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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 implies an introspection/read-only operation by saying 'learn the schema' and listing metadata outputs, but it never explicitly states that no data is modified or whether the call has side effects. No contradiction with annotations exists.

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 redundancy; the key outputs are front-loaded and the usage pointer is placed in the second sentence. Every clause contributes.

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 tool with no output schema, the description covers what is returned and when to call it. It is slightly short on the representation of the numeric-column flags and what exactly a 'provenance banner' looks like, but this is a minor gap for an entry-point metadata tool.

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 the description has no parameter meanings to add. The baseline of 4 applies, and the description appropriately focuses on output semantics instead.

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 the resource (Enrolvo dataset) and enumerates the returned contents: columns, numeric flags, row count, and provenance banner. It also frames the tool as the schema-discovery entry point, distinguishing it from siblings like dataset_search or dataset_stats, though it lacks an explicit tool verb such as '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?

'Call this first to learn the schema' is clear usage guidance, positioning this ahead of the other dataset tools. It does not explicitly name alternatives or say when not to use it, but for a zero-argument schema probe the guidance is adequate.

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 the matching semantics ('any of the given values'), the ordering behavior ('in the order given'), and the dataset scope. It does not mention exact-match/case-sensitivity or return format, but the core behavior is transparent and not misleading.

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 tight sentence that front-loads the mechanism and appends the intended use case. There is no filler, repetition, 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?

Given the low parameter count and clear schema constraints, the description covers the essential invocation details: which column to filter on, what values to provide, and the ordering behavior. It lacks an explicit note on exact matching or output shape, but for a simple comparison tool, this is reasonably 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?

Schema coverage is 0%, so the description must explain the parameters. It effectively defines 'column' as the dataset column to filter on and 'values' as the accepted values for that column, and adds that the order of values determines the order of returned rows. It stops short of clarifying that values are cell values or that the column should exist in the dataset, but it provides substantial semantic value.

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: fetch rows from the Enrolvo dataset where a specified column matches any of the given values, preserving the given order. The title and the 'X vs Y' phrase make the comparison use-case evident. It does not explicitly differentiate itself from siblings like dataset_row or dataset_search, but the filtering-by-multiple-values behavior is distinct.

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 use for side-by-side comparison questions ('X vs Y'), and the ordering behavior suggests this tool is meant for ordered comparison of specific values. However, it does not explicitly state when to use this tool over alternatives such as dataset_search or dataset_row, nor does it state 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.

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 transparently enumerates the returned content—source, computed date, licence, citation—making clear this is a metadata/read operation. It does not describe output formatting, but for a simple provenance lookup the disclosure is adequate.

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 concise sentences with no filler. The first sentence states exactly what information is returned, and the second gives the practical use case. The title reinforces the purpose without 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 lookup tool, the description is complete. It names the dataset, lists the provenance fields, and gives the calling context. No output schema exists, but the return values are described well enough for an agent to use the 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 baseline for such tools is 4. The description does not need to explain parameter meanings, and it does not attempt to. Nothing about parameter semantics is missing.

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 what the tool provides: the source, computed date, licence, and citation for the Enrolvo dataset. It also explains the intended purpose—attributing a figure correctly—which distinguishes it from sibling data-exploration tools like 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 description gives a clear usage context: 'Read this to attribute a figure correctly.' This tells the agent when the tool is relevant, though it does not explicitly exclude alternatives. Given the sibling tools are all about querying dataset contents, the attribution use case is sufficiently distinct.

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 does disclose one useful behavior: matching is exact but case-insensitive. However, it does not state whether all matching rows are returned, what happens when nothing matches, whether results are ordered, or any limits or errors.

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, focused sentence that immediately conveys the core matching rule. There is no filler, 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 simple two-parameter lookup, the description covers the essential semantics: column, value, exact matching, and case-insensitivity. However, with no output schema, no annotations, and no mention of return shape or sibling distinctions, the description leaves some operational context unspecified, though the low complexity makes this acceptable.

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 bare schema. It does add meaning by explaining that 'column' is the field to match on and 'value' is the exact (case-insensitive) value to look up. Still, it does not clarify valid column names, value formatting, or special cases.

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 says 'Look a row up by an exact key' and the description clarifies that it returns rows from the Enrolvo dataset where a column equals a value exactly, case-insensitively. This clearly identifies the operation and resource, though it does not explicitly contrast itself with sibling tools like 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 implies this tool is for exact-match lookups, but it gives no explicit guidance about when to use it versus dataset_search or other siblings. There are no stated exclusions, prerequisites, or alternative conditions.

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 disclosure. It covers important behavioral details: search is case-insensitive, matches are across cells, and results are limited to 50. It does not describe every edge case, but the core behavior is transparent enough for a search 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, focused sentence with no filler. The key behavior, matching target, case sensitivity, and result cap are all front-loaded and easy to scan.

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 and no output schema, the description covers the essential semantics: what is searched, how matching works, and the maximum result size. It could mention default limit behavior or return format, but these are not critical for correct selection and 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?

The schema documents the query parameter but not the limit parameter, so schema coverage is only 50%. The description adds useful context by noting case-insensitivity and a 50-row cap, which partially compensates for the missing limit description, but it does not explicitly tie the limit parameter to the cap.

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 a specific behavior: returning rows of the Enrolvo dataset where any cell contains the query, with case-insensitive matching and a cap of 50. This distinguishes it from sibling tools like dataset_stats or dataset_columns by focusing on content-based row retrieval.

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: call this tool when you need rows matching text in any cell. However, it does not explicitly state when not to use it or mention alternative sibling tools for different scenarios, so an agent must infer usage from the description alone.

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 full burden of behavioral transparency. It goes beyond a generic statement by disclosing that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. This gives the agent useful expectations about input normalization and edge cases, though it does not cover error behavior or missing-column handling.

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. It front-loads the list of statistics, then names the dataset, and adds edge-case behavior in parentheses. There is no redundancy, and every clause 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?

There is no output schema, so the description must convey what the tool returns. It lists all six statistics and mentions the excluded-row count, which covers the main return values. It does not describe the output shape or behavior for nonexistent columns, but for a low-complexity single-parameter tool the description is sufficient 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 0%, so the description must compensate for the empty schema. It does add meaning by specifying that the column must be numeric and that non-numeric rows are handled, but it never explicitly states that the 'column' parameter is the column name or its expected format. For a single required parameter the inference is straightforward, yet the semantic gap is not fully closed.

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 verb-resource pair: it computes summary statistics (count, min, max, mean, median, sum) of a numeric column. It also scopes the resource as 'the Enrolvo dataset' and clearly distinguishes this from siblings like dataset_columns, dataset_row, or dataset_top, which have different purposes. The title reinforces the same meaning, so an agent can select it accurately without ambiguity.

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 usage is strongly implied by the description: if an agent needs summary statistics of a numeric column, this is the tool. However, there is no explicit guidance about when to use this tool versus alternatives such as dataset_top or dataset_search, nor any mention of when not to use it. The description provides clear context but relies on inference rather than explicit routing.

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 state the read-only ranking behavior and highest/lowest orientation, but it does not disclose ordering details, default limit behavior, tie handling, or null handling, which would matter for a correct interpretation.

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 the key idea front-loaded. It has minor redundancy with the title but contains no filler 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?

The description is sufficient for a simple top-N tool: an agent knows the dataset, the numeric column requirement, and the high/low ordering. However, without an output schema, the tool still does not describe the exact return format, default limit, or edge-case 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 description coverage is only 33% since only 'ascending' has a description. The description adds meaning by clarifying that 'column' must be numeric and that rows are selected by high/low values, but it does not explain the 'limit' parameter, which remains undocumented in both the schema and the description.

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 that the tool returns the highest or lowest rows of the dataset ranked by a numeric column, which distinguishes it from siblings like dataset_stats and dataset_search. The title supplies the explicit 'Rank' verb, making the purpose easy to identify.

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 when to use the tool, but there is no explicit guidance about when not to use it or how it differs from dataset_stats, dataset_row, or dataset_search. The usage context is clear enough to infer, but not stated.

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