noun-phrase style is still specific and 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 gives an explicit usage instruction: 'Call this first to learn the schema.' This establishes a clear workflow context and tells the agent when in the sequence this tool belongs. However, it does not explicitly state when not to use it or mention alternatives among the sibling tools, so it stops short of full exclusion/alternative 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 and no output schema, the description carries the full behavioral disclosure burden. It does reveal one useful trait ('in the order given'), but it omits important behavior: whether matching is exact, what happens with no matches, how results are returned, and what 'side by side' means structurally. This is thin for a tool with no annotation safety net.

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 front-loads the core behavior and includes the key ordering detail. It is concise and free of filler. The only minor issue is slightly awkward wording ('whose column is any of the given values'), but it remains efficient.

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, the description conveys the basic idea but leaves essential gaps: it never says how 'column' relates to the dataset schema, what valid value formats are, or what the output structure looks like. Without an output schema or annotations, an agent would need to guess or inspect sibling tools like dataset_columns to use this 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 description coverage is 0%, so the description must compensate for the undocumented parameters. It mentions 'column' and 'given values,' but does not explain that 'column' is the dataset field to match on or that 'values' are the exact cell values to filter by. It also does not clarify constraints like case sensitivity or value format. This is insufficient compensation for the schema 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 states a specific action (retrieve rows) and resource (Focusvo dataset), with a clear scope: rows whose column value matches any of the given values, preserving the given value order. The phrase 'for "X vs Y" questions' and the title 'Compare rows side by side' help differentiate it from sibling tools like dataset_row (single row) and dataset_search (general search), 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 description provides clear usage context: use for 'X vs Y' questions where rows need to be compared side by side. It does not explicitly state when not to use it or name alternative tools, but the narrow comparison purpose is a sufficiently clear guideline.

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 disclosure burden. It reveals the tool is a read-only metadata lookup by listing exactly what it returns and using 'Read this'. For a zero-parameter provenance tool, there are no hidden side effects or complex behaviors requiring further disclosure.

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 compact sentences with no filler. The first sentence front-loads the returned metadata fields, and the second provides the practical use case, making it easy for an agent to scan and act on.

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 fully conveys what the agent receives, why it exists, and when to use it. No additional details about authorization, side effects, or return formatting 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 zero parameters, so the empty schema is complete and there is nothing for the description to explain about inputs. The description appropriately focuses on the output content instead.

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 provides: source, computation date, licence, and citation for the Focusvo dataset. 'Read this to attribute a figure correctly' gives it a concrete purpose that distinguishes it from sibling tools focused on columns, rows, stats, or 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 states the intended use case—consult this tool before attributing a figure. It does not explicitly name alternative tools or exclusion conditions, but the context is clear because none of the siblings address provenance or citation.

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 explaining behavior. It does disclose case-insensitive exact matching, which is useful, but it does not explain whether all matching rows are returned or only one, what happens with no matches, ordering, or limits. For a read-style lookup tool, this is a notable 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 focused sentence that communicates the core matching behavior and the case-insensitive nuance without wasted words. The title adds a clear verb. It is appropriately sized for such a simple tool.

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 schema and missing output schema, the description should clarify return behavior, but the singular/plural discrepancy between title and description leaves that unclear. It also does not direct the agent to dataset_columns for discovering valid column names or to dataset_search for non-exact queries. Operational details are incomplete.

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?

With 0% schema description coverage, the description must supply parameter meaning. It does relate the two parameters to the equality predicate ('a column equals a value'), which gives some relational semantics beyond bare string types. However, it does not enumerate valid column names, expected value formats, or how to discover available 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?

The title and description together clearly state the operation: look up rows by an exact column/value match, with case-insensitive equality. This distinguishes it from dataset_search by emphasizing exactness, though it does not explicitly name sibling tools. Minor ambiguity: the title says 'a row' while the description says 'rows'.

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 a caller needs rows matching an exact value for a column. However, it does not explicitly state when not to use it or which sibling tool should be used for partial, fuzzy, or range-based searches. Usage context is clear but alternatives are not mentioned.

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 burden. It adds useful details: case-insensitive matching and a 50-row cap. However, it does not disclose what happens with no matches, whether results are ordered, or the exact return format.

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 conveys the core behavior with no filler. Every element—row selection, cell matching, case-insensitivity, and result cap—is packed 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?

For a simple two-parameter search tool with no output schema, the description explains input behavior and the maximum result count. It could note what a result row looks like or empty-result behavior, but the basics are sufficiently covered.

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 50%: the query parameter has a description while limit does not. The tool description adds the case-insensitive matching behavior for query, which is meaningful. However, the relationship between the limit parameter and the 'up to 50' behavior is only implicit, so the description partially compensates but does not fully clarify parameter 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 states a clear verb ('search') and resource ('dataset'), and precisely defines the behavior: returns rows whose cells contain the query, case-insensitively, up to 50 results. This distinguishes it from sibling tools like dataset_row or dataset_stats, which clearly have different 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 description gives clear context for when to use the tool: when an agent needs to find dataset rows matching a query in any cell. It does not explicitly mention alternatives or exclusion criteria, but the search semantics are specific enough that an agent can infer the appropriate use case.

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, which addresses common edge cases. It does not mention behavior for empty columns or nonexistent columns, but the disclosed details add meaningful transparency.

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 concise sentence that leads with the output statistics and packs useful edge-case handling into a parenthetical. Every word contributes value with no 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?

For a simple one-parameter tool with no output schema, the description covers the core operation, output statistics, and important data-cleaning behavior. It is slightly incomplete regarding error handling for empty or fully non-numeric columns, but overall it gives an agent enough to use the tool correctly in typical cases.

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, and schema description coverage is 0%. The description adds context that the column must be numeric and belong to the Focusvo dataset, but it does not explain valid column name formats or how to discover available columns (e.g., via dataset_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?

The description clearly identifies the tool's function: computing six named summary statistics (count, min, max, mean, median, sum) for a numeric column in the Focusvo dataset. This distinguishes it from sibling tools like dataset_search, dataset_row, or dataset_top, which serve different 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 description implies the tool is for obtaining numeric summary statistics, but it does not explicitly state when to use it versus alternatives such as dataset_top or dataset_columns. No exclusions or alternative-routing guidance is provided.

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 burden. It discloses the core behavior: sorting rows by a numeric column and returning the highest or lowest set. It does not mention tie-breaking, null handling, non-numeric column errors, whether the operation is read-only, or what shape the results take, leaving some ambiguity.

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 short and front-loaded, with the core ranking behavior stated immediately. The illustrative 'which is the most/least X' adds practical query intuition without unnecessary fluff. It is slightly redundant with the title, but the extra nuance is useful.

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 three-parameter tool, the description plus schema covers the main invocation. Missing elements include explicit sibling-tool routing, behavior on invalid or non-numeric columns, and any statement about the returned row set. These gaps are moderate rather than severe.

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 only 33%, so the description must compensate. It adds meaning to 'column' by specifying it must be numeric and clarifies the 'ascending' behavior via highest/lowest. However, it does not explain the 'limit' parameter or the default ordering behavior beyond what the schema already provides.

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 the operation: ranking rows of a dataset by a numeric column and returning the highest or lowest values. The phrase 'which is the most/least X' gives a concrete query framing. It does not explicitly differentiate from sibling tools like dataset_row or dataset_stats, but the ranking intent is 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?

The description implies the use case: answer questions about most/least X in the dataset. However, it provides no explicit guidance about when to prefer dataset_top over related tools such as dataset_search or dataset_stats, and no when-not-to-use conditions are stated.

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