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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 states what information is returned, which is useful, but it does not explicitly say whether the operation is read-only, whether there are side effects, or how results are structured. 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?

A single, tightly written sentence that front-loads the returned fields and then gives a decisive usage directive. Every word earns its place; there is zero waste.

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-parameter schema-introspection tool with no output schema, the description fully explains what the agent will receive and when to call it. Nothing an agent needs to invoke it correctly 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, so the schema already carries no burden. The baseline of 4 applies, and the description appropriately adds no parameter-level detail because none is 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 identifies the tool's output: columns, numeric flags, row count, and provenance banner for the RemitDeck dataset. It lacks an explicit verb but is unambiguous about the resource and what it returns, and 'Call this first' distinguishes it from the 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 description explicitly instructs the agent to call this tool first to learn the schema, providing clear context for when to use it. It does not mention exclusions or alternatives, but the directive is strong and actionable.

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 disclosure burden. It conveys that the operation is read-only, filters by exact column equality, and preserves the order of the provided values. It does not disclose behavior for missing values, mismatched columns, or case sensitivity, but the core behavior is sufficiently 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 entire description is one compact sentence with the core behavior front-loaded and the use case appended after an em dash. Every word earns its place, and there is no redundant 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 tool, the description is largely adequate: it specifies input semantics, ordering, and purpose. However, the absence of an output schema leaves the return format unspecified, and edge cases like no matching rows or invalid columns are not addressed.

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 explains 'column' as the matching field and 'values' as the list of accepted values whose order determines the output row order. It does not detail array constraints or whether matching is exact, but it adds meaningful semantics beyond the bare schema.

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 does: it returns rows of the RemitDeck dataset whose column matches any of the given values, in the provided order. It distinguishes from siblings like dataset_row and dataset_search by specifying exact-value filtering and multiple row selection, though it lacks a direct verb like 'returns' or 'retrieves'.

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 a clear usage context (comparing entities side by side), but it does not explicitly name alternative tools or state when not to use it. This is implied guidance rather than explicit routing.

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 openly lists what the tool returns (source, date, licence, citation) and its intended purpose. It does not explicitly state it is read-only, but the lack of parameters and the metadata nature of the tool make side effects highly unlikely. This is reasonably 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 sentences with no fluff. The first sentence front-loads the tool's content, the second delivers the usage guidance. 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, no-output-schema tool, the description is complete: it names the returned fields and tells the agent why to call it. An agent can correctly invoke the tool and understand its result without any further documentation. There is no missing information that would prevent correct usage.

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 input schema has zero parameters, so there are no parameter semantics to document. According to the baseline for 0 parameters, the description is not required to compensate. It adds value by describing the output content, which is more meaningful than the empty schema.

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 provides — source, computation date, licence, citation — and names the specific resource (RemitDeck dataset). It lacks an explicit verb like 'get' or 'return', but the title and phrasing make the purpose unambiguous. It also distinguishes from sibling tools by focusing on provenance instead of data structure or statistics.

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 second sentence, 'Read this to attribute a figure correctly,' gives a clear, specific use case. However, it does not explicitly mention when not to use it or name alternative tools, though the sibling names imply alternatives. The context is sufficient for an agent to decide when to invoke 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?

With no annotations, the description carries full responsibility for behavioral disclosure. It reveals only case-insensitive matching but omits return format (whether one or multiple rows), behavior on no match, error conditions, or performance implications. This is insufficient for a tool with zero annotation support.

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 leads with the resource and matching condition. It avoids fluff and is easily parsed. However, it lacks a structured breakdown of parameters or return behavior, slightly reducing its usability.

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 tool with no output schema and no annotations, the description is incomplete. It omits edge cases, return format, error handling, and any differentiation from sibling tools. An agent would need additional context to use it 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 coverage is 0%, so the description must explain the parameters. It implies 'column' is the dataset column and 'value' is the match target, but does not specify allowed column names, value formatting, or type constraints. This minimal hint is insufficient to guide correct invocation without schema descriptions.

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 returns rows from the RemitDeck dataset where a specified column exactly equals a given value, with case-insensitivity noted. This distinguishes it from siblings like dataset_search (likely fuzzy) and dataset_columns (structure). The verb is implied but unambiguous, and the resource and matching condition are precise.

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 given on when to use this tool versus alternatives. It does not mention dataset_search for fuzzy matching or dataset_compare for comparisons, leaving the agent to infer applicability. No explicit conditions or exclusions 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 usefully discloses case-insensitive matching, cell-level search, and a cap of 50 results. However, it does not describe output shape, default limit behavior, ordering, or what happens when no rows match, leaving some behavioral aspects unclear.

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 with no redundant words. It front-loads the main resource ('RemitDeck dataset') and the core matching criterion. It could be marginally improved by using a proper verb and a clearer sentence structure.

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 essential behavior and the limit. However, with no annotations and no output schema, it lacks guidance on when to use this tool among siblings and omits details about default limit, result ordering, and output format. These gaps make it merely adequate rather than 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 coverage is 50%, with only 'query' described. The tool description adds meaning by clarifying that the query is a case-insensitive cell match and that 'up to 50' relates to the limit parameter. It does not explain the default limit if omitted, though the schema indicates limit is optional.

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 behavior: it returns rows of the RemitDeck dataset whose cells contain the query, case-insensitively, up to 50 results. Combined with the title 'Search the dataset', the purpose is unambiguous and distinguishable from siblings like dataset_row or dataset_top, though the description itself is a noun phrase rather than a full sentence with an explicit verb.

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 this tool versus alternatives such as dataset_row, dataset_top, or dataset_stats. There are no exclusions or conditions provided, so an agent must infer usage from the title and schema 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 the behavioral burden. It discloses meaningful behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This adds useful context beyond the title, though it does not describe what happens if the column is missing or contains no numeric values.

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 primary function and key statistics, then appends edge-case handling in a parenthetical. Every clause adds useful information with 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 one-parameter tool with no output schema, the description covers the operation, the returned statistics, and data-cleaning behavior. It is adequate for invocation, though a note about invalid or all-empty column behavior would make it 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?

The schema only defines column as a string with minLength 1, and schema description coverage is 0%. The description clarifies that the parameter refers to a numeric column of the RemitDeck dataset, but it does not specify column-name syntax, case sensitivity, or identifier requirements. It provides partial compensation for the undocumented schema.

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 names the verb (computes summary statistics), the resource (numeric column of the RemitDeck dataset), and the exact statistics produced: count, min, max, mean, median, and sum. This distinguishes it from siblings like dataset_row, dataset_search, and 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 when to use the tool: when summary statistics for a numeric column are needed. However, it does not explicitly state when not to use it or name alternative tools, leaving the agent to infer selection from the sibling list.

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 must carry the full behavioral burden. It states the basic sort-and-slice behavior and partially covers the ascending flag via the schema, but it does not disclose default limit behavior, tie handling, return format, or data type constraints beyond 'numeric column'.

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 fluff, and the example phrase 'which is the most/least X' adds a practical use-case context. It is appropriately brief, though the structure is slightly informal.

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 no output schema and no annotations, the description leaves important gaps: it does not explain what the returned rows look like, what the default limit is, or how ties are resolved. For a 3-parameter, 1-required tool, this level of incompleteness is inadequate for confident 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?

With only 33% schema description coverage, the description must compensate. It adds meaning by specifying that the column must be numeric and that results are highest or lowest, which helps interpret the `column` and `ascending` parameters. However, the `limit` parameter is completely undescribed in both schema and description.

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 verb (rank) and resource (RemitDeck dataset rows) and specify the key qualifier 'by a numeric column'. It distinguishes itself from siblings like dataset_search and dataset_row by framing the operation as returning the highest or lowest 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?

There is no guidance on when to use this tool versus alternatives such as dataset_stats or dataset_search. The only implicit hint is the phrase 'which is the most/least X', which suggests a use case but does not explicitly exclude sibling tools or state conditions.

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