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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 of behavioral disclosure. It does disclose the exact information returned, including the provenance banner, but it never explicitly states that the tool is read-only or side-effect free. For a zero-parameter schema inspection tool, this is a minor but notable 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 short sentences with no wasted words. The return contents are front-loaded and the usage guidance is placed clearly in the second sentence.

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 that there is no output schema, the description does a good job naming the key return components: columns, numeric identifiers, row count, and provenance banner. It could be slightly more explicit about the shape of the response, but it is complete enough for an agent to decide whether 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 and the schema coverage is 100%, so the description has no parameter semantics to add. Per the baseline for parameterless tools, this is adequate.

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 returns column names, numeric flags, row count, and the provenance banner for the DailyLogDesk dataset, so an agent knows what resource and scope are involved. It does not explicitly differentiate from siblings like dataset_provenance or dataset_stats, but the phrase 'Call this first to learn the schema' gives it a distinct role.

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 usage context: call this tool first to learn the schema. It does not mention when not to use it or point to alternatives, so it falls short of the highest bar, but the guidance is unambiguous 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 reveals two non-obvious behaviors: matching is disjunctive ('any of the given values') and output order follows the supplied value order, not the dataset's natural order. It does not detail exact-match or case-sensitivity behavior, but the core matching semantics are 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?

The description is a single purposeful sentence that front-loads the selection behavior and ends with the intended use case. There is no filler, repetition, or unnecessary 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?

For a simple two-parameter tool with no output schema, the description covers both inputs, the matching rule, ordering, and the intended question type. It omits explicit guidance about alternatives and edge cases, but these are minor given the tool's simplicity and the available sibling names.

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 supply meaning. It connects 'column' to a DailyLogDesk field and 'values' to the list of values to match, including the ordering implication. It does not explain constraints like minItems/maxItems or value format, although the schema covers those structurally, so the compensation is partial rather than complete.

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 returns DailyLogDesk rows matching any of the given column values, in the order provided, and ties this to 'X vs Y' comparison questions. It is specific about the resource and selection semantics, though it lacks an explicit verb like 'return' or 'compare' and does not name sibling tools for differentiation.

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' gives a clear intended context: comparing two or more specific values in the same column. It does not explicitly contrast with dataset_search or dataset_row, and it omits exclusions, but the use case is concrete enough for an agent to select it appropriately.

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 burden of conveying behavior. 'Read this' implies a read-only, non-destructive operation, and the listed content (source, date, licence, citation) describes what the agent can expect. It does not discuss edge cases or return formatting, but for a simple metadata endpoint this is adequate.

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 short sentences deliver the essential content and the usage trigger. No filler or redundant schema repetition; the title is slightly redundant but the description itself is tightly structured 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?

For a zero-parameter, no-output-schema provenance tool, the description is complete: it names every piece of information returned (source, computed date, licence, citation) and states when to read it. There are no hidden inputs or complex return expectations to document.

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 no parameter documentation burden. The description does not need to explain any input semantics; the baseline of 4 applies because there is nothing 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 the tool's resource (DailyLogDesk dataset) and the specific information it provides: source, computed date, licence, and citation. The imperative 'Read this to attribute a figure correctly' gives a concrete purpose, and the scope is obviously distinct from the sibling data-access 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 tells the agent when to use this tool: when attribution or citation for a figure is needed. It does not enumerate alternatives or exclusions, but the usage context is clear enough for a zero-parameter provenance tool.

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 adds a useful non-obvious behavior: matching is case-insensitive. However, with no annotations, it fails to disclose what happens with zero or multiple matches, or what the returned rows contain, leaving the tool's runtime behavior partially opaque.

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 immediately names the dataset and the exact matching rule. There is no filler or redundant wording.

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 core lookup behavior is present, but the title says 'a row' while the description says 'rows', creating ambiguity about whether one or many rows are expected. With no output schema, the return shape is unspecified, and valid column values are not documented.

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 only restates the relationship between column and value ('a column equals a value exactly') without explaining valid column names, value formatting, or matching nuances. The parameter names do most of the semantic work.

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 the resource (DailyLogDesk dataset) and the operation (return rows where a column equals a value exactly). The exact-match condition helps distinguish it from a general search tool, though it does not explicitly name a sibling alternative.

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 this tool is for exact, case-insensitive lookup rather than fuzzy or comparative search, but it never states when to prefer it over siblings like dataset_search or dataset_compare. No exclusions or alternative routing 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully discloses case-insensitive matching, cell-level search, and the maximum result count, but it does not mention response shape, pagination, ordering, or match semantics beyond substring containment.

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 compact sentence conveys the resource, matching rule, case sensitivity, and result cap without filler. The most important behavioral constraints are front-loaded 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?

For a two-parameter search tool with no output schema and no annotations, the description covers the essential behavior but leaves out return format details and explicit guidance about choosing this tool over sibling dataset tools. It is adequate for basic invocation 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?

The schema already documents the query parameter, and the description adds useful context such as case-insensitivity and the 50-row cap. The limit parameter is only described by its numeric constraints in the schema; the description partially compensates by mentioning the cap but does not explicitly explain how the limit parameter controls the result count.

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 defines the tool as returning rows from the DailyLogDesk dataset where any cell contains the query, with case-insensitive matching and a 50-row cap. This is distinct from sibling tools like dataset_columns, dataset_stats, and dataset_row, so an agent can identify this as the full-text search operation.

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 usage context is implied: use when you need rows matching arbitrary text in any cell. However, there is no explicit statement of when to use this tool over siblings or when not to use it, leaving some selection reasoning 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?

With no annotations provided, the description carries the behavioral burden. It usefully discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. This gives meaningful insight into data-cleaning behavior beyond the basic 'compute stats' operation.

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 well-organized sentence that front-loads the operation, names the target dataset, and folds the important data-cleaning caveats into the same statement. There is no wasted text.

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 summary tool with no output schema, the description covers what the tool computes, what data it operates on, and how anomalies are handled. It does not spell out the exact output shape or error behavior, but this is a minor gap given the tool's 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?

The schema only says 'column' is a non-empty string, and description coverage is 0%. The description compensates by explaining that the column must be numeric and part of the DailyLogDesk dataset, and by describing how numeric formatting is normalized. It does not enumerate valid column names, but the single-parameter shape makes the meaning sufficiently clear.

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 operation (compute count, min, max, mean, median, sum) on a specific resource (a numeric column of the DailyLogDesk dataset). This clearly distinguishes it from sibling tools like dataset_row, dataset_search, 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 description makes the intended use clear: summarizing a numeric column. However, it never explicitly says when to prefer this over siblings, nor does it mention that it is the wrong choice for non-numeric or row-level tasks. Usage context is implied rather than stated.

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 behavioral disclosure burden. It does state the core behavior (highest or lowest rows by a numeric column), but it does not describe limit/default behavior, tie handling, non-numeric handling, or the returned row shape. Without an output schema, this is a significant 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 one front-loaded sentence containing the resource, criterion, direction, and a usage phrase. There is no filler or redundant 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 tool with no annotations and no output schema, the description should clarify result count, limit semantics, and return value shape. It does not, so although the purpose is clear, an agent cannot confidently predict the outcome for all valid calls.

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 only 33%, with only 'ascending' described. The description adds the important fact that the column must be numeric and connects ascending to lowest-first ordering, but it does not explain 'limit' or its default behavior, leaving a key optional parameter under-specified.

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: rank rows of the dataset by a numeric column and return the highest or lowest rows. It is distinguishable from siblings like dataset_stats or dataset_row, though it does not explicitly name or exclude 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 phrase 'which is the most/least X' supplies the intended query context, implying use when the user wants extreme values. However, it does not state when to prefer dataset_stats, dataset_search, or dataset_row, nor does it give explicit exclusions or conditions not to use this tool.

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