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

With no annotations, the description carries the burden of behavioral disclosure. It states exactly what the tool returns (columns, numeric flags, row count, provenance banner), making the read-only nature and output scope transparent. It does not cover format details, but for a zero-parameter metadata tool this is sufficient.

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 that front-loads the output contents and ends with a clear usage directive. Every phrase earns its place, with no filler 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 simple zero-parameter introspection tool, the description provides the essential output items and the right invocation timing. The lack of an output schema is partially offset by the enumerated return values, though column count is implied rather than stated.

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 description coverage is effectively 100% with an empty parameter object. Per the baseline for zero-parameter tools, the description need not add parameter meaning, and it does not attempt 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 specifies the tool's output: the columns, which are numeric, the row count, and the provenance banner for the FlatRateBook dataset. It also gives a direct call-to-action ('Call this first') that distinguishes it as the schema-learning entry point among siblings like dataset_row and 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 instruction 'Call this first to learn the schema' gives explicit usage context and implies this tool precedes the sibling analysis tools. It does not enumerate when not to use it or name alternatives, but the positioning is clear enough 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?

With no annotations provided, the description carries the behavioral burden. It states the selection rule (column value must be one of the given values) and output order (order given), making the retrieval behavior clear. It does not describe output row shape or error handling, but these are less critical for a simple filter 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 entire description is one dense sentence that front-loads the operation and dataset, adds the ordering detail, and ends with a practical usage tagline. 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 two-parameter read-only tool with no output schema, it covers the dataset, filter logic, ordering, and use case. It could be more explicit about exact-match semantics and whether full rows are returned, but nothing essential for invoking it is missing.

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 explain the parameters. It does: 'column' is the field to match, 'values' are the candidate match values, and the phrase 'in the order given' clarifies that value order controls result order. It meaningfully adds to the bare property 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 identifies a concrete operation: retrieving rows from the FlatRateBook dataset that match any given values in a specified column, preserving the given order. It is clearly more specific than a generic 'compare', but it does not explicitly contrast itself with sibling tools like dataset_search 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 phrase 'for "X vs Y" questions' gives a clear, practical trigger for when an agent should use this tool: comparing specific rows side by side. It does not name alternatives or state when not to use it, so it stops short of a full usage rule.

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 clearly indicates this is a read-only metadata retrieval tool ('Read this') and enumerates the content returned: source, date computed, licence, and citation. This is sufficient for a simple zero-parameter tool, though it does not discuss formatting or edge cases.

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, well-structured sentence that front-loads the tool's content and concludes with a clear usage directive. Every phrase earns its place with no 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, metadata-only tool, the description is fully complete: it names the dataset, lists the exact provenance fields, and tells the user when to consult it. An agent has everything needed to invoke this tool 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 the schema already reflects that with 100% coverage. Per the rubric, the baseline is 4 when there are no parameters, and the description adds contextual meaning about what the returned provenance metadata contains.

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 specific resource (FlatRateBook dataset) and details exactly what the tool provides: source, computation date, licence, and citation. The title reinforces the purpose. This clearly distinguishes it from the sibling data-manipulation tools like dataset_search and 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 description states when to use it: 'Read this to attribute a figure correctly.' This gives a clear use case and implies it is for citation/attribution rather than data exploration. However, it does not explicitly name alternatives or state when not to use it, though sibling names make the distinction fairly 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 full responsibility for behavioral disclosure. It mentions case-insensitivity, which is useful, but omits details such as return format (list vs single row), pagination, handling of missing columns/values, or whether the operation is read-only. This is a significant gap for a data-access 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 a single, concise sentence with no filler. It is efficiently phrased, though it sacrifices necessary detail for brevity.

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 leaves the agent without essential information: what the result looks like, how many rows may be returned, and how this differs from dataset_search. The tool is simple, but the description is too sparse to fully support correct invocation and result interpretation.

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, but it only restates that a column and value are used without adding any context about valid column names, value formats, or constraints. It provides no meaning beyond what the bare schema already conveys.

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 action: looking up rows in the FlatRateBook dataset based on exact column-value equality, and even specifies case-insensitivity. This distinguishes it from a typical search tool, though it does not explicitly name a sibling, so it is clear but not maximally differentiating.

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 guidance is provided on when to use this tool versus alternatives like dataset_search. It does not mention that this is for exact matches while search might be for fuzzy/partial, nor does it state any prerequisites or limitations.

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 discloses case-insensitivity and the 50-row cap, which are useful behavioral details. However, it does not mention default limit behavior, ordering, pagination, or error handling, leaving gaps for an agent relying solely on this text.

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 filler. It immediately states what the tool returns, the matching rule, and the cap, making it efficient and easy to parse.

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 simple and the description covers the core function, but it omits practical details like the default limit when 'limit' is not provided, whether results are sorted, or if all columns are returned. Since there is no output schema, these gaps could leave an agent uncertain about the exact response 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?

With only 50% schema coverage (query has a description, limit does not), the description compensates meaningfully. It clarifies that 'query' matches any cell (beyond the schema's 'text to look for in any cell') and adds case-insensitivity, while also reinforcing the limit maximum with 'up to 50'. This adds value beyond 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 states a specific verb ('search'), a clear resource ('FlatRateBook dataset'), and precise behavior (rows whose cells contain the query, case-insensitive, up to 50). It clearly distinguishes from sibling tools like dataset_stats or dataset_columns by focusing on content search across all cells.

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 when to use it (when you need to find rows matching a text query) but does not explicitly mention alternatives or when not to use it. There is no comparison to siblings or any exclusion criteria, so the guidance is minimal and relies on the agent to infer 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, the description carries the full burden of behavioral disclosure. It does disclose that grouping commas and currency are handled and non-numeric rows are excluded and counted, which is valuable context beyond the schema. However, it does not mention error handling for empty/missing columns, return format, or other edge cases, leaving some gaps.

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 that front-loads the list of statistics, then adds the data handling note. Every word earns its place; it is concise, clear, and well-structured with no 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?

Given the simplicity of the tool (one parameter, no output schema), the description covers purpose and data handling well. It implies the return includes those statistics but does not explicitly state the return structure or error behavior. Minor gaps remain, but the overall context is adequate for an agent to use it 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 schema provides only a bare 'column' string with no description (0% coverage). The description adds meaning by clarifying the column is expected to be numeric and explaining how non-numeric rows are handled. This compensates for the schema gap, though it does not specify format constraints beyond that, which is sufficient for a single parameter.

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 computes count, min, max, mean, median, and sum for a numeric column, specifically of the FlatRateBook dataset. This is a specific verb+resource that distinguishes it from siblings like dataset_columns (which lists columns) and dataset_search (which searches).

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 provide any when-to-use guidance or exclusions. It only describes what the tool does, leaving the agent to infer when to use it versus alternatives. There is no mention of conditions, alternatives, or context that would help select this tool over siblings.

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 the core read-only behavior of selecting highest or lowest rows by a numeric column, but it does not mention output shape, tie/null handling, invalid column behavior, default ordering, or limit defaults. This is minimally viable but leaves gaps.

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 a clarifying user-facing question. Every part earns its place, and the core operation is front-loaded without 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 simple three-parameter ranking tool, the description is nearly adequate: the operation is clear and the optional parameters are expressed in the schema. But without an output schema or annotations, the missing default-limit behavior and return shape leave an agent guessing on important details.

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 must compensate. It usefully clarifies that column must be numeric and connects ascending to highest/lowest, but it never mentions limit, its default, or its bounds. The schema's min/max partially covers limit, but only barely.

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 operation: returning the highest or lowest rows of the dataset ranked by a numeric column, with the natural-language gloss 'which is the most/least X'. This clearly differentiates it from siblings like dataset_stats, dataset_row, and dataset_search, though it does not name them 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 'which is the most/least X' gloss implies the intended use case for top/bottom-N ranking questions, and the contrast with stats/search/row tools is implicit. However, the description gives no explicit guidance about when to prefer this tool over a sibling 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.