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

With no annotations, the description carries the behavioral burden. It describes the read-only nature implicitly by listing what it returns and positioning it as a schema-learning call, but does not explicitly state that it has no side effects or require any authentication. It adds useful context about the output contents.

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 no filler. The first lists the returned information, the second gives a clear usage directive. Information is front-loaded and each word 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 parameterless metadata probe with no output schema, the description fully specifies what the agent can expect to receive and when to invoke it. It is complete for its purpose.

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, so the baseline is 4. The description correctly omits any parameter details because none exist; no additional meaning is needed.

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 precisely what the tool returns: columns, numeric indicators, row count, and provenance banner, and explicitly identifies it as the first call to learn the schema. This clearly distinguishes it from siblings like dataset_search 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 usage guidance by instructing to 'Call this first to learn the schema,' indicating when to use it. However, it does not mention when not to use it or name alternative tools, so it stops short of full guidance.

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 reveals the ordering behavior ('in the order given') and the OR logic ('any of the given values'), but it does not disclose whether the operation is read-only, how results are returned, error handling, pagination, or performance implications. For a simple read-like tool, this is a notable gap, especially without annotations to cover safety.

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, tightly worded sentence. It leads with the core behavior, then the ordering constraint, then the intended use case. There is no extraneous text, and it is structured so the most important information appears first. Ideal conciseness for this tool.

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 only two simple parameters and no output schema, the description covers the essential purpose and key behavior. However, it omits practical details like whether the operation is read-only, what happens if a value is not found, and whether results are limited. Since there are no annotations to fill these gaps, the description is not fully complete for an agent to use the tool without additional assumptions.

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 'column' is a field in the dataset and 'values' are the target values to filter on, and it adds the ordering semantics tied to the values order. However, it does not specify the expected format for 'column' (e.g., exact name match) or whether values are case-sensitive. The description adds some meaning beyond the bare schema but leaves room for 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 clearly states the operation: it retrieves rows from the PunchListWorks dataset where a specified column matches any of the given values, preserving the order of the values. It also provides the use case ('X vs Y' questions), which helps distinguish it from sibling tools like dataset_row (single row) or dataset_search (broader search). The verb 'compare' is implied but the actual behavior is explicit.

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 mentions the tool is for 'X vs Y' questions, which gives some context on when to use it, but it does not explicitly state when to prefer alternative tools or when not to use it. There is no mention of exclusions or comparisons to siblings, leaving the agent to infer usage from the phrase. This is adequate but not 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?

No annotations are provided, so the description carries the disclosure burden. It discloses the returned fields (source, date, licence, citation), which is the core behavior, but it does not explicitly state that the operation is read-only or describe the return format. For a zero-parameter lookup this is adequate, though not rich.

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 zero filler: the first enumerates the payload, the second gives the usage directive. The key content is front-loaded before the purpose statement.

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 and no annotations, the description covers the payload and the purpose. It doesn't specify the response format, which is a minor gap given how simple and single-purpose the tool is.

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 nothing for the schema or description to document; the baseline of 4 applies. The description's enumeration of returned fields also reassures the agent that no configuration or input is 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?

States precisely what the tool delivers — source, compute date, licence, and citation for the PunchListWorks dataset — and attaches a concrete use case, attributing a figure. The provenance content (licence/citation) clearly sets it apart from the data-content siblings like dataset_columns, 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?

"Read this to attribute a figure correctly" gives an explicit trigger condition for calling the tool. It doesn't name alternatives or state when not to use it, but the sibling set, all about data values rather than metadata, makes the boundary 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 includes an important behavioral detail—case-insensitive exact matching—beyond the schema. It does not disclose whether it returns one row or all matching rows, or how errors/no-match cases are handled, which leaves some behavior undocumented.

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 carries substantive information and no filler. The exact-match rule is stated immediately and the case-insensitive qualifier is appended as an efficient parenthetical.

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 tool, the description covers core selection behavior and leaves little ambiguity about how to form a call. However, without annotations or an output schema it omits expected result shape and no-match behavior, so it is 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 schema offers only names and minLength, so the description supplies the missing semantics by explaining that 'column' is the field to match on and 'value' is the exact value compared against it, case-insensitively. This compensates for 0% schema description coverage, though it does not enumerate valid column 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 title and description state a concrete operation: looking up dataset rows by exact column-value equality. The case-insensitive qualifier adds precision and distinguishes it from more open-ended sibling tools like dataset_search, though it does not explicitly name alternatives.

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 (case-insensitive)' gives a clear condition for when the tool is appropriate: exact key lookups. It does not explicitly say when to prefer dataset_search or another sibling, so the 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 behavioral disclosure burden, and it does disclose important traits: case-insensitive matching, matching against any cell, and a hard cap of 50 returned rows. It does not state default limit behavior or the return shape, but the core matching semantics are clearly described.

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, information-dense sentence with no filler. The subject, matching rule, case-sensitivity, and row cap are all front-loaded and every phrase contributes 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 search tool with one required parameter and no output schema, the description gives enough to invoke it correctly: what to query, how matching works, and the result cap. It lacks an explicit statement of returned row fields and sibling-tool selection guidance, but the operation is simple and the description covers the essential invocation 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?

The description adds meaning beyond the schema by clarifying that query matching is case-insensitive and cell-based, and by framing the limit as a 50-row cap. The schema's limit field only has min/max constraints, so the natural-language cap meaning is useful. It stops short of explaining default behavior if limit is omitted, but for two parameters this is solid coverage.

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 behavior: return dataset rows whose cells contain the query text, with case-insensitive matching and a 50-row cap. It is more specific than the generic title and clearly positions the tool as a cell-content search rather than a stats or row-lookup tool. It does not explicitly name sibling tools, but the function itself is unambiguous.

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 dataset_search should be used instead of dataset_row, dataset_stats, dataset_top, or related siblings. The intended use is only implied by the search behavior. No alternatives, exclusions, or preconditions are mentioned.

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 disclosure burden. It adds non-obvious details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This is valuable beyond the schema, though output format is not described.

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 front-loads the full list of returned statistics and then appends edge-case handling. There is no filler or repetition of the schema.

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 one-parameter tool with no output schema, the description names all returned metrics and the relevant data-cleaning behavior. The only minor gap is ambiguity about whether 'count' refers to included numeric rows or excluded non-numeric rows.

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 does so by clarifying that the column parameter must reference a numeric column and by explaining preprocessing behavior around commas, currency, and non-numeric values.

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 count, min, max, mean, median, and sum for a numeric column, which distinguishes it from sibling tools like dataset_search, dataset_row, or dataset_compare. The resource (PunchListWorks dataset) and scope (numeric column) are explicit.

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 clearly signals when to use it: when you need summary statistics for a numeric column. It does not explicitly contrast with sibling tools, but the numeric-column restriction is a strong usage context that prevents obvious misuse.

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 must disclose behavior itself; it does state highest/lowest ordering and numeric-column constraint. It omits details like default row limit, behavior when limit is omitted, tie handling, or returned fields, leaving notable behavioral 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?

One tightly written sentence that front-loads the operation, names the resource, and gives a memorable use-case phrase. There is no filler or redundant restatement of the schema.

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 annotations and no output schema, the description should clarify what calling it returns and how optional parameters like limit behave. The description leaves the default limit and output shape unspecified, so an agent cannot reliably predict the tool's behavior beyond the basic ranking concept.

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 while the description adds the 'numeric column' constraint and clarifies highest/lowest semantics, it adds no explanation for the limit parameter. The limit's optional behavior and interaction with ranking are left entirely to the schema's bare minimum/maximum bounds.

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 a ranking operation on rows by a numeric column, with a concrete use case ('which is the most/least X'). It is specific but does not distinguish itself from sibling tools such as dataset_stats or 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 'which is the most/least X' phrasing provides a clear context for when to use this tool. However, it never mentions alternatives or when not to use it, so it stops short of full routing guidance.

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