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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. It discloses the output contents (columns, numeric indicators, row count, provenance banner) and implies a read-only nature. It doesn't mention potential side effects or limitations, but for a schema-introspection call 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 entire description is one sentence that front-loads the essential output and the usage directive. No wasted words; it is appropriately sized for its purpose.

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 tool has no output schema and no parameters, the description must explain what it returns and when to use it. It covers both. It could specify the exact structure of the return (e.g., a list or object), but for an entry-point schema tool, the description is sufficiently complete for an agent to call 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?

There are zero parameters, and schema description coverage is trivially 100%. The baseline for 0 params is 4. The description adds no parameter-specific meaning, but none is needed. It also clarifies the dataset scope, which is valuable.

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 verb (learn/get) and the resource (columns, numeric flags, row count, provenance banner of the WallChartBook dataset). It explicitly differentiates from siblings by positioning itself as the entry-point schema tool, so an agent can immediately tell it apart from dataset_row 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?

Provides explicit guidance: 'Call this first to learn the schema.' This tells the agent when to use it. It doesn't list when not to use it or name alternatives, but the directive is strong and contextually clear given the sibling set.

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 of behavioral disclosure. It discloses the matching semantics ('any of the given values') and ordering behavior ('in the order given'), which adds real value beyond the schema. However, it does not mention edge cases, case sensitivity, or whether the returned rows include all columns, so transparency is partial.

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 that front-loads the action and resource, then adds the matching criterion, ordering behavior, and usage context. Every clause earns its place and there is 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 simple two-parameter read-only comparison tool with no output schema and no annotations, the description covers the core semantics, ordering, and usage context. It does not describe the return format or error behavior, but the simplicity of the tool and clear rules make it adequately complete 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?

Schema description coverage is 0%, so the description must compensate. It clearly explains both parameters: 'column' is the field to match on, and 'values' is the list of values to match with OR semantics ('any of'). The order-given note also clarifies how the values array is used. This is strong compensation for a two-parameter 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 names the specific resource (WallChartBook dataset), the action (returns rows), and the selection criteria (column matches any given values). The 'X vs Y' phrasing and 'side by side' title clearly differentiate it from single-row lookup (dataset_row) and free-form search (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 description explicitly ties the tool to 'X vs Y' questions, giving a clear when-to-use signal. It does not explicitly name sibling alternatives or state when not to use it, but the context is unambiguous enough for an agent to select it over row lookup or 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?

With no annotations, the description carries the full burden. It discloses what the tool provides (source, date, licence, citation) and implies a read-only operation via 'Read this', which is adequate for a provenance lookup. It does not discuss edge cases such as missing or incomplete provenance information, but the zero-parameter schema limits the risk.

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, front-loaded with the most useful content, and every phrase earns its place. The description is compact without sacrificing clarity.

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 a zero-parameter schema and no output schema, the description adequately explains the return values (source, date computed, licence, citation) and the reason to call it. It could specify the citation format or note that provenance may be unavailable, but for this simple tool the description is practically 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 input schema has zero parameters with 100% schema description coverage, so the baseline is 4. The description adds no further parameter meaning, and none is needed because no inputs are required.

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 specific resource (WallChartBook dataset) and the exact kind of metadata returned (source, date computed, licence, citation). It clearly distinguishes this provenance tool from data-manipulation siblings like dataset_search or dataset_stats, though it lacks an explicit verb such as 'returns' or 'gets'.

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?

'Read this to attribute a figure correctly' gives a clear, concrete use-case for when the tool should be invoked. It does not explicitly list alternatives or exclusions, but the purpose is specific enough for an agent to select it over the other dataset tools.

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?

The description discloses the key matching semantics (exact, case-insensitive), adding value beyond the schema. However, it uses 'rows' (plural) while the title says 'a row', leaving ambiguity about whether one or multiple matches are returned, and it says nothing about no-match behavior or return format. With no annotations, more behavioral disclosure would be expected.

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 a parenthetical note, containing no redundant words. It efficiently front-loads the dataset and the matching condition.

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 exact-match lookup, the description covers the core operation and names the dataset. However, there is no output schema and no annotations, so an agent still lacks clarity on whether one or multiple rows are returned and how this tool differs from dataset_search. The gap is moderate, not severe.

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 provides types and minLength. The description adds that 'column' is the field to match and 'value' is the exact value to look up, linking both parameters to the operation. It does not detail valid column names or any extra value constraints, but it does provide the essential meaning.

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 the tool returns rows from the WallChartBook dataset where a column exactly equals a value, with case-insensitivity. This clarifies the lookup operation, but it uses a noun phrase rather than an active verb and does not explicitly contrast with sibling search tools.

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 alternative guidance is given. The exact-match, case-insensitive phrasing implies it is intended for precise key lookups, but it never states when to prefer it over dataset_search 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses case-insensitive matching and a 50-row cap, but it does not mention default limit behavior, ordering, empty-result handling, or whether full row objects are returned.

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, efficient sentence front-loads the key behavior and includes only valuable modifiers. It avoids restating the title or schema redundantly.

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 tool, the description provides enough to invoke it correctly: dataset, query behavior, and result cap. However, with no output schema or annotations, it would benefit from stating the default limit and whether results are full row objects.

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 documents query's meaning but not limit. The description adds useful semantics by specifying cell containment, case-insensitivity, and the up-to-50 result cap. The default limit is left unspecified, but both parameters are otherwise understandable.

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 tool as returning rows from the WallChartBook dataset whose cells contain the query, with case-insensitive matching. This makes it easy to distinguish from sibling tools like dataset_columns, 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 matching semantics imply this tool is for full-text search across rows, but the description does not explicitly state when to use it over alternatives or mention any exclusions. Usage guidance is present only by 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 behavioral burden. It adds useful context by saying grouping commas and currency are handled and non-numeric rows are excluded and counted. It does not describe the output structure or edge cases such as an all-non-numeric column, so it is adequate but not comprehensive.

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, well-organized sentence. The metric list is front-loaded, and the edge-case handling is appended without padding or repetition.

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, the description supplies the returned metrics and the key data-cleaning behavior. It lacks only an explicit pointer to column discovery and the exact return format, but the low complexity makes this adequate 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?

Schema coverage is 0%, so the description must compensate. It clarifies that the single `column` parameter expects a numeric column from the WallChartBook dataset, which is valuable. It does not explain how valid column names are formatted or where to discover numeric columns, leaving a partial gap.

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 what the tool computes (count, min, max, mean, median, sum) for a numeric column, making the purpose unmistakable. It clearly distinguishes this tool from row/search/top/columns siblings by focusing on summary statistics.

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 strongly implied by the title and description: use this when you need summary statistics for a numeric column. However, it never explicitly names alternatives or states when not to use it, leaving the routing decision 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 transparency burden, yet it only restates the ranking behavior and the numeric-column requirement. It does not disclose limit behavior, default sort direction, output format, or what happens with non-numeric columns, which an agent would need to know.

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 compact sentence that front-loads the operation and resource. There is no filler or repetition.

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, no annotations, and only one of three parameters described in the schema, the description is too thin to be complete. It omits return value shape, limit semantics, and sort-direction behavior, all of which matter 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?

Schema description coverage is only 33%, so the description should compensate, but it only adds the 'numeric column' constraint and a vague most/least hint. It does not explain the limit parameter, its maximum of 50, or how ascending maps to 'lowest first', leaving the agent under-informed.

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 action — return the highest or lowest rows of the WallChartBook dataset ranked by a numeric column — and gives the use-case phrase 'which is the most/least X'. This clearly distinguishes it from siblings like dataset_row (single row), dataset_search (searching), and dataset_stats (aggregate statistics).

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' phrase gives a clear context for when to use the tool, but the description does not name alternatives or state when not to use it. No comparison to dataset_stats or dataset_search is provided, so the guidance remains 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.