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

With no annotations, the description carries the burden of disclosing behavior. It usefully reveals the returned information and the recommended call order, implying a read-only schema inspection. However, it does not explicitly state that it has no side effects or describe any rate/cost implications.

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 sentences with no filler. The output contents are listed first, and the usage guidance is front-loaded with 'Call this first,' making it easy for an agent to parse quickly.

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 tool with no output schema, the description covers the key facts an agent needs: what information is returned and when to call it. It could add an explicit note about the return format, but this is not essential 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?

The tool has zero parameters, so there is no parameter semantics for the description to clarify. The schema coverage is trivially complete, and the description appropriately focuses on what the response will contain rather than on nonexistent arguments.

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 concrete resource ('the Venbix dataset') and specifies the exact outputs: columns, numeric flags, row count, and provenance banner. It does not explicitly differentiate itself from siblings such as dataset_provenance, but the scope (schema shape overview) is clear.

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 directive: 'Call this first to learn the schema.' This tells the agent when to invoke the tool, though it does not explicitly describe when to prefer sibling tools like dataset_search or dataset_stats.

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?

There are no annotations, so the description carries the full burden. It discloses the row-selection logic and ordering semantics, which is valuable. However, it does not describe the output layout (despite the title saying 'side by side'), whether all matching rows are returned, or how unmatched values are handled.

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 with no filler. It front-loads the core behavior and ends with the intended use case. 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?

Given only 2 simple parameters and no output schema, the description conveys the essential return value: rows in the specified order. It does not describe the precise rendering of the side-by-side comparison, but for a lightweight retrieval tool this is a minor 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?

Schema description coverage is 0%, so the description must compensate. It does: 'column' is explained as the field to match against, and 'values' is explained as the list that determines both membership and output order. The minimum/maximum constraints are left to the schema, but the core semantics are clear.

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 the operation in concrete terms: it returns rows whose column matches any given values, in the order given. It also distinguishes itself from sibling tools by explicitly targeting 'X vs Y' comparison scenarios, so an agent can tell it apart from dataset_row 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 clearly signals when to use the tool ('for X vs Y questions') and implies the intended comparison use case. It does not explicitly name alternatives or exclusion conditions, but the context is unambiguous enough for a capable 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 behavioral burden. It clearly indicates this is a read-only informational tool that returns provenance metadata, and implies no side effects or destructive operations. It could explicitly state 'read-only' or describe the return format, but the intent is clear.

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 content is front-loaded and directly informs an agent about what the tool offers and why it should be used.

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, simple informational tool, the description provides everything needed to call it correctly: what it returns, the dataset it refers to, and when to use it. No output schema is required for this level of 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?

The tool has zero parameters, so parameter semantics are not a concern. The description fully explains what the tool returns without needing to compensate for schema gaps.

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 resource (the Venbix dataset) and a clear purpose: retrieving the source, date, licence, and citation. It distinguishes itself from sibling tools by focusing on provenance/metadata rather than data access or manipulation.

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 usage context. It does not explicitly list exclusions or alternatives, but the sibling names make the distinction obvious, and the zero-parameter interface makes this tool's use unambiguous.

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 usefully discloses that matching is exact and case-insensitive and that all matching rows are returned. However, it does not mention behavior for empty results, multiple matches beyond 'rows', ordering, limits, or potential side effects—though this appears to be a read-only lookup.

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, focused sentence with no filler. The title front-loads the 'exact key' concept, and the description efficiently adds the case-insensitive detail. 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 two-parameter exact-lookup tool, the description is largely adequate: it states what it returns and the matching rule. However, with no output schema, no annotations, and no sibling routing context, an agent still lacks guidance on when exactly to prefer this over dataset_search or what a returned row looks like.

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: 'column' is clearly the field to compare and 'value' is the exact value to match, with the case-insensitive nuance. It adds meaning beyond the bare schema, though it omits details about edge cases or special character handling.

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 (rows of a Venbix dataset) and the matching semantics (exact column-equality, case-insensitive). The title's 'exact key' phrasing helps distinguish this from a broader search, though it does not explicitly name a sibling 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?

No guidance is given about when to use this tool versus sibling tools such as dataset_search or dataset_compare. The condition 'where a column equals a value exactly' implies an exact-match use case, but there are no explicit usage rules, exclusions, or alternative recommendations.

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 useful traits beyond the schema: case-insensitive matching and the 50-row cap. However, it omits what happens when more than 50 rows match, result ordering, and whether this is a read-only operation.

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 tight sentence with zero filler. The scoping constraints (cell containment, case-insensitivity, 50-row cap) are all front-loaded and every word contributes to the 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 2-parameter read tool, the description is nearly complete: it states the result set (rows), the matching rule, and the cap. With no output schema, the return row structure is not described, but this is a minor gap for a straightforward search 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 coverage is only 50% (query is described, limit is not). The description partially compensates by adding case-insensitive semantics and an implicit 'up to 50' tie to the limit parameter, but it does not explicitly document the limit's meaning or the query matching behavior in full.

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: returning rows of the Venbix dataset, and adds concrete scoping details (cell substring match, case-insensitive, capped at 50). This specificity distinguishes it from sibling tools like dataset_row, dataset_stats, and dataset_columns, though it does not name them explicitly.

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?

Usage is implied rather than stated: an agent can infer this is the tool to use when searching for text across dataset cells, but there is no explicit guidance on when to prefer it over dataset_row or dataset_top, and no exclusions or alternatives are named.

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, and it does disclose meaningful behavioral details: it handles grouping commas and currency, and excludes non-numeric rows while counting them. This goes beyond a generic 'get stats' statement. However, it does not specify the return format, behavior on empty columns, or what happens if the column does not exist.

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 list of statistics and then packs in the key behavioral caveats. Every clause adds information; 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 parameter and no output schema, the description covers the core inputs and a couple of edge cases, which is adequate. But it leaves important gaps: the structure of the returned object, how the excluded non-numeric rows are counted (e.g., a separate 'excluded' field), and error behavior for missing or non-existent columns. The agent may be unsure what to expect from the output.

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 only defines 'column' as a string with no description, and schema coverage is 0%. The tool description compensates by explaining that the column must be numeric and that non-numeric values are handled in a specific way. This adds semantic meaning beyond the raw schema, though it could further clarify the exact column identifier format (e.g., exact name matching).

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Venbix dataset. This distinguishes it from the sibling tools (e.g., dataset_row, dataset_search, dataset_top) which are not aggregation tools. The verb-resource relationship is explicit and 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?

No guidance is provided on when to use this tool versus alternatives like dataset_search or dataset_top. There is no mention of exclusions, prerequisites, or scenarios where other tools would be more appropriate. The usage is only implied by the phrase 'numeric column', but no explicit when-to-use context is given.

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 states the core behavior (returns highest or lowest rows by a numeric column) but omits important details such as the return format (list of rows, values only), handling of missing or non-numeric data, default limit behavior, and whether the sort is stable. No side effects or permission requirements are mentioned. The description is thin and leaves many behavioral aspects to inference.

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 an explanatory quote. It is front-loaded with the key action and resource, and contains no extraneous information. Every word contributes to clarity, making it highly efficient.

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 3 parameters, no output schema, and no annotations, the description is incomplete. It fails to mention the output format (e.g., rows with all columns or just selected), whether pagination is supported, the default limit if not provided, and any constraints on the column (e.g., must exist, must be numeric). An agent would need to infer these aspects or make assumptions, which is risky 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 coverage is only 33% (only 'ascending' has a description). The description adds minimal parameter context: it implies 'column' should be numeric, but it does not explain 'limit' (e.g., default value, max) or elaborate on 'ascending' beyond what the schema already states. It does not compensate for the low schema coverage, leaving agents without a clear understanding of parameter usage and defaults.

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 by a numeric column, with the option for highest or lowest. The verb 'rank' and resource 'rows' are explicit, and it distinguishes from siblings like dataset_row (specific row), dataset_search (search), and dataset_stats (aggregate stats) by focusing on top N rows. The phrase 'which is the most/least X' reinforces the ranking purpose.

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 when an agent needs the top or bottom rows by a numeric column, but it does not explicitly mention alternatives or conditions for when not to use it. No sibling tools are referenced, and no exclusions are stated. The usage is clear but only implicitly conveyed.

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