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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 discloses the output contents, including the somewhat unexpected provenance banner, and implies this is an introspection/read step. It does not explicitly state that it is non-destructive or describe side effects, but for a schema-inspection tool the behavior is reasonably 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 two concise sentences: the first lists the output components, the second states the usage directive. Every word adds value, and the key guidance 'call this first' is front-loaded near the end but clearly emphasized.

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 schema-introspection tool, the description covers the return payload adequately and provides the essential usage context. With no output schema, listing columns, numeric flags, row count, and provenance banner gives the agent enough to know what will come back. It could be slightly more explicit about side-effect-free behavior, but the context is otherwise 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 tool takes zero parameters, so parameter semantics are inherently simple. The description correctly focuses on what will be returned rather than parameter details, and there is nothing missing for calling this tool correctly.

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 what the tool returns: columns, numeric indicators, row count, and provenance banner, and frames it as the way to learn the schema of the Clipboardo dataset. It does not explicitly name a sibling to differentiate from, but the content and 'call this first' instruction make its role 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 gives clear usage context: call this first to learn the schema. It does not explicitly state when not to use it or name alternatives, but the 'first' ordering implies it precedes other dataset tools and is sufficient for an agent to select it at the start of a workflow.

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 is the only source of behavioral disclosure. It usefully reveals equality-based matching over the 'any of' values and that output order follows the input order, but it does not describe the output presentation, exact-match/case sensitivity, duplicate handling, or empty-result behavior.

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 sentence contains the full selection semantics and the use case with no filler. It is front-loaded with the core behavior and ends with the purpose, making it easy to scan and act on.

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 tool is simple and the core selection behavior is clear, but because there are no annotations and no output schema, the description should say more about what the returned rows look like and whether a value with zero matches is silently omitted. The missing output-format detail is the main 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 has no descriptions for column or values, but the description connects column to the dataset column being filtered and values to the accepted row values. It also clarifies that the values order determines row order, adding useful semantics beyond the bare string/array 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 defines the result set: rows from the Clipboardo dataset matching the given values in a specified column, in the provided order. The 'X vs Y' phrasing signals comparison use and distinguishes it from dataset_row and dataset_search, though it relies on the title for the explicit verb rather than stating 'returns' or 'lists'.

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 'for X vs Y questions' clause gives an explicit when-to-use context: whenever an agent needs to compare specific rows or entities. It does not name alternatives or state when not to use it, but the comparison framing is enough to route the agent away from the single-row, stats, and search 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?

No annotations are provided, so the description carries the transparency burden. It makes clear the tool returns provenance information and is read-oriented ('Read this'), but it does not explicitly state side-effect behavior or return formatting. For a simple zero-parameter metadata lookup, this is adequate though not exhaustive.

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 with no filler. The concrete content fields are front-loaded, and the practical instruction follows immediately. Every 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 zero-parameter provenance lookup with no output schema, the description names the key returned fields and the intended use case. Nothing essential is missing for an agent to correctly select and invoke this 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 0 parameters, so the baseline is 4; the description does not need to explain parameter semantics. It instead adds value by describing the informational content the tool returns.

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?

Clearly identifies the tool as providing provenance metadata (source, computed date, licence, citation) for the Clipboardo dataset and ties it to the use case of attribution. This distinguishes it from the sibling dataset_* tools, which focus on columns, rows, search, stats, and comparison.

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 'Read this to attribute a figure correctly' gives a clear, actionable context for when to use the tool. It does not explicitly list exclusions or alternatives, but the use case is specific enough to guide an agent.

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 disclosure; it usefully reveals exact matching, case-insensitivity, and that multiple rows can be returned. However, it does not state whether the operation is read-only, what the response looks like, or how edge cases such as no match or multiple matches behave.

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 short sentence plus an informative title, with no filler. The key behavior—exact match and case-insensitivity—is front-loaded, and every 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 simple two-parameter lookup with no output schema, the description is largely sufficient: it names the dataset, the match predicate, and case sensitivity. It could add response shape or duplicate-match behavior, but those are minor gaps given the low complexity.

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 connect `column` and `value` to the matching operation and adds case-insensitivity, but it gives no guidance on what a valid column looks like, value formatting, or further disambiguation beyond the single sentence.

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 identify retrieval of dataset rows by an exact column-value match, with explicit case-insensitivity. This distinguishes it from search-like siblings, but it doesn't explicitly name a sibling or contrast fuzzy vs exact behavior, so it stops short of a 5.

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 statement about when to choose this tool over alternatives like dataset_search or dataset_compare. The exactness implies an exact-match use case, but no when-to-use or when-not-to-use guidance is 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?

With no annotations provided, the description carries the behavioral burden and does well by disclosing case-insensitive matching, cell-level containment, and the 50-row cap. It does not describe ordering, pagination, or exact return shape, but the core behavior is 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 a single, front-loaded sentence with no filler. It packs in the operation, resource, matching rule, case sensitivity, and result cap with maximal efficiency.

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 no annotations and no output schema, the description covers the essential behaviors an agent needs: what to search, how matching works, and the maximum result size. Minor gaps around pagination and return format prevent a 5.

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%: 'query' is described, while 'limit' is only hinted at through min/max. The description adds slight context with 'up to 50,' which aligns with the limit parameter, but does not explicitly explain how limit behaves or its default.

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 ('search') and resource ('Clipboardo dataset') and clarifies the matching semantics ('cells contain the query'). It clearly conveys what the tool does, though it does not overtly differentiate it from sibling dataset tools.

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 finding rows containing arbitrary text, but it does not explicitly say when to use this tool versus sibling tools like dataset_columns or dataset_comparison. There is no when-not-to-use guidance or alternative 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 provided, the description carries the full burden and does well by disclosing that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It stops short of specifying the output structure or behavior for empty or absent columns, but the disclosed parsing behavior is valuable.

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 that front-loads the computed statistics and resource, then adds a parenthetical with key parsing details. Every part earns its place; there is no filler or repetition.

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 one simple parameter and no output schema, the description covers the essential information: what is computed, on which column, and how edge cases like currency and non-numeric rows are handled. The missing exact return format and error behavior are minor for this level of complexity.

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 single 'column' parameter. It adds the important constraint that the column must be numeric and mentions currency/comma handling, which clarifies expected input. It does not provide examples or details about column name resolution, but the parameter itself is simple and self-descriptive.

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 computes count, min, max, mean, median, and sum for a numeric column of the Clipboardo dataset. This is specific and actionable, though it does not explicitly differentiate from sibling tools such as dataset_top 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 phrase 'numeric column' implies the appropriate use case: when summary statistics of a numeric column are needed. However, there is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives among the sibling tools, 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?

No annotations are provided, so the description must carry the behavioral burden. It discloses the ordering behavior (highest/lowest) but omits important traits such as how ties are handled, whether non-numeric columns cause errors, what the return payload contains, and whether the result is simply a filtered list or includes ranking metadata.

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 that conveys the core operation and the natural-language intent without redundancy. Every 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 simple ranking tool with no output schema and no annotations, the description gives enough to understand the basic operation, but it leaves gaps: no mention of limit semantics, default behavior, output shape, or edge cases. An agent could invoke it correctly but might misjudge boundary behavior or result format.

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 only 33%, so the description must compensate. It adds the key constraint that the column must be numeric, which is genuinely useful. However, it does not explain the limit parameter's role or the ascending default beyond what the schema's brief 'ascending' description states.

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 verb-resource relationship: it returns the highest or lowest rows of the dataset based on a numeric column, with the natural-language framing 'which is the most/least X'. This distinguishes it from row-fetch, search, stats, and column tools, though sibling differentiation is not 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 intended use case is implied clearly: use when you need the top or bottom rows ranked by a numeric column. However, there is no explicit guidance about when to prefer this over siblings like dataset_stats or dataset_search, and no exclusionary conditions.

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