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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 the returned content (columns, numeric flags, row count, provenance banner) but does not mention potential limitations or side effects, though for a read-only schema tool this is largely sufficient.

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 concise and well-structured, listing the returned fields and a usage hint without extraneous 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?

Given the tool's simplicity, the description fully covers what it does, what it returns, and when to use it. No critical information is missing for an agent to correctly invoke 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 to describe. Schema coverage is effectively 100%, and the description adds no unnecessary parameter context.

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?

Clearly states it returns columns, numeric flags, row count, and provenance banner, and explicitly positions it as the first call to learn the schema, distinguishing it from sibling tools like stats or 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?

Explicitly instructs to call this tool first to learn the schema, providing clear when-to-use guidance relative to other dataset tools.

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 responsibility for disclosing behavior. It does disclose key behavior: rows are filtered to those whose column value is any of the given values, and the order of results follows the order of the provided values. This is meaningful but does not mention output format, handling of missing matches, or any read-only assurance, leaving some behavioral 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 a single efficient sentence that front-loads the core behavior and includes a practical usage hint. Every phrase earns its place, with no filler or repetition of schema details.

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 retrieval tool, the description covers the matching semantics, ordering, and intended comparison use case. It does not describe the output shape or edge cases, but the absence of an output schema makes those less critical for this tool's apparent simplicity.

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 clarify parameter meaning. It does explain that 'column' is the dataset column to match against and 'values' are the values used for matching, with order preserved. This adds semantics beyond the raw schema types and min/max constraints.

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 that the tool returns rows from the Requly dataset filtered by a column matching any of the given values, in the order provided. This clearly identifies the resource and the filtering behavior, and the 'X vs Y questions' phrase helps distinguish it from sibling tools like dataset_search or dataset_top. However, it lacks an explicit verb like 'return' or 'list', relying on the title to complete the 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?

The 'for X vs Y questions' note gives an implied usage context, suggesting the tool is meant for comparing specific values side by side. It does not explicitly state when to prefer this over siblings like dataset_row or dataset_search, nor when not to use it. The guidance is implied rather than explicit.

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 implies a read-only action through 'Read this' and lists the returned provenance fields, but it does not explicitly state that there are no side effects, no required authentication, or no special access conditions.

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 wasted words. It front-loads the key content (source, date, licence, citation) and adds the practical instruction to use it for correct attribution.

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 zero-parameter metadata tool, the description is complete enough. It names the exact provenance fields that will be returned and explains why an agent would need them, so no critical context is missing.

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 coverage is 100%, so there are no parameter semantics to clarify. The baseline for a zero-parameter tool is 4, and the description needs no additional parameter information.

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 that the tool provides the source, computed date, licence, and citation for the Requly dataset. This is a specific resource plus a distinct metadata purpose, and it is easily distinguishable from sibling tools that focus on columns, rows, search, stats, and comparisons.

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 says to read this tool when attributing a figure correctly, giving a clear usage context. It does not explicitly name alternatives or exclusions, but the provenance purpose is sufficiently distinct from the data exploration siblings.

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?

The description discloses one behavioral trait: the match is case-insensitive. However, it does not mention whether the operation is read-only, nor does it describe the output format or error handling behavior. Given the absence of annotations, the description carries the burden but only partially fulfills it.

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 directly communicates the core functionality without any fluff or redundant details. It is well-structured and easy to parse.

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 nature of the tool and the minimal schema, the description is largely complete for basic usage. It lacks an output schema and details on pagination or limits, but for an exact-match lookup, the information provided is sufficient to understand what the tool does.

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 implicitly defines both parameters: 'column' refers to a column name and 'value' is the exact value to match. This adds semantic meaning that the schema lacks (which only specifies string type and minLength). However, it does not elaborate on valid column names or value formatting rules.

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's purpose: to retrieve rows from the Requly dataset where a column equals a value exactly, with case-insensitivity. Although it lacks an imperative verb like 'find' or 'get', the intent is unmistakable and specific.

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 no explicit guidance on when to use this tool versus siblings such as dataset_search or dataset_compare. It does not mention alternative tools or specify conditions for selection, leaving the agent to infer usage from naming conventions.

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?

The description does not disclose any side effects, permissions, or read-only guarantees. Since annotations are empty, the description carries full responsibility. It mentions a result limit but does not state the absence of side effects or the nature of the operation (e.g., read-only).

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 immediately communicates the core functionality. It is front-loaded with the action and resource, with no redundant details or 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?

Given the simplicity of the tool, the description is mostly sufficient. It specifies the search criterion, case-insensitivity, and result cap. However, it omits details like default limit behavior, sorting, or error handling, which could be relevant but are not critical for a basic search operation.

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?

Only one of two parameters (query) has a description in the schema. The description adds no extra meaning; it repeats the schema's 'text to look for in any cell'. The 'limit' parameter is completely undocumented, and the description's 'up to 50' hint is not explicitly linked to it.

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's function: it searches rows of the Requly dataset for cells containing the query, case-insensitive, and returns up to 50 results. This is specific and distinguishes it from sibling tools like dataset_row (single row) or dataset_stats (aggregate data).

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 explicit guidance is given on when to use this tool versus alternatives. It does not mention scenarios where search is preferred over other operations, nor does it specify any prerequisites or limitations beyond the basic functionality.

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?

The description discloses non-trivial behaviors: it handles grouping commas and currency, and excludes non-numeric rows while counting them. However, it does not state whether the tool is read-only, whether it can fail, or any side effects, and there are no annotations to supplement this 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 a single, compact sentence that covers all essential information without redundancy. It lists the computed statistics and notes special handling, making it both concise and well-structured.

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 low complexity (one required parameter, no output schema), the description provides adequate context for the purpose and expected behavior. However, it omits details about the return format or potential errors, and does not reference the dataset schema, leaving some gaps 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 schema provides only the parameter name 'column' with no description, and the tool description does not explicitly explain what the 'column' parameter represents (though it is inferable). Given the 0% schema coverage, the description should compensate but does not sufficiently clarify the parameter's 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 explicitly enumerates the statistical operations (count, min, max, mean, median, sum) and identifies the target resource (numeric column of the Requly dataset), making the tool's purpose unambiguous. It inherently distinguishes itself from sibling tools like dataset_top or dataset_search, which have different focuses.

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 no guidance on when to use this tool instead of alternatives. It does not mention conditions like 'when you need summary statistics' or compare with sibling tools, leaving the selection decision entirely to the agent.

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 exist, so the description carries the full burden. It describes the ranking behavior but does not explicitly mention read-only nature, side effects, or limitations beyond what schema shows. It does not contradict, but adds minimal transparency beyond the obvious.

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 clear, concise sentence with a helpful example. 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?

Covers the core functionality and use case. Lacks details on return format or edge cases, but for a simple ranking tool it is adequate. No output schema exists, so not required.

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 clarifies that the column must be numeric and that ascending means lowest first, which partially covers the schema gap (only ascending had description). It does not explain 'limit' but that is self-explanatory. Adds useful meaning.

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?

States a specific verb ('rank') and resource ('rows') by a numeric column, and gives an example. Clearly distinguishes from sibling tools like dataset_search 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?

Provides a clear use case ('which is the most/least X') but does not explicitly compare to sibling tools or state when not to use. The intent is implied but not directly contrasted.

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