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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 what the tool returns (columns, numeric indicators, row count, provenance banner) and the expected usage behavior (call first). For a zero-parameter introspection tool, this is adequate transparency; no side effects are indicated, and the tool's nature makes them unlikely.

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 filler: the first enumerates the return contents, and the second gives the usage directive. Every word earns its place, and the key information is 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?

Although there is no output schema, the description explicitly lists everything the tool provides. With zero parameters and a simple introductory role, nothing an agent needs to decide whether to call this tool 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 rubric baseline is 4. The description adds useful context about what an agent will learn from the call, and there are no parameter semantics for the description to clarify.

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 (the Coshhvo dataset) and lists concrete outputs: columns, numeric flags, row count, and provenance banner. The imperative 'Call this first to learn the schema' makes its role clear and distinguishable from sibling tools that focus on rows, stats, search, 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?

It gives an explicit when-to-use instruction: 'Call this first to learn the schema.' It does not spell out when not to use it or explicitly name alternatives, but the 'first' ordering and schema-learning purpose provide clear context for an agent choosing among 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?

With no annotations present, the description carries the burden of disclosure. It clearly explains the selection and ordering behavior, which is the core trait. However, it does not describe the output format, matching semantics (e.g., exact match/case sensitivity), or behavior when no rows match, leaving moderate 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 a single concise sentence that front-loads the behavior and ends with the intended use case. Every word contributes meaning, with no redundancy 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?

For a two-parameter read-oriented tool with no output schema, the description explains the core mechanism well enough for invocation. But the title promises 'side by side' comparison while the description only says rows are returned in order, leaving the actual return layout ambiguous. Edge cases such as no matches or duplicate values are also unaddressed.

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 so by relating `column` to the field being matched and `values` to the accepted values that determine the row order. This adds real semantic meaning beyond the raw schema, even though it does not restate constraints like minItems or maxItems.

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 exactly what the tool does: it returns rows from the Coshhvo dataset whose column matches any of the given values, in a specified order. It also ties this to the concrete use case of 'X vs Y' questions, making its purpose distinct from generic row retrieval or 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 'for "X vs Y" questions' provides an explicit use case, and the 'order given' clause implies this is the tool when preserving a requested comparison order matters. It does not explicitly name sibling tools or state when not to use it, so it falls short of full guidance.

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. It discloses the informational contents and implies a read-only operation, but it does not explicitly state that it is read-only or mention any permissions or side effects. The disclosed content helps, but behavioral transparency is not complete.

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 concise sentences, front-loads the core content, and adds a practical usage note. There is no redundant or vague 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?

For a zero-parameter metadata tool, the description is complete: it names the dataset, enumerates the returned provenance fields, and indicates the intended use case. No output schema exists, but the description adequately describes the return contents.

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 no parameters, so the description cannot meaningfully add parameter-level semantics. The schema already covers this case fully, and the description appropriately focuses on what the tool returns rather than parameters.

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 exactly what the tool provides: source, computed date, licence, and citation for the Coshhvo dataset. It also links the tool to a concrete action — attributing a figure — making it easy to distinguish from the sibling data 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 clearly indicates when to use the tool: when you need to attribute a figure correctly. It does not explicitly name alternatives or exclusions, but the purpose is specific enough that an agent can infer when it applies versus sibling 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?

No annotations are present, so the description carries the burden. It discloses case-insensitive exact matching, a useful behavioral trait. However, it leaves ambiguity between title's singular 'a row' and description's plural 'rows', and does not state behavior for multiple matches or no 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?

A single sentence with no redundant information. The exact-match scope is front-loaded and the case-insensitive detail is included compactly.

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 query this is largely usable: an agent can infer the call parameters and the return is 'rows'. But there is no output schema and the description omits return shape, error handling, and the singular/plural outcome, leaving moderate room for misinterpretation.

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 define parameters. It explains that 'column' is the column to match and 'value' is the equality value, and adds a case-insensitivity caveat, though it does not provide concrete examples, allowed values, or formatting details.

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 states a lookup verb and the description specifies the resource ('rows of the Coshhvo dataset') and matching criterion (column equals value exactly, case-insensitive). It distinguishes from a fuzzy search sibling by emphasizing exactness, though it does not name an 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?

Use case is implied by 'exactly (case-insensitive)' — appropriate when an exact match is required rather than a search. No explicit when-to-use/when-not-to-use guidance or comparison with dataset_search or sibling 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?

There are no annotations, so the description carries the full behavioral burden. It does disclose useful behavior: matching is case-insensitive, applies to any cell, and returns at most 50 rows. However, it does not describe result ordering, output row shape, pagination, or behavior 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?

The description is a single front-loaded sentence with no filler or redundancy. Every phrase adds meaningful functional detail: scope, matching rule, case behavior, and result cap.

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 with two flat parameters and no output schema or annotations, the description is mostly adequate: it names the dataset, query semantics, and result cap. The main gap is the ambiguity around the limit parameter and the lack of any statement about what information the returned rows contain.

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 documents the query parameter as 'text to look for in any cell', and the description reinforces the cell-containment semantics. However, the limit parameter is not explicitly described in either the schema or the description, so it is unclear whether 50 is a default, a hard maximum, or how requesting fewer rows works.

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: return rows of the Coshhvo dataset whose cells contain the query, and it adds useful constraints like case-insensitivity and a 50-row cap. This distinguishes it from siblings like dataset_stats or dataset_row, which clearly 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 intended use is clear: choose this tool when you need to find dataset rows by arbitrary cell text. It does not explicitly name alternatives or say when not to use it, so it misses the top score, but the semantic context is strong enough for an agent to route correctly.

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?

There are no annotations, so the description carries the full burden. It discloses non-trivial behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not cover error cases or output format, but the core data-handling behavior is clearly communicated.

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 with no filler. The output statistics are front-loaded, followed by important data-handling caveats. Every part of the sentence contributes useful 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 one-parameter read-only statistics tool, the description lists all computed values and key data-cleaning behaviors, effectively acting as a return-value specification in the absence of an output schema. Minor gaps such as exact output field names and error behavior do not prevent correct 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?

The schema only says column is a string with minLength 1, providing no semantic guidance. The description compensates by clarifying that the column must be numeric and that parsing quirks like commas and currency are managed. For a single simple parameter, 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 operation: it computes count, min, max, mean, median, and sum for a numeric column of the Coshhvo dataset. This distinguishes it from siblings like dataset_search, dataset_row, and dataset_columns, which address different needs.

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 intended use is implied: use when summary statistics for a numeric column are needed. However, it does not explicitly mention alternative tools or exclusion criteria, so the agent must infer when this tool is preferable to siblings like dataset_top or dataset_search.

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 must carry the behavioral burden. It does disclose the core behavior: returning highest or lowest rows by a numeric column. But it does not mention default limit behavior, ordering ties, null handling, or what the returned rows look like.

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 compact and front-loads the key operation. The quoted 'which is the most/least X' adds illustrative value, though the description is a fragment and could have used the space to mention limit defaults.

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 3-parameter tool, the description is usable but leaves important gaps. Without an output schema or annotations, it should state whether it returns full rows, how many are returned by default, and what the output ordering exactly is.

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%, so the description must compensate. It usefully clarifies that column must be numeric and maps highest/lowest to ordering, but it does not explain the limit parameter or what happens when limit is omitted.

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 'Rank rows by a numeric column' and description 'highest (or lowest) rows ... by a numeric column' clearly state the action and resource. The ranking semantics are enough to tell it apart from siblings like dataset_row and dataset_stats, though it does not explicitly name those alternatives.

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' signals the intended use case for ranking questions. However, there is no explicit guidance about when not to use this tool or when to prefer siblings like dataset_stats or dataset_search.

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