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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. It discloses the concrete output contents (columns, numeric indicators, row count, provenance banner) and implies a non-mutating schema-inspection behavior through 'learn the schema.' No side effects or access concerns are mentioned, but the tool is a simple zero-parameter inspection.

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, front-loads the key output contents, and ends with a practical instruction. Every clause earns its place 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 zero-parameter tool with no output schema, the description sufficiently explains both the return value and how to use it. It covers the dataset name, what data will be returned, and the recommended first-step usage, making it adequate for an agent to call 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 tool has zero parameters, so the baseline is 4. The description appropriately avoids inventing parameter details and focuses on the output, which is more relevant for a schema-discovery 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 enumerates what the tool returns: columns, numeric flags, row count, and provenance banner for a specific dataset. It implies a schema-discovery purpose, though it does not explicitly distinguish itself from sibling tools such as dataset_stats or dataset_top.

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 instruction to 'Call this first to learn the schema' provides explicit timing guidance for when to invoke it. It does not state exclusions or directly name alternatives, but the sequencing guidance is clear and useful.

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 usefully discloses the filter logic ('whose column is any of the given values') and ordering ('in the order given'), but it omits matching exactness, case sensitivity, return shape, or any side-effect/read-only guarantees.

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 core behavior and the intended use case without repetition. The phrasing is slightly awkward ('The rows ... whose column is any'), but every element 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 simple and the description gives enough to attempt a call, but without annotations or an output schema, more detail would help: exact-match behavior, row output format, and how this differs from dataset_search or dataset_row. It is adequate but not fully complete.

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. It does tie 'column' to the matching field and 'values' to the list used for filtering, and it explains ordering. However, it does not define exact-match semantics or value formatting beyond what the schema's types already 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 clearly specifies the operation: return rows from the DrawScheduleWorks dataset filtered by a column matching a list of values, preserving the given order. The title adds the 'compare side by side' framing, though the description itself lacks an explicit verb and does not directly name 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 phrase 'for "X vs Y" questions' provides clear context for when this tool should be used. It does not explicitly contrast it with sibling tools like dataset_search or dataset_row, but the described behavior is specific enough to imply its niche.

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 of behavioral disclosure. It explicitly states the content returned (source, date, licence, citation) and implies a read-only operation via 'Read this.' This is sufficient for a simple provenance tool, though it does not mention potential errors or edge cases.

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 that front-loads the essential information and includes a clear call to action. Every word earns its place 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 tool with no parameters and no output schema, the description fully covers what the tool does and why it should be used. It names the specific dataset and the use case (citation), leaving no ambiguity for an agent.

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 an empty schema, so there is nothing to document. The baseline of 4 applies because the description adds no parameter details, but none 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 clearly states the tool provides provenance information (source, date, licence, citation) for the DrawScheduleWorks dataset, with a specific verb ('read') and resource. It is easily distinguished from sibling tools like dataset_columns or dataset_stats, which handle different aspects.

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 guidance: 'Read this to attribute a figure correctly,' which tells the agent exactly when to use this tool. It does not explicitly list alternatives or exclusions, but the sibling tools are obviously different in purpose, so the usage 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 of behavioral disclosure. It mentions the case-insensitive exact match but does not disclose whether the operation is read-only, how it handles multiple matches or no matches, the response format, or any side effects. This is insufficient for a data-access 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, focused sentence with no wasted words. It front-loads the core operation and the case-insensitive detail. While it lacks depth, it is appropriately concise for the simple functionality it describes.

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 two-parameter tool, the description is incomplete for an agent to use it correctly. It does not explain what the returned rows look like, whether a single row or all matching rows are returned, or how it differs from dataset_search. With no output schema or annotations, these details are essential but omitted.

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 bare schema. It implicitly explains that 'column' is a column name and 'value' is the value to match, but it does not specify valid column names, value formatting, or behavior when no match exists. The added meaning is minimal beyond what the parameter names already suggest.

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 verb and resource: it returns rows from the DrawScheduleWorks dataset where a column exactly matches a value, case-insensitively. This clearly conveys the operation, though it does not differentiate from sibling tools like dataset_search, which might offer similar lookups with different semantics.

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 guidance is provided on when to use this tool versus alternatives such as dataset_search or dataset_top. The description only states what it does, not the conditions that would favor this tool, nor any exclusions or prerequisites.

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. It discloses case-insensitivity and the 50-row limit, which adds behavioral context. However, it omits details like pagination, sorting, or read-only status, though these are largely implied by the tool's search nature.

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, front-loaded sentence delivers the core behavior without extraneous details. It is concise and to the point, covering the essential functionality.

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 search tool, the description provides enough context to understand what it does. It does not specify the exact return format or ordering, but given the absence of an output schema, this is a minor gap. The description is adequate for an agent to invoke 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 50% (only query has a description). The description mentions 'up to 50' which hints at the limit parameter but does not fully explain its semantics beyond the schema's maximum. It adds little value over the existing query description, and does not compensate for the undocumented limit parameter.

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 searches rows of a specific dataset (DrawScheduleWorks) for cells containing a query, case-insensitive, with a limit of 50. This distinguishes it from siblings like dataset_row (likely single row retrieval) and dataset_top (top rows without 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 implies when to use it (when searching for rows matching a query) but does not explicitly contrast with alternatives or mention when not to use it. No sibling tools are referenced, so an agent must infer the appropriate 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 behavioral disclosure burden. It goes beyond a bare 'compute stats' statement by noting that grouping commas and currency are handled and that non-numeric rows are excluded and counted. This gives the agent meaningful expectations about data cleaning behavior.

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 that front-loads the computed statistics and appends important edge-case handling. Every clause adds information and there is no filler.

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 single-parameter tool with no output schema, the description is largely complete: it lists all computed values and describes how dirty data is treated. The main gap is absence of usage alternatives, which is already penalized under usage_guidelines, so the remaining detail is sufficient for an agent to call 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?

Schema description coverage is 0%, so the description must compensate. It does add meaning by specifying that the column must be a numeric column of the DrawScheduleWorks dataset and that formatted values are normalized. However, it does not clarify how column names are passed, what valid column identifiers look like, or whether the column should match dataset_columns output exactly.

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 operation: computing count, min, max, mean, median, and sum for a numeric column. It is specific about the resource (DrawScheduleWorks dataset) and naturally distinguishes itself from siblings like dataset_top or dataset_search by describing statistical aggregation rather than retrieval or comparison.

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 explicit guidance about when to use this tool versus alternatives like dataset_top or dataset_search. The context is implied—use it when summary statistics are needed—but no exclusions, prerequisites, or alternative selection criteria are 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, the description carries the behavioral disclosure burden. It states the core ordering behavior (highest or lowest by numeric column) and implies a non-mutating query, but it does not mention default ordering, limit behavior, null handling, or return shape.

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 one compact sentence with a natural-language gloss. It is appropriately sized and avoids verbosity, though the phrasing could be slightly more direct.

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 is not complete for a tool with no output schema and low schema coverage: it omits limit semantics and default behavior, and gives no indication of what the returned rows look like. An agent could invoke it with just a column name but would be guessing about result size and format.

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 description adds important meaning by specifying that column must be numeric, which the schema does not convey, and clarifies the ascending concept via 'lowest/least'. However, it does not explain limit's role or default, leaving a gap given only 33% schema description 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 title and description together state a specific operation: rank/select the highest or lowest rows of the DrawScheduleWorks dataset by a numeric column. This clearly identifies verb, resource, and value semantics, though it does not explicitly contrast with sibling tools like dataset_stats or dataset_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 the intended use case for top/bottom ranking questions. However, there is no explicit guidance about when not to use this tool or when a sibling such as dataset_stats or dataset_search would be more appropriate.

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