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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. It implies a read-only operation by listing the informational outputs, but does not explicitly state that it is safe, has no side effects, or requires no permissions. This is a minor gap given the tool's 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?

The description is a single, efficient sentence that front-loads the core outputs and ends with a usage hint. It contains no fluff and every word contributes to understanding.

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 has no output schema, so the description must convey the response contents. It lists the four data elements, but does not specify the response structure (e.g., JSON keys, data types). For a simple getter, this is sufficient but not fully detailed, and no prerequisites are mentioned.

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 for the description to add beyond the schema. The baseline score of 4 applies since no parameter documentation 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 states the tool returns specific schema information (columns, numeric flags, row count, provenance banner) for the Exitvo dataset. It also positions it as the first call to learn the schema, which helps differentiate it from siblings like dataset_stats or dataset_search, though it does not explicitly name 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 instruction 'Call this first to learn the schema' provides direct usage guidance, implying this is the entry point before using other dataset tools. It does not explicitly list alternatives or when not to use it, but the actionable direction 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 behavioral disclosure burden. It discloses the selection logic and ordering behavior, which is useful, but it does not mention output format, matching semantics (exact vs partial), case sensitivity, or what happens 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 sentence that conveys the core behavior, ordering, and intended use case with no filler. It is concise and immediately informative.

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 read tool, the description covers the essential selection logic and purpose. However, with no output schema and no annotations, it does not describe the return structure or side-by-side presentation beyond the title, leaving a minor gap.

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: 'column' is identified as the attribute to match, and 'values' are the ordered list of accepted values. It adds the key ordering semantic that the schema alone cannot convey.

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 dataset rows whose column matches any of the provided values, in the given order. The 'X vs Y' phrasing conveys the comparison use case, though it does not explicitly differentiate from sibling tools like dataset_search or dataset_row.

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 the tool is useful ('for X vs Y questions'), but it does not explicitly state when to use this tool over alternatives or mention any exclusions or prerequisites. Usage context is present but largely implicit.

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 the type of information returned (source, date, licence, citation), which is transparent about the output content. It does not mention side effects, but as a read-only metadata tool, that is expected. No contradictions with annotations (none exist).

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 with no filler. The key information (source, date, licence, citation) is front-loaded, followed by the usage context. It is appropriately concise and 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?

Given that the tool has no parameters and no output schema, the description covers all necessary aspects: what it returns and when to use it. It is complete for an agent to decide to invoke 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?

There are zero parameters, so the description correctly omits parameter details. The schema coverage is 100% (vacuously true with no params), and the baseline for zero parameters is 4, which is appropriate.

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: providing the source, date, licence, and citation for the Exitvo dataset. It uses a specific verb ('read') and resource, and is distinctly different from siblings like dataset_columns, dataset_search, etc., which handle different aspects of the dataset.

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 provides an explicit usage instruction: 'Read this to attribute a figure correctly.' This tells the agent when to use it (when attribution is needed). It does not explicitly mention when not to use other tools, but the sibling context makes the distinction 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 must carry behavioral disclosure. It does reveal that matching is case-insensitive and that the tool returns rows, which are useful behaviors. However, it does not mention potential multiple matches, no-match handling, or any read-only guarantee. The description is thin on behavioral detail beyond these points.

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, front-loaded with the core purpose. Every word adds value: 'rows', 'column equals a value', 'exactly', 'case-insensitive'. There is no redundancy or fluff, making it highly efficient.

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 lookup tool with no output schema and no annotations, the description adequately covers the essential function: returning matching rows. It specifies the matching rule and case sensitivity. It could mention what happens with no match or multiple matches, but for a basic row lookup, the description is largely sufficient.

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 does map 'column' and 'value' to the comparison operation: 'where a column equals a value'. This adds meaning beyond the schema's bare type constraints. However, it does not clarify whether 'column' is a name, index, or specific format, leaving some ambiguity.

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: retrieving rows where a specified column equals a given value exactly, with case-insensitivity. This distinguishes it from siblings like dataset_search (likely fuzzy) and dataset_top (top rows). The verb-resource structure is specific and unambiguous.

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 this tool: for exact, case-insensitive matching. While it does not explicitly name alternatives or state when not to use it, the specificity of 'exactly' and 'case-insensitive' strongly signals its niche relative to siblings. This is clear contextual guidance, though it lacks explicit exclusions.

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 case-insensitive matching and the 50-row cap, which is useful. But it does not clarify whether full rows are returned, what happens when no rows match, whether the limit applies as a default, or any ordering/performance behavior, leaving notable 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 entire description is one crisp sentence that front-loads the core behavior (rows with matching cells), then adds the case-insensitive and limit modifiers. 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?

Despite lacking an output schema, the description's 'Rows... up to 50' gives the agent a clear expectation of result shape and scale. For a simple 2-parameter search tool, this is largely sufficient, though a note on default limit or empty results 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?

Schema coverage is 50%: query is described in the schema, limit is not. The tool description adds the 'case-insensitive' nuance and confirms the 50-row ceiling, but it does not explain limit's default value or interaction with the query, so it only partially compensates for the schema gap.

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 action: returning rows of the Exitvo dataset whose cells contain the query, with a clear scoping detail (case-insensitive, up to 50). It clearly differentiates from siblings like dataset_row or dataset_columns by describing a content-based search rather than a fetch-by-key or structural 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 description implies usage: use this tool when you need rows matching a text query across cells. However, it does not explicitly mention when not to use it or name alternatives such as dataset_top or dataset_row, so 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, the description carries the burden of behavioral disclosure. It usefully states that grouping commas/currency are handled and non-numeric rows are excluded and counted, but it does not describe the return shape, error cases, or behavior for empty/all-non-numeric columns.

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 tight, front-loaded sentence that packs the statistics list and key parsing caveats without filler. Every clause adds 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?

The tool is simple with one parameter, but there are no annotations and no output schema. The description names the computed statistics yet does not specify the output format or resolve ambiguity around whether the count of excluded non-numeric rows is returned separately.

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. The description clarifies that this parameter refers to a numeric dataset column and explains preprocessing behavior, adding meaning beyond the bare schema for a single-parameter 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 names the exact operation (computing count, min, max, mean, median, sum), the resource (a numeric column of the Exitvo dataset), and the special handling of formatted numbers. This clearly distinguishes dataset_stats from siblings like 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 intended use is implied: use this when summary statistics for a numeric column are needed. However, there is no explicit statement of when not to use it or which sibling alternative to choose instead, leaving routing partially to inference.

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 states the core highest/lowest behavior and numeric-column requirement, but it does not mention default ordering, how limit applies, behavior with non-numeric values or ties, or whether the operation is read-only. This leaves important behavioral traits undisclosed.

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. It states the key idea compactly and the explanatory phrase adds value without excessive length.

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 annotations, no output schema, and only partial parameter documentation, the definition is too thin for fully confident invocation. It explains the purpose but leaves out how limit and ascending interact, what the returned rows look like, and edge-case behavior.

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, but it only adds meaning for the column parameter ('numeric column'). It does not explain the 'limit' parameter or connect the 'highest/lowest' wording to the 'ascending' parameter default.

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 conveys that dataset_top returns the highest or lowest rows by a numeric column, and the parenthetical 'which is the most/least X' clarifies the intended ranking use case. It is specific enough to be distinguished from siblings like dataset_search or dataset_stats, though it lacks an explicit verb beyond the title.

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 when this tool is appropriate, but the description does not explicitly contrast it with sibling tools such as dataset_search or dataset_stats, nor does it mention any prerequisites or exclusions. Usage guidance is present but not fully explicit.

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