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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 clearly enumerates what the tool returns: columns, which are numeric, row count, and provenance banner. It does not explicitly state read-only or side-effect-free behavior, but the described metadata retrieval strongly implies 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?

Two sentences with no filler. The first sentence lists the returned information, and the second gives a direct usage directive. Everything 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 fully covers what the agent needs: what data to expect and when to call it. There are no hidden inputs or ambiguous behaviors left unexplained.

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 100% schema description coverage, so the schema imposes no burden. The description adds value by explaining what the output will contain, which is meaningful even though no parameters require clarification.

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 what the tool provides: columns, numeric flags, row count, and provenance banner for the Huddlevo dataset. It also frames it as the first call to learn the schema, which distinguishes it from siblings. It lacks an explicit verb like 'returns' or 'lists,' so it doesn't quite reach a 5.

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 contextual guidance: 'Call this first to learn the schema.' This tells the agent when to use the tool. It does not mention alternatives or when not to use it, so it stops short of full exclusionary 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 must disclose behavioral traits itself. It states that rows are returned 'in the order given' (the order of the values list), which is a key behavior. However, it does not mention whether this is a read-only operation, what happens when no rows match, whether it returns full rows or a subset, or any limits on the dataset size. It adds some value beyond the schema but lacks depth.

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, tightly worded sentence that front-loads the core functionality and includes the ordering behavior and usage hint. There is no redundancy or filler; 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 tool with no annotations and no output schema, the description should fully specify what the agent needs. It explains the input semantics and the ordering, but does not describe the return structure (e.g., full rows, columns included) or edge cases like empty results. It is adequate for a simple filtering tool but leaves room for ambiguity about the output format.

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 schema only provides types and constraints. The description adds meaning by explaining that 'column' is the field used to filter and 'values' are the values to match, and that the output respects the order of values. It does not clarify details like exact match semantics, case sensitivity, or whether column must be a valid column name, but it gives the core filtering logic that the schema omits.

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 verb and resource ('rows of the Huddlevo dataset') and clarifies the filtering mechanism (column matches given values) and ordering. It includes a purpose hint ('for X vs Y questions') that distinguishes it from general search, but does not explicitly name alternatives among siblings.

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' implies a usage context for comparisons, but there is no explicit guidance on when to use this tool versus dataset_search, dataset_row, or dataset_top. No exclusions or alternatives are mentioned, leaving the agent to infer the appropriate scenario.

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. It discloses the returned content but does not mention side effects, authentication, or failure modes. However, as a read-only metadata tool, the description is minimally adequate but could be more explicit about the return format.

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 concise sentences with no fluff. The core content is front-loaded, and the usage hint is clear.

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 lists the key provenance fields and the purpose (attribution). It lacks the exact structure of the response, but for a simple metadata tool, it's largely sufficient. Given no output schema, a bit more detail on the response format 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?

There are no parameters, and the schema is empty. Per the baseline for zero parameters, a score of 4 is appropriate 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 explicitly states the tool provides the source, computed date, licence, and citation for the Huddlevo dataset. This clearly differentiates it from sibling tools that handle columns, comparisons, rows, searches, statistics, and top-level views.

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 instructs the agent to read this when attributing a figure correctly, providing a clear use case. It doesn't explicitly mention alternatives, but the context of siblings makes it obvious this is for provenance, not data manipulation.

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 case-insensitive exact-match behavior, but it does not say whether all matching rows are returned, what happens if no row matches, or how the output is structured. Given the simple read-like operation, this is minimally 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?

The description is a single sentence with no filler. The essential distinction ('exactly, case-insensitive') is included, though the phrasing 'The rows... where' is slightly awkward.

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 tool with no output schema and no annotations, the description provides the core matching logic but omits result-shape details and edge-case behavior. The small parameter surface keeps the burden modest, so a minimum-viable level is reached.

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 needs to explain the two parameters. It meaningfully connects 'column' and 'value' into the matching predicate, but it does not clarify accepted column names or value format beyond the schema's generic string type.

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 that the tool returns rows from the Huddlevo dataset matching an exact column-value comparison, and the title adds the 'look up' verb. The 'exactly (case-insensitive)' clause hints at the distinction from search-like siblings, though it does not explicitly differentiate 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 description implies a usage context: use this tool when an exact, case-insensitive match is needed. However, it does not explicitly state when to prefer it over sibling tools such as dataset_search, nor does it mention alternatives or when not to use 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully reveals case-insensitive substring matching and the 50-row result cap, but does not mention return format, ordering, empty-result behavior, or whether the limit has a default.

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 that front-loads the object, action, matching behavior, and cap. There is no filler or redundant repetition of the tool name.

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 conveys the core search behavior but omits result shape, ordering, default limit behavior, and usage context relative to sibling tools. It is minimally adequate but not 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 only 50%, with query documented but limit not. The description adds meaning by clarifying case-insensitive matching across any cell and the 50-row cap, but it does not explicitly explain the limit parameter beyond what the schema's min/max already imply.

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 verb and resource: it returns rows of the Huddlevo dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly differentiates it from sibling tools like dataset_columns or dataset_stats.

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 to find rows matching a text query. However, there is no explicit guidance on when to prefer it over alternatives such as dataset_row or dataset_compare.

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 adds useful context: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This goes beyond the title, though it could say more about empty-column or missing-value behavior.

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 tightly written sentence that front-loads the list of statistics and places the caveats in parentheses. Every phrase adds information, and there is 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 single-parameter tool, the description covers the core statistics and important parsing/exclusion behavior. It does not specify the return format or mention sibling alternatives, but the tool is simple enough that these gaps are minor.

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 non-empty string, so the description adds meaning by specifying it must be a numeric column of the Huddlevo dataset. This compensates well for the 0% schema description coverage, though it does not detail how column names should be referenced.

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 returns summary statistics for a numeric column of the Huddlevo dataset, explicitly listing count, min, max, mean, median, and sum. This makes it easy to distinguish from siblings like dataset_columns 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 usage for numeric summary statistics and notes handling of non-numeric rows, but it does not explicitly say when to prefer this over sibling tools or when not to use it. There is no mention of alternatives such as dataset_columns for discovering column names.

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 disclosure burden. It explains that rows are ordered by a numeric column and that both highest and lowest rows are possible, but it does not describe return format, tie-handling, or why some rows may be excluded. This is adequate but minimal.

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 and front-loads the main purpose. The illustrative 'which is the most/least X' earns its place, though the 'Huddlevo dataset' branding adds minor noise.

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 relatively simple read-only ranking tool, the description covers the primary intent but omits important operational details such as the effect of limit, default ordering behavior, and how it differs from sibling tools. No output schema or annotations are present, so more context would be valuable.

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 should compensate, but it only adds 'numeric column' as a hint. The limit parameter is left unexplained, and the description does not connect 'highest or lowest' to the ascending parameter. The schema already documents ascending, so the description adds little beyond it.

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 operation as returning the highest or lowest rows of the dataset by a numeric column, using the concrete example 'which is the most/least X'. This conveys the core ranking behavior, though it does not explicitly distinguish itself from siblings like dataset_search or dataset_stats.

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 a use case for answering top/bottom questions, giving some practical guidance. However, it does not say when to prefer this tool over alternatives such as dataset_stats, dataset_row, or dataset_search, nor does it mention exclusions.

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