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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 states exactly what information is returned and implies a read-only introspection operation via 'learn the schema.' It does not explicitly disclaim side effects, but the zero-parameter metadata nature makes that 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 defines the returned content, the second states the intended invocation order. 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 tool with no output schema, the description supplies both the behavioral contract and the usage context. An agent knows what to expect—columns, numeric flags, row count, provenance banner—and when to call it.

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 there is nothing for the description to explain. Per the baseline for parameterless tools, this receives a 4.

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 output—columns, numeric indicators, row count, and provenance banner—and frames it as the schema-learning entry point for the Walkvo dataset. 'Call this first to learn the schema' distinguishes it from sibling tools like dataset_search, dataset_stats, and dataset_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?

It gives explicit timing guidance: 'Call this first to learn the schema.' This tells the agent when to use it relative to other dataset tools. It does not enumerate exclusions or explicitly name alternatives, 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 carries the full disclosure burden. It transparently states the selection rule and ordering behavior, but says nothing about output format, row limits, read-only nature, or how the 'side-by-side' comparison actually materializes.

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 that efficiently communicates the core selection behavior. The trailing em-dash clause is compact but somewhat vague, so it does not fully earn a 5.

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 2-parameter tool with no output schema and no annotations, the description conveys the core selection rule and intended use case. However, it omits return/presentation details—the 'side-by-side' format is never explained—and does not describe what the output looks like, leaving the agent with some uncertainty before invoking.

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 clarifies that column is the field to compare and values are the candidate values to match in given order, which partially compensates. Yet it does not specify exact-match semantics, valid column names, or behavior for missing values.

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?

Description states a specific behavior: return rows whose column matches any of the given values, in the order given, and ties it to 'X vs Y' questions. It is distinguishable from siblings like dataset_stats or dataset_search in intent, but the title's 'Compare rows side by side' suggests a presentation format that the description does not clearly deliver.

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' implies when the tool is appropriate, giving useful context. However, it does not explicitly name alternatives among the six sibling tools, nor state when not to use it.

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 behavioral disclosure. It explains what fields will be returned—source, date, licence, citation—but does not state whether this is a read-only operation, how the data is returned, or what happens if provenance is unavailable. For a simple metadata-fetch tool this is near-adequate but still leaves 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 two short sentences with no filler. The first sentence states exactly what the tool returns; the second frames the use case. 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 no-argument metadata tool, the description is reasonably complete: it enumerates the fields the agent will need for attribution. The lack of an output schema and explicit return-format details is a minor gap, but the description compensates by naming the expected content.

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 schema description coverage is effectively complete, so there are no parameter semantics to explain. The baseline of 4 applies because nothing is missing on this dimension.

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 a specific resource (Walkvo dataset) and the exact content (source, computed date, licence, citation). This clearly distinguishes it from sibling tools like dataset_row or dataset_stats, so an agent can tell it apart without opening schemas.

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 a clear, practical context for when to use the tool. It does not explicitly list alternatives or negative cases, but the sibling names and the tool's metadata nature make that exclusion obvious.

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 behavioral burden. It discloses that matching is case-insensitive, which is useful, but it omits other behavioral traits such as whether multiple rows can be returned, pagination, ordering, or error behavior. The plural 'rows' suggests multiple results, but this is not explicit.

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 redundant words. It is front-loaded with the core purpose and includes the key detail of case-insensitivity. No fluff or unnecessary elaboration.

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 tool with no output schema or annotations, the description is adequate but not complete. It clarifies the matching semantics but leaves ambiguity about whether one or multiple rows are returned, and it does not mention limits or response format. This is a minimal viable description but lacks several practical details.

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 absence of parameter details. It only says 'a column equals a value,' which maps directly to the parameter names but adds no extra meaning about formats, constraints, or valid inputs. The names 'column' and 'value' are somewhat self-explanatory, but the description does not clarify expected types or examples.

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 what the tool does: it returns rows where a column equals a value exactly, with case-insensitive matching. This is specific and distinguishes it from a fuzzy search tool like dataset_search, though it lacks a strong verb (e.g., 'returns') and reads as a noun phrase rather than an 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?

There is no guidance on when to use this tool versus alternatives like dataset_search. It does not mention exclusions, prerequisites, or typical use cases. The only implied context is exact-match lookups, but it never explicitly contrasts with fuzzy or partial matching.

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 burden. It usefully discloses case-insensitive matching and the 50-row cap, but it omits details like return format, ordering, default limit behavior, and what happens 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?

One sentence conveys the action, scope, and key constraints with no redundancy. The information is compact and 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?

The tool is simple and the schema covers the required parameter, but without an output schema or annotations the agent still lacks information about the return shape, ordering, and limit defaults. The description is usable but leaves meaningful 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 query parameter benefits from both a schema description and the case-insensitive clarification. The mention of 'up to 50' partly explains the limit parameter, but the description does not clarify the default value or behavior when limit is omitted; with 50% schema coverage this is only partial compensation.

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 names the resource (Walkvo dataset) and the action: returning rows whose cells contain the query, case-insensitively, up to 50. It is specific, but it does not explicitly differentiate from sibling tools like dataset_row or 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?

There is no guidance about when to choose this tool over siblings or when not to use it. The intended use case is only implied by the name and behavior description, so the agent must infer the selection context.

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 well by noting that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which are non-obvious data-cleaning behaviors. It does not mention return structure or empty-column behavior, but the listed statistics largely imply the output.

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 dense sentence delivers the operation, resource, and important edge-case handling without wasted words. The parenthetical qualifiers earn their place because they change how an agent should interpret inputs and results.

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 single-parameter, no-output-schema tool, the description is nearly complete: it names the input, the output statistics, and the key data-cleaning behavior. It lacks explicit error handling or return-format details, but those are minor for this simplicity level.

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 the description does not name the parameter explicitly. However, it adds real meaning by specifying that the column must be numeric and that formatting/currency quirks are handled, which helps the agent choose an appropriate value.

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 (computing count, min, max, mean, median, sum) and the resource (a numeric column of the Walkvo dataset). It does not explicitly differentiate from siblings, but the aggregate-statistics nature is self-evident against row/search/top 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?

Usage context is implied: an agent would call this when it needs summary statistics for a numeric column rather than row-level or search operations. No explicit when/when-not guidance or named alternatives are provided, leaving the choice to inference.

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 adds the numeric-column requirement but mostly restates the ranking concept from the title. It does not disclose output format, default limit behavior, tie handling, or whether the returned rows are in sorted order.

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, and the core highest/lowest concept is front-loaded. It is efficient, though the brevity contributes to under-specification in other dimensions.

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?

With no output schema and no annotations, the description should clarify what the tool returns (e.g., rows, values, or counts) and how limit and ascending shape the result. It covers the numeric-column requirement but leaves the output format and ordering behavior undisclosed.

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%, and the description partially compensates: it clarifies that 'column' must be numeric and maps 'highest/lowest' to the ascending flag. However, it says nothing about 'limit' and relies on the schema for the ascending default.

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 indicate the operation: return the highest or lowest rows of a dataset ordered by a numeric column. The 'most/least X' phrasing helps map natural-language ranking questions to this tool. It is distinct from siblings like dataset_row and 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?

Use is implied through 'which is the most/least X' for top-N/bottom-N questions, but there is no explicit statement of when to choose this tool over dataset_stats or dataset_search, and no exclusions or prerequisites are mentioned.

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