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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the burden of disclosing behavior. It implies a read-only metadata operation by listing returned information, but it does not explicitly state that no data is modified or that no side effects occur.

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 and directly states the return contents and when to call. There is no extraneous detail or 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?

For a zero-parameter tool with no output schema, the description lists all key outputs and provides usage context. It is sufficient for an agent to decide when and why to invoke this tool.

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 and the schema description coverage is 100%, so the baseline is high. No parameter explanation is needed, and the description correctly focuses on the output.

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 the dataset's columns, numeric flags, row count, and provenance banner, and it is positioned as the first call to learn the schema. This distinguishes it from sibling tools like dataset_compare and dataset_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 'Call this first to learn the schema' gives explicit temporal guidance, implying it should be used before other dataset operations. It does not explicitly name alternatives, but the instruction to call first provides sufficient context.

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 does mention that rows are returned 'in the order given', which is a behavioral trait, but it does not state whether rows are returned in full, how results are paginated, or any side effects (though likely read-only). It lacks details on output format and error handling, which is a significant gap for a tool with no annotations.

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 that states the core behavior and use case without unnecessary words. It is concise and efficient, though it could be slightly clearer about the comparison aspect. Overall, it is well-structured and avoids fluff.

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 has no output schema and no annotations, the description is the only source of context. It covers the filtering and ordering logic, but does not describe what the returned rows contain (e.g., all columns or just the compared column), pagination, or any prerequisites. For an agent to correctly interpret the result, more detail is needed.

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 explains that 'column' is the column to filter on and 'values' are the values to match, and that the order of values determines the order of rows. However, it does not elaborate on constraints like min/max length or the array nature, which are already in the schema. It adds some meaning but not exhaustive.

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 selects rows whose column matches any of the given values, preserving the given order, and is aimed at 'X vs Y' comparisons. This distinguishes it from sibling tools like dataset_row (single row) or dataset_search (general search). It is specific about the operation and resource, though it could be more explicit about the side-by-side comparison aspect.

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 a clear use case ('for X vs Y questions'), which implies when this tool is appropriate, but it does not explicitly mention alternatives or when not to use it. There is no comparison to siblings like dataset_search or dataset_top, 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 provided, the description carries the burden of disclosure. It states what information is returned (source, date, licence, citation) but does not mention output format, pagination, or any side effects (though there are none for a metadata read). It is 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?

The description is extremely concise—two sentences—and front-loads the key content (what it provides) before the usage hint. There is no redundant or filler 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 no-parameter, no-output-schema metadata tool, the description fully covers what an agent needs: it specifies the exact fields returned and the purpose. Nothing is missing for successful 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 tool has zero parameters, so the description does not need to explain them. Per the rubric, a baseline of 4 is appropriate when no parameters exist, and the description adds no param-related details but also does not need to.

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 provides the source, computed date, licence, and citation for the Keysvo dataset. This is a specific verb+resource, and it is distinct from siblings like dataset_columns or dataset_stats which focus on other aspects.

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 advises reading this tool 'to attribute a figure correctly', giving a concrete use case. It does not explicitly contrast with alternatives, but given the sibling list, the purpose is unambiguous and the usage context 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 must carry the full behavioral burden. It only discloses that matching is case-insensitive, but omits critical details like whether multiple rows can be returned, how column names are handled, or any performance or error behavior. This is insufficient for a tool with zero annotation coverage.

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 front-loads the core behavior. It is not verbose and avoids redundancy, earning a high score for conciseness. However, its brevity limits the amount of useful information conveyed.

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 has only two parameters, no output schema, and no annotations, the description should provide sufficient context for an agent to call it correctly. It lacks usage guidance, return format details, and edge-case handling. The ambiguity between 'row' (singular) in the title and 'rows' (plural) in the description further reduces completeness.

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 states that 'column' is a column in the dataset and 'value' is the exact value to match, but adds little beyond the parameter names. It doesn't specify formats, whether column names are case-sensitive, or how values are represented.

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 rows from the Keysvo dataset where a specified column equals a value exactly, with case-insensitivity. It identifies the resource and operation distinctly, though it doesn't explicitly differentiate from sibling tools like dataset_search. The title reinforces the 'exact key' purpose, making it understandable.

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. No mention of when exact matching is preferred over fuzzy search (dataset_search) or when to use other sibling tools. The agent receives no context for selecting this tool over others.

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 mentions case-insensitive matching and a row limit, but doesn't state whether the operation is read-only, whether it has side effects, or what the return format is. The read-only nature is implied but not explicitly disclosed.

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, clear sentence that conveys the essential behavior without superfluous words. It's well-structured and directly addresses what the tool does.

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 description is sufficient to understand the core function and constraints (case-insensitive, up to 50 rows). It lacks details on output structure or error handling, but for a simple search tool, it provides enough context for basic 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 schema covers both parameters, but only 'query' has a description. The description of 'query' adds minimal value beyond the schema (just reiterates it's text to search). 'limit' has no description, and its semantics are only implied by its name and min/max. Overall, the description adds little to the 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 that the tool searches for rows containing the query across all cells, is case-insensitive, and returns up to 50 rows. While it doesn't explicitly differentiate from sibling tools like dataset_columns or dataset_stats, the purpose 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, typical use cases, or situations where a sibling tool might be more appropriate.

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 handling of grouping commas, currency, and exclusion of non-numeric rows, which provides some behavioral insight. However, without annotations, it lacks details on edge cases, errors, or operation side effects.

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 with a parenthetical for additional notes. It avoids unnecessary wording and is 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?

The description covers the core functionality and important data handling, but it does not specify the return format, error handling, or behavior when the column is entirely non-numeric. For a simple tool, this may suffice, but more detail would improve completeness.

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 only parameter 'column' is described only by context ('a numeric column of the Keysvo dataset'). No explicit format (name vs. index) or constraints are given, and the schema has no 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 description clearly states the tool's purpose: computing count, min, max, mean, median, and sum for a numeric column. It distinguishes itself from sibling tools like dataset_row or dataset_top by focusing on statistical summaries.

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 does not explicitly state when to use this tool compared to alternatives. It implies numeric summary usage but provides no comparative guidance against 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 to indicate read-only behavior or side effects. The description gives some behavioral insight (ordering by highest/lowest, numeric column) but does not mention defaults for limit or ascending, nor whether it returns full rows or just values. Since no annotations are present, 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 conveys the tool's purpose. It avoids unnecessary detail and is well-structured for quick comprehension.

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 ranking tool, the description covers the core logic but omits important context such as the shape of the returned data (full rows vs. specific fields) and the default value for limit. Without an output schema, this missing information could lead to incorrect assumptions about the tool's 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?

The schema provides a description only for 'ascending', leaving 'column' and 'limit' without explanation. The description adds that the column must be numeric and clarifies the meaning of ascending (most/least), but does not explain the limit parameter or its default behavior. Overall schema coverage is low, and the description partially compensates.

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 the highest or lowest rows by a numeric column, which is a specific verb and resource. It distinguishes itself from sibling tools like dataset_search (which likely filters) and dataset_stats (which aggregates) by focusing on ranking. The title reinforces this purpose.

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 for answering 'which is the most/least X' but does not explicitly state when to prefer this over alternatives such as dataset_search or dataset_stats. No direct comparison or exclusion is provided, leaving the agent to infer the appropriate context.

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