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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 of behavioral disclosure. It accurately describes the returned metadata content and implies a read-only introspection call, but it does not explicitly state that it is safe/non-destructive or describe potential failure modes.

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 output contents are front-loaded and the usage instruction is placed in a separate, clear sentence.

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 metadata tool, the description covers what data is returned and when to call it. It could optionally mention the exact return format, but that is not essential for 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 tool has zero parameters, so there is nothing for the description to add beyond the schema. The baseline of 4 applies because 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 that the tool returns columns, numeric flags, row count, and provenance banner for the MultiplesBook dataset, and it explicitly frames the tool as the way to learn the schema. It is specific and actionable, though it does not explicitly contrast itself with sibling tools like dataset_stats or dataset_provenance.

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' gives clear temporal guidance on when to use the tool. It does not mention exclusions or alternatives, but the usage context is explicit and sufficient for an agent.

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. It does disclose meaningful behavior: row selection by column-value membership, OR semantics via 'any of', and output order according to the given values. It does not cover exact-match/case sensitivity, no-match behavior, or confirm the operation is read-only, which a fuller description could add.

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 compact sentence with no filler. The selection rule is front-loaded and the 'for X vs Y questions' usage context is added with minimal cost.

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 defines the selection and ordering behavior plus a usage context. It still omits the return format, behavior when no rows match, and guidance among sibling tools, so it is minimally adequate rather than 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 description coverage is 0%, so the description must compensate. It does relate the two parameters: 'column' is the field being matched and 'values' are the candidate values, with order emphasized. However, it does not explain the string/array types or the min 2 / max 10 constraint beyond what the schema already shows.

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 the rows of the MultiplesBook dataset for a specific column matching any of the provided values, in the order given. The title and the 'X vs Y' phrasing clarify it is for side-by-side comparison, but it does not explicitly name how it differs 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?

'For X vs Y questions' gives a clear intended use context, and the ordering note tells the caller that the order of the values matters. It does not explicitly mention alternatives or state when not to use it, so it does not reach the top score.

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 content returned (source, date, licence, citation) but does not specify the output format (e.g., plain text vs JSON) or any caveats like versioning or licensing nuances. This is a moderate gap for a metadata tool.

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, front-loading the key details (source, date, licence, citation) and immediately stating the use case. No wasted words; every sentence 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, parameterless metadata tool, the description adequately lists what will be returned. While it omits output format details, the lack of an output schema is not a major issue given the tool's simplicity. It is complete enough for an agent to call 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?

The tool has zero parameters and an empty schema, so the description need not explain parameter semantics. The baseline for 0-parameter tools is 4, and the description appropriately focuses on the returned information rather than input requirements.

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 provenance information (source, computation date, licence, citation) for the MultiplesBook dataset. This is specific and distinct from sibling tools that handle columns, comparisons, rows, search, stats, and top values, making the purpose 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 tells the agent to use this tool when needing to attribute a figure correctly, which provides clear usage context. It doesn't explicitly name alternatives or exclusions, but the distinction from siblings is implied by the metadata-focused content.

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 transparency burden. It does disclose case-insensitive equality matching, but it leaves unclear whether a single row or multiple rows are returned, and the title says 'a row' while the description says 'rows'.

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 with no filler. It is concise and the key qualifier 'exactly (case-insensitive)' is prominent, though it is terse enough to omit useful behavior details.

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 is too thin. An agent still lacks clarity on the return shape, single-vs-multiple rows, and how this tool relates to dataset_search in terms of lookup strategy.

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?

With 0% schema description coverage, the description helps by tying 'column' and 'value' to the equality comparison. It does not, however, explain valid column names, value formats, or what happens when multiple rows match.

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 and description together communicate an exact-key lookup over the MultiplesBook dataset. The qualifier 'exactly' distinguishes it from fuzzy or broad search, though it does not explicitly name the sibling search tool.

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 'by an exact key' implies the intended usage: use this when an exact column/value match is known. However, there is no explicit guidance about when not to use it or when to prefer a sibling like 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?

No annotations are present, so the description carries the full behavioral burden. It discloses case-insensitivity and the 50-row limit, which are useful. However, it does not describe the return format, ordering, empty-result behavior, or any permission requirements, so some uncertainty remains.

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 compact sentence that front-loads the main behavior and includes the two most important modifiers (case-insensitive, up to 50). Every word earns its place, with 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?

Given there is no output schema and no annotations, the description should cover response format and edge cases; it does not. The presence of similar sibling tools also argues for a sentence on when to use this search versus other dataset access tools. Still, the core matching behavior is well specified.

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 description maps the query parameter to cell-content matching and the limit parameter to a 50-row cap, complementing the partial schema. It adds the case-insensitive detail not present in the schema. With only 50% schema coverage, this partially compensates but leaves unspecified what happens when no limit is given.

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: returns rows of the MultiplesBook dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes it from sibling tools like dataset_row (single row) or dataset_stats (aggregates).

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 search use case but does not explicitly state when to prefer this over dataset_row or dataset_columns. There is no mention of alternatives or exclusions, so the agent must infer when this is the right tool. This is adequate but leaves routing 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 behavioral burden and does useful work by disclosing preprocessing (commas/currency handled) and row exclusion behavior. It stops short of describing edge cases or the exact return structure, but covers the main behaviors an agent needs for this simple read-only tool.

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 dense sentence front-loads the output statistics and then adds preprocessing caveats with no redundant phrasing. Every part 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 one-parameter tool with no output schema or annotations, the description covers what is computed, input preprocessing, and row handling, which is nearly complete. It lacks only finer edge-case details such as behavior when all rows are non-numeric or when the column is empty.

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 the essential meaning that it must be a numeric column of the dataset. It does not enumerate valid column names or mention how to discover them, which would be needed for full compensation at 0% schema description 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 states exactly what the tool computes (count, min, max, mean, median, sum) for a numeric column of the named MultiplesBook dataset. This clearly distinguishes it from siblings like dataset_search, dataset_row, and dataset_columns.

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 conveys that the tool applies to numeric columns and that non-numeric rows are handled, so an agent can infer when it is appropriate. However, it does not explicitly compare with sibling tools or state when a different tool should be used, leaving usage guidance 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 full burden of behavioral disclosure. It communicates highest/lowest ranking, but does not disclose behavior around ties, missing values, non-numeric column handling, or whether a default limit exists. This leaves important behavioral questions unanswered.

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 no filler. It is front-loaded with the core behavior and uses the quoted phrase to convey typical usage efficiently.

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 top-k ranking tool, the description plus schema is minimally adequate to call the tool. However, there is no output schema and no annotations, so the description should ideally state the default limit behavior and the return shape more explicitly to be fully self-contained.

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 low at 33%, but the description adds the critical meaning that 'column' must be numeric, which is not stated in the schema. The ascending parameter already has a schema description, and limit's min/max constraints are present, but the description does not add further semantics for limit or ascending.

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 'The highest (or lowest) rows of the MultiplesBook dataset by a numeric column' state a specific verb, resource, and selection criterion. The phrase 'which is the most/least X' clarifies the intent and distinguishes it from search/statistics siblings, though it doesn't explicitly name 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 description implies usage for ranking questions like 'which is the most/least X'. It provides a clear context cue, but it offers no explicit guidance about when not to use it or when a sibling such as dataset_stats or dataset_search would be more appropriate.

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