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

With no annotations provided, the description carries the full behavioral disclosure burden. It clearly reveals what the call returns: columns, numeric flags, row count, and a provenance banner, and implies a read-only schema-inspection operation. It could mention side effects or the absence of modification explicitly, but the content disclosure is solid.

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 returned information and end with an actionable directive. Every word earns its place, and the structure makes the tool's role immediately clear.

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 introspection tool with no output schema, this description is complete enough. It states what information will be received, identifies the target dataset, and tells the agent exactly when to invoke it, which is sufficient for correct usage.

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 input schema is empty, so there is no parameter meaning to add. Per the baseline guidance for zero-parameter tools, a score of 4 is appropriate because there is nothing the description needs to compensate for.

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 the columns, numeric-type indicators, row count, and provenance banner of the Jobcardo dataset, and explicitly frames it as the first call to learn the schema. The phrase 'Call this first' also positions it apart from sibling tools like dataset_compare, 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 instruction 'Call this first to learn the schema' gives clear usage context: this is the entry point for understanding the dataset before using other tools. It does not explicitly name alternatives or state when not to use it, but the 'first' guidance is a strong and unambiguous usage signal.

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 disclosure burden. It does meaningful work by specifying filter semantics ('any of the given values'), ordering behavior ('in the order given'), and that whole rows are returned. It does not mention edge cases such as no matches or duplicate values, but it gives enough behavioral context for a query 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, dense sentence that packs in the target resource, the filtering logic, ordering behavior, and the intended use case. The title adds the 'side by side comparison' framing. There is no redundancy or 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 two-parameter tool, the description covers what is selected, how filtering works, and how ordering is determined. Since there is no output schema, it would be helpful to state the exact shape of returned rows or behavior on empty results, but an agent can still invoke the tool correctly for comparison scenarios.

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 schema's lack of semantic detail. It does this well: 'column' is identified as the attribute to match, 'values' as the list of allowed values, and 'any of' plus 'in the order given' define matching and ordering semantics. This adds substantial meaning beyond the raw input 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 clearly states the operation: return rows of the Jobcardo dataset where the column matches any of the given values, preserving the given order. It also conveys the intended use case ('for "X vs Y" questions'), which makes the tool's purpose easy to grasp. It does not explicitly distinguish itself from sibling tools like dataset_search or dataset_row, so it is clear but not fully differentiated.

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' signals when this tool is appropriate, giving an agent contextual guidance beyond a bare functional description. However, it does not name alternative sibling tools or state explicit when-not-to-use conditions, so the guidance is clear but incomplete.

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, and it succeeds for a simple read-only metadata tool: it tells the agent exactly what information will be returned (source, date, licence, citation) and signals a non-destructive, informational nature via 'Read this'. It stops short of stating explicit side-effect-free behavior, but nothing in the tool suggests mutation or external impact.

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, both valuable: the first enumerates the returned metadata fields, the second gives a directive for the tool's appropriate use. Nothing is wasted, and the key content is front-loaded.

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 metadata-and-citation tool with no parameters, no annotations, and no output schema, this description covers the essentials agent needs: what data it returns and when to use it. No additional behavioral or return-format details are necessary to invoke 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 tool has zero parameters, so there is no parameter meaning to convey. Per the baseline for 0-param tools, a 4 is appropriate; the description and empty schema are fully consistent, leaving no ambiguity.

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 exactly what the tool exposes — source, computed date, licence, and citation — and pairs it with a clear action ('Read this to attribute a figure correctly'). This distinguishes it from the sibling tools, which all concern data access or statistics rather than provenance and attribution.

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 the intended use case explicitly: use this tool when you need to attribute a figure correctly. It does not name sibling alternatives or state exclusions, but the provenance content is so distinct from columns/compare/row/search/stats that the context is clear.

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 exist, so the description carries the full burden. It discloses exact matching and case-insensitivity, but it does not state whether all matching rows are returned or just one, what happens with no match, or any output/order behavior. This ambiguity is material for a lookup 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, and it front-loads the core operation. It loses one point for omitting enough behavioral detail while remaining one 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 tool with no output schema and no annotations, this description is too thin. It does not explain the return shape, multiplicity of results, or interaction with related tools, so an agent lacks enough context to invoke it confidently under varied conditions.

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 meaning of 'column' and 'value'. It only restates that a column equals a value, without defining valid column names, value formats, or matching constraints, leaving the parameters under-specified.

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 is clear: it retrieves rows from the Jobcardo dataset where a column equals a value exactly, and the title reinforces the exact-key lookup semantics. It does not explicitly contrast with sibling tools like dataset_search, but the exact-match wording inherently distinguishes it from broader 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?

There is no explicit guidance on when to choose this tool over sibling tools such as dataset_search or dataset_columns, and no exclusions are stated. The intended use is only implied by the phrase 'exactly (case-insensitive)'.

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 must carry the behavioral disclosure burden. It does disclose case-insensitive matching and that matches can occur in any cell, which is helpful. However, it does not mention default limit behavior, output format, or whether results are sorted or paginated.

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 that gets straight to the point. It front-loads the key behavior and avoids 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 search tool with only two parameters, the description covers the core invocation details: what is searched, where it is searched, case sensitivity, and the result cap. It does not specify default limit or output shape, but the low complexity makes this a minor gap.

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 describes the query parameter as 'text to look for in any cell', so the description adds limited new parameter-level meaning. The limit parameter has no description in the schema, and the tool description only restates the maximum of 50 rather than explaining its default or effect.

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 and resource: it returns dataset rows whose cells contain the query. It clearly differentiates this search tool from sibling tools like dataset_columns, dataset_row, or dataset_top by describing cell-level substring matching.

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 tool's purpose clear enough that an agent can infer when to use it, but it does not explicitly state when to prefer an alternative. No sibling tools or exclusion criteria are mentioned, so guidance is 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, the description carries the disclosure burden and does well: it reveals that grouping commas and currency symbols are preprocessed and that non-numeric rows are excluded and counted. It does not disclose output structure or behavior for an entirely empty/missing column, but the stated cleaning behavior is significant.

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, front-loaded sentence lists the stats first and then the edge-case behavior in a parenthetical. Every clause carries information, and there is no filler or duplication.

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, read-only statistics tool, the description covers the operation, input type, and key data-cleaning behavior. Since there is no output schema, a bit more detail about the returned format would be helpful, but overall the agent has enough to invoke 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 only defines column as a non-empty string with no description (0% coverage). The description adds that the column must be numeric and that formatted numbers (commas/currency) are accepted, which is genuinely useful. It still leaves the exact accepted column-name format unspecified, so it only partially compensates.

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 exact statistics (count, min, max, mean, median, sum) and the target (numeric column of the Jobcardo dataset), making its function unmistakable. It is clearly distinct from sibling tools like dataset_search, dataset_top, 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 use case is implied by the focus on numeric-column summary statistics, but the description does not state when to prefer this over sibling tools or what to do when the column is non-numeric. No explicit exclusions or alternative routing are provided.

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 restates the ranking concept and does not mention whether the operation is read-only, what the returned rows look like, how ties or non-numeric values are handled, or defaults for limit and ordering beyond what the schema already states.

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 is compact and readable, though it sacrifices helpful behavioral 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?

For a tool with no output schema and no annotations, the description omits important context: whether full row records are returned, what the default limit is when omitted, and what happens when the provided column is not numeric. The agent can infer the basic invocation but not the full contract.

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%, and the description does little to compensate. It clarifies that the column should be numeric, but it leaves 'limit' and 'ascending' mostly to the schema, with 'ascending' only documented inside the parameter definition.

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 clearly indicate that the tool ranks rows by a numeric column and returns the highest or lowest entries, matching the 'which is the most/least X' intent. It is distinct from siblings like dataset_search and dataset_stats, though it does not explicitly contrast itself with them.

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 the tool is for ranking-style questions, giving some usage context. However, it does not explicitly say when to use this tool instead of dataset_stats, dataset_search, or dataset_row, so the guidance remains implicit.

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