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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 full burden of disclosing behavior. It does describe the informational contents returned (columns, numeric flags, row count, provenance banner) and implies a read-only schema-introspection operation. However, it does not explicitly state that the tool has no side effects, nor does it mention permissions or error 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?

Two short sentences with no filler. The output contents are listed first, and the critical instruction to call first comes second. Every word contributes to the tool's purpose and usage.

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 covers the essential return categories: columns, numeric flags, row count, and provenance banner. It also provides workflow context by instructing the agent to call it first. Slightly more detail about the output shape or format would make it fully complete, but this is adequate 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 baseline is 4. The description does not need to explain parameter meaning because there are none; instead it clarifies what the output will contain, which is the relevant semantics for a no-argument introspection 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 clearly specifies what the tool provides: the dataset's columns, which are numeric, row count, and provenance banner. It also explicitly tells the agent to 'call this first to learn the schema,' which establishes its role as an introductory schema-discovery tool and distinguishes it from siblings 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 description gives explicit usage guidance: 'Call this first to learn the schema.' This tells the agent when in the workflow to invoke it. It does not enumerate exclusions or compare against siblings, but the ordering instruction is strong enough to guide correct usage.

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. It discloses the core selection rule and ordering behavior, which is good, but it does not describe what happens with missing values, duplicate values, or the exact shape of the returned 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 entire description is one compact sentence with no filler. The essential behavior is front-loaded, and the use case is appended as a short contextual cue.

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 tool with no output schema, the description provides enough information to invoke it correctly: choose a column, supply values, and expect rows in the given order. Additional detail about return format or edge cases would help, but the interface is simple enough that 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 description coverage is 0%, so the description must add meaning. It effectively explains that 'column' is the field to match against and 'values' is the list controlling which rows are returned and in what order. This compensates well for the sparse schema, though it does not name the parameters explicitly.

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 specified column matches any of the given values, in the order given. It distinguishes this from single-row lookup, free-text search, and aggregate/top tools, though it does not use an explicit verb like 'retrieve' or 'return'.

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 the intended scenario: comparing two or more specific rows identified by column values. However, it does not explicitly mention when not to use this tool or point to alternatives such as dataset_search or dataset_row.

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 disclosure burden. It does reveal the returned information categories, implying a read-only metadata operation. It does not state side-effect safety or response format, but for a zero-parameter metadata accessor that omission is minor.

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 the content list front-loaded and a user-oriented usage note at the end. No filler or repeated schema 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 description gives the tool's entire purpose, content list, and a concrete invocation trigger, which is adequate for a parameterless provenance lookup. Without an output schema it could have included more detail on the exact structure, but the listed fields are sufficient for an agent to know when and why to call it.

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 the schema is trivially 100% covered. There is nothing for the description to add about argument semantics, so the baseline score of 4 applies.

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 the exact content returned (source, computed date, licence, citation) for the named BreakerDesk dataset, which clearly distinguishes it from sibling tools that handle columns, rows, search, stats, and comparisons. It lacks an explicit action verb, but the noun-phrase framing is unambiguous and resource-specific.

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 statement 'Read this to attribute a figure correctly' gives a concrete when-to-use trigger tied to citation and attribution needs. It does not explicitly name alternatives, but no sibling tool serves the same provenance purpose, so the guidance is sufficient.

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 usefully discloses exact, case-insensitive matching and implies a read-only lookup. It does not mention return shape, handling of multiple matches, or error behavior, which leaves some behavioral ambiguity.

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 with no filler. The key distinction, 'exactly' and 'case-insensitive', is front and center, making the core behavior immediately understandable.

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?

There is no output schema, so the description should clarify the return contract, but it is ambiguous: the title says 'a row' singular while the description says 'rows' plural. It also does not state what happens when no rows match or when the column does not exist, leaving important gaps for an agent using the result.

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 map the parameters. It does: 'column' is the column to compare, and 'value' is the value to match. The case-insensitive exactness adds semantic detail, but there is no guidance on column name format or value types beyond string.

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: retrieve dataset rows where a specified column equals a given value exactly, with case-insensitive matching. It is conceptually distinguished from dataset_search by emphasizing exact equality, though it does not name the sibling explicitly.

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 'equals a value exactly' implies this tool is for exact-match lookups rather than fuzzy or broader search, which gives some directional guidance. However, it does not explicitly state when to prefer this tool over siblings like dataset_search or dataset_top, nor does it provide 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, cell-level containment, and the 50-row cap. It does not describe output order, default limit, or empty-result behavior, but the core search behavior is clearly specified.

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 states the resource, matching rule, case sensitivity, and row cap. No filler or repetition of schema 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 simple search tool with two parameters and no output schema, the description plus schema are nearly sufficient. Missing details are minor: default limit behavior and return 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?

The schema already documents query as 'text to look for in any cell'; the description adds case-insensitivity. The limit parameter is named only through the 'up to 50' phrase, with no description of defaults or interaction with the schema maximum. At 50% schema coverage, the description partially compensates but not fully.

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: finding rows whose cells contain the query, and adds useful semantics (case-insensitive, max 50). It does not explicitly contrast with siblings like dataset_row or dataset_stats, so it stops short of full sibling differentiation.

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?

Usage is implied: use this tool when you need to search dataset rows by text content. No explicit when-to-use or when-not-to-use guidance is given, and no alternatives are named, so an agent must infer the decision from the tool names and description.

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 full behavioral burden. It discloses some behaviors: grouping commas and currency are handled, non-numeric rows are excluded and counted, which is useful for understanding how raw data is processed. However, it does not mention whether the tool mutates data (it implies read-only but doesn't explicitly state it), or any errors that might occur, such as what happens if the column is absent or has no numeric values.

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 concise—a single sentence that lists the exact statistics and key data-handling behaviors. It front-loads the main purpose (summarizing a numeric column) and then provides processing details. It could be slightly more structured (e.g., separating the statistic list from the caveats), but it is effective and not verbose.

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 relatively simple with one parameter and no output schema, so the description covers the main functionality. However, it lacks information about how to specify the column if the dataset has multiple tables or aliases, and it doesn't mention what the response structure looks like (though the output schema is absent, the agent might benefit from a brief note on the return format). Given the simplicity, the description is adequate but not fully comprehensive.

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 clarifies that the 'column' parameter refers to a numeric column and implies the column exists in the BreakerDesk dataset. It mentions that grouping commas and currency are handled, which hints at data formats the parameter may accept, adding meaning beyond the schema's minimal definition. While it doesn't enumerate possible values, for a single string parameter this is sufficient.

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the BreakerDesk dataset, which is a specific verb-resource combination. It distinguishes itself from sibling tools that likely handle other dataset operations, though it does not explicitly mention any sibling by name.

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 by stating the tool computes column statistics and mentions handling of grouping commas/currency and exclusion of non-numeric rows, giving context for when it applies. However, it does not explicitly state when to use this tool versus alternatives like dataset_search or dataset_compare, nor does it state exclusions. Since no alternative names are mentioned, guidance is less explicit than ideal.

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, and it does convey the core read-only ranking behavior and that both highest and lowest orderings are possible. It does not disclose edge behavior such as tie handling, non-numeric column handling, or how the returned rows are shaped, so some behavior remains implicit.

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 sentence with a clarifying example in quotes; it front-loads the ranking operation and contains no filler or repetition. Every element 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 3-parameter ranking tool the core behavior is present, but with no annotations and no output schema the description still leaves default ordering, the role of limit, and the concrete return shape to be inferred. 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?

The description adds meaning by clarifying that 'column' must be numeric and that results can be highest or lowest ('most/least X'). Schema description coverage is only 33%, and the 'limit' parameter is left entirely to the schema without any semantic explanation.

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 returns: the highest or lowest rows ranked by a numeric column, phrased as 'which is the most/least X.' This is clearly distinct from sibling tools like dataset_search, dataset_stats, 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 'most/least X' phrasing signals a concrete use case for ranking questions. However, it does not explicitly explain when to prefer this tool over dataset_stats or dataset_row, nor does it provide 'when not to use' guidance.

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