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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 behavioral burden. It reveals the tool is a read-only metadata inspection call by listing its outputs and recommending it as the first learning step. It does not mention detailed response formatting, but no destructive or side-effect behavior is implied or plausible for this 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 short sentences with the output inventory front-loaded and the usage recommendation in the second sentence. Every word contributes to the agent's understanding of what the tool provides and when to call it.

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 tool with no output schema, the description is complete enough: it names all relevant return contents and the recommended position in the workflow. An agent can invoke it correctly and know what to expect from the response.

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 accepts zero parameters and the input schema confirms this, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because the description has nothing to add beyond what the empty 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 clearly identifies the resource ('the Footfally dataset') and exactly what the tool returns: columns, numeric flags, row count, and provenance banner. It also positions the tool as the schema-discovery entry point, which distinguishes it from siblings like 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?

'Call this first to learn the schema' gives an explicit invocation order and makes the tool's role as the initial discovery step clear. It does not explicitly list when to use alternatives, but the first-call instruction is sufficient guidance for a parameterless schema-introspection tool.

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?

It discloses key behaviors: matching rows where the column equals any of the given values, and returning them in the order of the values array. However, with no annotations provided, it omits the return shape, handling of no-match values, and case sensitivity, so behavioral disclosure is only 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 description is a single sentence with no filler. It front-loads the dataset name and core behavior, and the 'X vs Y' note adds genuine usage context without wasting words.

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 tool with no output schema, the description covers the dataset, matching behavior, ordering, and intended use case. Minor gaps remain around the exact row format and error behavior, but nothing essential is missing 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?

Schema description coverage is 0%, so the description must compensate. It maps both parameters ('column' and 'given values') to their roles in filtering and adds the important ordering semantics of the values array, but it does not address edge cases like duplicate values, empty strings, or what happens when no 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 description states what the tool does: it returns rows of the Footfally dataset filtered by a column whose values appear in the given list, preserving the order of that list. This distinguishes it from siblings like dataset_search or dataset_row, though it lacks an explicit verb such as 'returns' or 'selects'.

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 a clear use case ('for "X vs Y" questions') that signals when this tool is appropriate. It does not explicitly name alternative tools or state when not to use it, so some routing is left 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 behavioral burden. It frames the tool as a read operation ('Read this') and lists exactly what information will be provided, implying no side effects. It does not discuss output formatting or access prerequisites, but for a zero-parameter provenance lookup these are 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 compact sentences front-load the returned fields and then state the intended use. No filler or redundancy; every phrase 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, no-input provenance tool, the description adequately covers what the agent will get and why it matters. The absence of an output schema is compensated by listing the concrete fields; only exact citation formatting details are left unspecified.

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 schema coverage is effectively complete, so there is nothing for the description to add about parameter meaning. The description appropriately focuses on the returned content instead of inputs.

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 resource (Footfally dataset provenance) and the attributes it returns (source, computed date, licence, citation), and 'Read this to attribute a figure correctly' makes the purpose explicit. It is distinguishable from sibling dataset_* tools by its metadata focus, though it lacks an explicit verb like 'returns'.

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 'Read this to attribute a figure correctly' gives a clear context for when the tool should be used. It does not explicitly name sibling alternatives or exclusion conditions, but the content type is distinct enough that an agent can select it appropriately.

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 disclosing behavior. It does disclose the key matching behavior: exact match and case-insensitivity. However, it does not mention whether multiple rows can be returned, what happens when no row matches, or whether the operation is read-only, though 'look up' implies a query.

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 no filler and conveys the core behavior efficiently. It could be slightly clearer grammatically, but it is appropriately sized and front-loads the key matching semantics.

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 lookup tool, the description provides the essential matching rule and dataset context. However, with no annotations and no output schema, it leaves some gaps: return format, multiple-match behavior, and explicit read-only intent are not stated. This is sufficient but minimal.

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 has 0% description coverage, so the description must compensate. It does add meaning by explaining that `column` is the field to search and `value` is the exact value to match, with case-insensitive behavior. Still, it does not elaborate on constraints such as needing an existing column name or how the values are compared beyond the stated case-insensitivity.

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 operation as looking up rows in the Footfally dataset by an exact column-value match. It distinguishes this from the sibling dataset_search by emphasizing exact equality and case-insensitivity, so an agent can tell it apart without opening other schemas.

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 the tool: when an exact, case-insensitive match on a column value is needed. However, it does not explicitly state when not to use it or mention alternatives like dataset_search for fuzzy or non-exact matching, 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 provided, the description carries the behavioral burden. It usefully discloses case-insensitive matching and the maximum result cap, but omits behavior like the default limit when not supplied, result ordering, and what fields are included in 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 description is a single sentence with all essential information front-loaded and no filler. Every clause adds meaning.

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, the core behavior is present, but with no output schema the description should clarify more about the returned rows, ordering, and the effect of omitting limit. These gaps could lead an agent to assume an unfounded default behavior.

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 50%: only query has a description. The description reinforces query semantics with 'case-insensitive' and paraphrases limit's maximum as 'up to 50', but it does not add much beyond the schema and does not state the default limit.

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 rows of a specific dataset where cells match the query, with case-insensitive matching and a 50-row cap. It is a concrete verb+resource description, though it does not explicitly contrast with sibling tools like 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 intended usage is implied: use this tool when you need dataset rows containing arbitrary text in a cell. However, there is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives such as dataset_stats or dataset_columns.

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 meaningfully discloses edge-case handling: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. It does not mention return format or error behavior, but for a simple stats tool these disclosures are strong.

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 communicates the output statistics up front, then appends the two important behavioral caveats in parentheses. There is no redundancy or unnecessary detail.

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 single-parameter aggregation tool with no output schema, the description lists the returned statistics and key input nuances, which is largely sufficient. It leaves minor gaps such as exact output formatting or behavior for an invalid or empty column, but these are not critical given the tool's simplicity.

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% (the only parameter 'column' has no description). The description compensates fully by explaining that the column must be numeric, that formatting like commas and currency is accepted, and that non-numeric values are excluded and counted. This adds critical semantics the schema alone lacks.

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 action (compute count, min, max, mean, median, sum) on a clear resource (a numeric column of the Footfally dataset). This is precise and distinguishable from the sibling tools like dataset_top or dataset_columns, which target different operations.

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 implied through the description: use when summary statistics for a numeric column are needed. However, no explicit when-to-use, when-not-to-use, or alternative routing is stated, leaving the agent to infer when this tool is preferable over sibling 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?

With no annotations, the description carries the full behavioral burden. It does disclose the core behavior—returning ranked rows by a numeric column—and implies a read-only query. It does not mention default ordering, handling of non-numeric columns, tie behavior, or output structure, leaving partial transparency.

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 one front-loaded sentence with a useful query pattern and no wasted words. It avoids repeating schema details and conveys the essential intent 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 three-parameter tool, the description plus schema is largely sufficient for selecting and invoking it. The absence of an output schema and annotations makes the lack of explicit return-format, default-limit, and read-only confirmation a noticeable gap, but not a blocking one.

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 adds meaning by specifying that the column must be numeric and that the result is ordered highest or lowest, which maps to the ascending parameter. The limit parameter is not explained in the description, though its name and schema constraints make it partly self-evident.

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 operation: returning the highest or lowest rows of the dataset by a numeric column, framed as 'which is the most/least X'. This distinguishes it from siblings like dataset_stats or dataset_row, though it does not explicitly name a contrasting sibling.

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' phrasing implies the intended use case, and the focus on rows by numeric column separates it from column/stats/search tools. However, there is no explicit when-not-to-use guidance or named alternative, so the usage guidance is mostly inferred rather than stated.

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