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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 burden of explaining behavior. It discloses the key output elements, but it does not explicitly state that the tool is read-only, nor does it describe the output format or any error/edge-case behavior. This is adequate for a zero-parameter schema-inspection tool but not richly 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 two sentences with no wasted words. The output contents are front-loaded, and the usage guidance is brief and actionable.

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?

Even without an output schema, the description names the essential return items: columns, numeric flags, row count, and provenance banner. It is complete enough for an agent to understand what it will get and why this should be the first call, though the provenance banner concept could use a little more explanation.

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 input schema is empty with 0 parameters, so parameter semantics are not a concern. Per the baseline for zero-parameter tools, the description does not need to compensate for missing parameter documentation.

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 what the tool returns: the columns, their numeric status, row count, and provenance banner for the Attestroom dataset. The closing instruction, 'Call this first to learn the schema,' gives it a distinct role among siblings, though it lacks an explicit verb like 'returns' or 'lists.'

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?

'Call this first to learn the schema' provides explicit guidance on when to invoke the tool: before exploring the dataset. It does not mention alternatives or exclusions, but the precedence instruction is clear enough for an agent to sequence it 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?

With no annotations, the description carries the behavioral disclosure burden. It usefully reveals matching semantics ('any of the given values') and ordering ('in the order given'), but it does not describe the return format, limits, or how 'compare side by side' is actually rendered.

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, front-loaded with the resource and operation, with the use case appended. Every phrase earns its place and there is no redundant 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 retrieval tool, the description is mostly sufficient: it says which rows are returned and in what order. However, with no output schema and no annotations, the lack of any detail about the output structure or side-by-side representation leaves some ambiguity.

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 for the bare schema. It does clarify that 'column' is the field to match against and 'values' are the matching values whose order determines output order. It still omits details like case sensitivity and missing-value behavior, but the basics are covered.

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 clear operation: return rows from the Attestroom dataset whose column matches any of the given values, in the order given. It also names its intended use case, 'X vs Y' questions, making it easy to distinguish from siblings like dataset_row or 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 'for X vs Y questions' phrase gives clear context for when this tool is appropriate, and the value-matching semantics distinguish it from single-row or search tools. However, it does not explicitly name alternatives or state when 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.

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 clearly describes the content of the tool's response: source, computation date, licence, and citation. This is a simple read-only metadata lookup with no destructive or state-changing behavior implied, and the description makes that nature sufficiently clear.

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 short sentences that front-load the key content fields and end with the practical use case. Every word earns its place, and there is no redundant or vague 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 zero-parameter provenance lookup, the description is nearly complete: it names the dataset, the fields the user will receive, and the intended usage. There is no output schema, so a slightly more explicit statement of the exact return format would be a minor improvement, but it is not essential for correct tool selection.

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 the description needs to explain about parameter semantics. The description appropriately focuses on what the tool returns rather than how to configure 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 states what the tool provides: the source, computation date, licence, and citation for the Attestroom dataset. It is distinct from the sibling tools, which focus on columns, rows, stats, and comparisons, whereas this tool is about provenance and attribution. It lacks an explicit action verb like 'get' or 'returns,' but the intent is 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 sentence 'Read this to attribute a figure correctly' gives a clear condition for when to use the tool: whenever the user needs to cite or attribute the dataset. It does not explicitly enumerate when not to use it, but the sibling tool names and the narrow provenance scope make the appropriate use case obvious.

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 transparency burden. It discloses key matching behavior (exact, case-insensitive) and that rows are returned, but it does not mention whether multiple rows can be returned, no-match behavior, or any read-only/error characteristics.

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 dataset and match criterion. There is no filler or redundant restatement of the schema.

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 lookup tool, the core semantics are present, but the description lacks an output description (no output schema), match-multiplicity expectations, and explicit relation to sibling tools. It is adequate 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?

Schema coverage is 0%, so the description must clarify the two parameters. It does connect 'column' and 'value' to the match operation, indicating column is the field and value is the lookup value, but it offers no additional constraints, formats, or examples.

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: returning rows of the Attestroom dataset matching a column/value pair exactly, with case-insensitivity. The title's 'Look a row up by an exact key' reinforces the verb and resource, but it does not explicitly distinguish itself from the sibling dataset_search beyond the word 'exactly'.

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 use cases: exact, case-insensitive column-value lookup. However, it provides no explicit when-to-use/when-not-to-use guidance or alternatives such as dataset_search for fuzzy matching, leaving the agent to infer usage from the phrase 'exactly'.

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 and does reveal key behavior: case-insensitive substring matching across cells and a 50-row cap. It doesn't disclose default limit behavior when omitted, ordering, or lack of side effects, though read-only nature is implied by 'search'.

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?

Single sentence, front-loaded with the core operation, and no redundant words. It 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 simple two-parameter search without an output schema, the description says what is returned (rows) and how matching works, and the parameters are bounded by the schema. It lacks only minor details like the default limit and ordering, which are not essential for a basic call.

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 covers only query ('text to look for in any cell'); description compensates partially by explaining search semantics and the 'up to 50' result ceiling, which clarifies limit's role. It doesn't fully document the limit parameter's default or effect beyond the maximum.

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 concrete verb ('Rows ... whose cells contain') and resource (Attestroom dataset), making it clear this is a row-content search. It is implicitly distinct from sibling tools like dataset_columns and dataset_stats, but does not explicitly differentiate itself.

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 usage context clear: use it to find rows where any cell contains the query, with case-insensitive matching. It provides no exclusion criteria or pointers to alternatives among the sibling tools, so the guidance is adequate but not 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 and no output schema, the description carries the full disclosure burden. It does disclose two useful behaviors—grouping commas/currency are normalized and non-numeric rows are excluded—which signals that messy input will not cause an error. However, it is silent on output format, behavior for empty/all-non-numeric columns, and error handling for unknown column names, and 'counted' is ambiguous about whether excluded rows appear in the count or are reported separately.

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 packed sentence with no filler; the statistic list is front-loaded and every clause (normalization, exclusion, counting) carries information. This is an efficient use of the available space with zero waste.

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 one-parameter tool, the core calling information is present: which column to pass and what data-cleaning behaviors to expect. Gaps remain because there is no output schema, so the return shape is only implied by the statistics list, and error conditions (missing column, no numeric values) are unstated, potentially surprising an agent at the edge cases.

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 the sole parameter. It adds real meaning: the column is expected to be numeric, may contain grouping commas or currency symbols, and non-numeric rows are dropped. It stops short of giving examples of accepted formats and does not point the agent to dataset_columns for discovering valid column names.

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 enumerates exactly which statistics are returned (count, min, max, mean, median, sum) and binds them to a specific resource ('numeric column of the Attestroom dataset'), making the operation concrete. The verb is implied ('computes') rather than explicitly stated, and sibling tools are not named, but the specific statistic list makes confusion with dataset_columns, dataset_row, or dataset_top unlikely.

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?

No explicit when-to-use or when-not-to-use guidance is given, and no alternative sibling is named. The numeric-column emphasis and the note about currency/grouping handling imply this is the tool for column-level aggregates, but the agent must infer its boundary with siblings like dataset_top or dataset_row on its own.

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 full burden of behavioral disclosure. It only says rows are ranked by a numeric column, with no mention of whether the operation is read-only, how ties are handled, what happens with missing or non-numeric values, or whether a default limit applies.

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 wasted words, and the 'most/least X' framing is memorable and useful. The only issue is minor punctuation and quoting awkwardness, but the structure is otherwise appropriately concise.

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, combined with the schema, is adequate for making a basic top-N call: it names the dataset, the numeric column requirement, and the ordering direction. However, there is no output schema, and the description does not clarify what the returned rows look like or what the default limit behavior is, leaving some ambiguity for a full understanding.

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 describes the `ascending` parameter, so the description partially compensates by clarifying that `column` must be numeric and that 'highest/lowest' maps to ordering direction. However, the `limit` parameter is left semantically unexplained, and the description does not fully fill the gaps left by the low 33% schema coverage.

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 this tool returns the highest or lowest rows of the Attestroom dataset ordered by a numeric column. It distinguishes itself from sibling tools like dataset_row (single row), dataset_stats (aggregates), and dataset_search (filtering) by focusing on ranking/top-N behavior.

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 this tool is for extreme-value ranking questions, giving some usage context. However, it does not explicitly state when to use this tool over dataset_stats or dataset_search, nor does it mention any exclusions or alternative tools.

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