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

With no annotations, the description must carry the behavioral burden. It discloses the returned content (columns, numeric flags, row count, provenance banner), which implies a read-only metadata inspection, but it does not explicitly state that it has no side effects, requires no special permissions, or what happens on failure. The disclosure is adequate but leaves some implicit assumptions.

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: the first front-loads the exact information returned, and the second gives a clear call-order instruction. There is no redundant phrasing 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 parameterless metadata tool with no output schema, the description adequately covers what the agent will receive and when to call it. It is complete enough to invoke correctly, though it could slightly benefit from stating that it is a read-only operation or hinting at the output shape.

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 is empty and has 0 parameters, so there are no parameter semantics for the description to clarify. The description reinforces that invocation is simple and unconditional, matching the baseline of 4 for a zero-parameter tool.

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 enumerates what the tool returns—columns, numeric indicators, row count, and provenance banner for the Eposvo dataset—so an agent can tell this is a schema/shape discovery tool. It lacks an explicit action verb like 'get' or 'list', and it does not directly contrast with sibling tools, but 'Call this first to learn the schema' makes its purpose 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?

'Call this first to learn the schema' gives explicit guidance about when to invoke this tool relative to others. It does not name what alternative to use when schema knowledge is not needed, but the ordering and purpose are 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?

With no annotations, the description must carry behavioral disclosure. It does reveal useful behavior: rows are matched by 'any of' the values and appear 'in the order given.' However, it does not explain the output format, whether matching is exact, or how 'side by side' comparison is presented.

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 with no wasted words, and the core matching logic is front-loaded. It is slightly awkward grammatically ('whose column is any...'), but the structure is efficient and readable.

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 provides adequate invocation guidance, but important context is missing: no output schema, no mention of what a comparison result looks like, and no differentiation from closely related sibling tools. The title's 'side by side' behavior is not carried into the description.

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 does: 'column' is the field to match, and 'values' are the candidate values. It adds the OR semantics ('any of') and the ordering behavior, going meaningfully beyond the raw 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 identifies the resource (Eposvo dataset) and the action: retrieve rows whose column matches any of the given values, in a specified order. The 'X vs Y' phrasing helps differentiate it from sibling dataset tools, though it relies on the title for the explicit 'compare' verb.

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 ('for X vs Y questions') but does not explicitly mention when not to use the tool or how it compares to siblings like dataset_row or dataset_search. The guidance is present but underdeveloped.

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 disclosure. It does state the kind of information returned, implying a read-only metadata operation, but it does not explicitly confirm no side effects, nor does it describe any access or formatting constraints. This is acceptable but not deeply transparent.

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 compact and front-loaded: it immediately lists the key provenance fields and then gives a practical instruction on when to read it. Every sentence contributes meaning, with 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, parameterless provenance tool, the description covers the essential information: source, computed date, licence, and citation, plus the intended use case. It does not describe the output format, but given the simplicity and no output schema, 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?

The tool has zero parameters, so parameter semantics are not a concern. The description correctly focuses on what data the tool returns rather than inputs, and the empty schema needs no further elaboration.

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's purpose: returning provenance information for the Eposvo dataset, including source, computed date, licence, and citation. It is distinct from sibling tools like dataset_rows or dataset_stats, which deal with data content rather than metadata 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 gives a clear use case: 'Read this to attribute a figure correctly.' This tells the agent when to call the tool. It does not explicitly exclude alternatives or mention sibling tools, but the context is strong enough for a tool with no parameters and a focused provenance purpose.

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 matching semantics (exact, case-insensitive) and that rows are returned, but with no annotations it fails to explain more. It does not state whether the operation is read-only, what happens with zero or multiple matches, whether results are limited or ordered, or why the title's singular 'row' conflicts with the description's plural '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 no filler or repetition, and the key matching behavior is presented up front. It is appropriately concise, though the ambiguity and missing usage context prevent a top score.

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, no output schema, and 0% schema description coverage, the description is thin. It does not clarify return shape, limits, error behavior, or when to choose this over dataset_search and dataset_compare. The singular/plural ambiguity also leaves an incomplete picture for an agent.

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%, and the description only refers loosely to 'a column' and 'a value' without explicitly mapping them to the required 'column' and 'value' parameters. It offers some semantic context but does not sufficiently compensate for the lack of schema documentation.

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 core operation: returning rows from the Eposvo dataset where a column equals a value. It adds specificity with 'exactly (case-insensitive)', which distinguishes it from fuzzy search tools like dataset_search. However, the title says 'a row' while the description says 'rows', creating minor 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?

No guidance is given on when to use this tool versus alternatives such as dataset_search or dataset_compare. The description does not state exclusions, prerequisites, or situations where another sibling would be more appropriate.

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 transparency burden. It discloses two important behaviors: case-insensitive matching and a result cap of 50 rows. This goes beyond a bare 'search dataset' statement, though it does not mention sorting, default limit behavior, or empty-result 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 with no fluff. It front-loads the core result type ('Rows of the Eposvo dataset') and packs in matching semantics, case sensitivity, and result limit without wasted 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 search tool, the description is largely complete: it specifies what is searched, how matching works, and the maximum result count. There is no output schema, but the description's promise of 'rows' is sufficient for basic invocation; minor gaps like exact row shape and ordering are not critical for selecting or calling the 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?

Schema description coverage is 50%: only 'query' is described. The description adds useful meaning by clarifying that query matches substrings case-insensitively across cells and that results are capped at 50, which aligns with the limit parameter. However, it does not explicitly explain the limit parameter's role or default behavior, so it only partially compensates for the schema 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 states a clear action: returning dataset rows whose cells contain the query, with specific behavioral details (case-insensitive, up to 50 rows). This distinguishes it from siblings like dataset_stats or dataset_provenance by focusing on content-based row 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 when to use the tool: when an agent needs to find rows matching arbitrary text in any cell. However, it does not explicitly state when not to use it or mention any alternative sibling tools, leaving the routing decision mostly 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 available, the description carries the behavioral disclosure burden. It does this well by revealing that grouping commas and currency are handled and that non-numeric rows are excluded and counted. These are non-obvious behaviors beyond the basic concept of stats, though it stops short of specifying the exact output format.

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 well-structured sentence that front-loads the main output list and packs essential edge-case behavior into a parenthetical. No words are wasted and every clause adds information.

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 simple one-parameter interface and the absence of an output schema, the description provides a clear list of returned statistics and important parsing/exclusion behavior. It is adequate for an agent to invoke correctly, though it could further detail the exact response structure or behavior on empty columns.

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 the 'column' parameter refers to a numeric column of the Eposvo dataset, adding meaning beyond the bare string type and minLength constraint. However, it does not specify whether the column name must match an existing column exactly or how invalid column names are handled.

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 explicitly enumerates the computed statistics (count, min, max, mean, median, sum) and identifies the target resource as a numeric column of the Eposvo dataset. This clearly distinguishes it from sibling tools like dataset_top or dataset_search, which serve different purposes.

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 usage for numeric column analysis by listing statistics and handling of formatted numbers, but it does not explicitly state when to choose this tool over alternatives. No exclusions or alternative references are provided, leaving the when-to-use decision mostly 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 discloses the core behavior: returning highest/lowest rows by numeric column and supporting ascending order. It does not mention default limits, tie handling, null values, or the shape of the returned rows, leaving 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 description is one tight sentence that front-loads the core operation and adds a helpful natural-language use case. Every element earns its place, and there is no redundant padding.

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 mostly workable, but important call-time facts are missing: the default limit when limit is omitted, the output format, and how this tool relates to sibling row/stat/search tools. The low schema coverage and absence of an output schema make these gaps noticeable.

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 useful meaning by saying the column must be numeric, but it adds nothing about the limit parameter or its default, and the ascending behavior is already stated in the schema. The compensation is partial.

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 ranks rows of the Eposvo dataset by a numeric column and returns the highest or lowest ones, and the title reinforces the verb-resource pair. It does not explicitly call out sibling tools or contrast with dataset_stats/dataset_row, so it stops short of full differentiation.

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 this is for top/bottom-N natural language queries, giving an implicit usage cue. There is no explicit guidance about when to prefer dataset_top over dataset_stats, dataset_search, or dataset_row, and no exclusions are stated.

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