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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 lists what the tool returns but does not mention side effects, error behavior, or performance characteristics. As a simple metadata query, this is adequate but not fully 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?

Two sentences with no wasted words. The key information (what is returned and when to use) is front-loaded and immediately useful.

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 no output schema, the description sufficiently explains the return content (columns, numeric flags, row count, provenance banner). It could be slightly more explicit about the format of the return, but for a schema discovery tool it is complete enough.

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 schema coverage is 100% (empty schema), so there are no parameter details to add. The baseline of 3 applies because the description does not need to explain parameters that do not exist.

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 verb ('call this first to learn the schema') and resource ('columns, numeric flags, row count, provenance banner'). It distinguishes itself from sibling tools like dataset_stats or dataset_provenance by focusing on schema shape.

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?

Explicitly tells the agent to call this tool first to learn the schema, which is strong usage guidance. It does not enumerate alternatives or when-not-to-use, but the directive is clear and actionable.

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 does some of the behavioral work: it discloses that rows are filtered by 'any of' the given values and returned in the given order. But it does not explain the 'side by side' presentation, exact-match semantics, empty-result behavior, or whether this is a safe read operation, leaving notable transparency 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?

A single compact sentence front-loads the core behavior and the intended use case. Every phrase earns its place, with no repetition of schema fields 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 two-parameter tool, the core selection behavior is covered, but with no output schema and no annotations, the description omits how results are actually presented (despite the title promising 'side by side'), any edge-case behavior, or confirmation that this is a read-only operation. These are meaningful gaps for an agent deciding whether and how to invoke 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 0%, so the description must compensate. It does: it connects 'column' and 'values' by explaining that rows are selected when their column value matches any value in the array, and that output order follows the order of the supplied values. This adds meaning beyond the bare JSON schema, though it omits exact match-versus-substring semantics.

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 retrieves Reconzy dataset rows where a given column matches any of the supplied values, preserving the order of the values. The 'for X vs Y questions' ending signals its comparison purpose. It does not explicitly differentiate from sibling tools such as dataset_search, 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?

The phrase 'for X vs Y questions' implies when the tool is appropriate, and the schema's minimum of 2 values reinforces comparison use. However, the description gives no explicit guidance on when not to use it or how it differs from alternatives like dataset_search or dataset_row.

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 lists the output fields (source, date, licence, citation), which is useful, but it does not explicitly state that this is a read-only operation or mention any side effects. The read-only nature is implied by the content, but not stated.

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 packs the essential information: what the tool returns and why to use it. Every word earns its place, with no redundancy 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 simple metadata tool with no parameters and no output schema, the description fully covers what an agent needs: the specific data fields returned and the use case. There is no missing information that would hinder 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, and the schema coverage is trivially 100%. Per the rubric, a zero-parameter tool gets a baseline of 4. The description does not need to explain parameters, and it correctly focuses on the output.

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 returns the source, computation date, licence, and citation for the Reconzy dataset. It specifies the exact resource (Reconzy dataset) and the purpose (attribute a figure correctly), distinguishing it from sibling tools that focus on data exploration like columns, stats, and 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 it: when you need to attribute or cite a figure. It does not explicitly exclude alternatives, but the purpose is clear enough for an agent to infer this is the right tool for provenance questions. No explicit 'when not to use' is given, but the context is sufficient.

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 burden; it discloses case-insensitive equality and plural row results, but omits return format, empty-result behavior, and error 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 concise sentence with no filler, and the title reinforces the core action immediately.

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 behavior; a minor gap is that it does not clarify whether multiple matching rows are returned or what happens when no match exists.

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?

Parameter names are self-explanatory and the description links column and value, but there is no parameter-level detail about what column identifier format is expected or how values are interpreted.

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?

Title and description clearly state an exact row lookup by column/value equality, with case-insensitive matching, and this is distinct from the broader search/aggregation tools in the sibling list.

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 'exact key' and 'case-insensitive' give clear context for exact-match lookups, but there is no explicit comparison with dataset_search or other sibling 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?

With no annotations provided, the description carries the full burden. It discloses case-insensitivity and a limit of 50 results, which is useful. However, it omits behavioral details such as read-only nature, handling of empty results, ordering, or pagination behavior. The disclosure is partial but 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?

A single sentence that is front-loaded with the key action and resource. Every word contributes meaning, with no fluff or repetition. The structure is efficient and immediately clear.

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 no annotations, no output schema, and only 2 parameters, the description is too sparse. It fails to mention whether the tool is read-only, what happens when no rows match, the default limit if not specified, or the structure of the returned rows. For a search tool, this leaves the agent uncertain about critical operational 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?

Schema description coverage is 50% (query is described, limit is not). The tool description adds meaning to the limit parameter via 'up to 50', which clarifies its purpose. It does not explain the default limit when omitted, nor does it add anything beyond the schema for query. This partially compensates for the missing schema description but leaves 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 states a specific verb (search) and resource (Reconzy dataset rows), and specifies the matching criterion (cells contain the query) and case-insensitivity. It clearly distinguishes from sibling tools like dataset_columns, dataset_stats, etc., 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 clearly implies when to use this tool: when you need to find rows matching a text query across any cell. It does not explicitly mention alternatives, but the purpose is so distinct from siblings that the agent can infer correct usage. No exclusions are stated, but the context is 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 carries the full burden of behavioral disclosure. It reveals that grouping commas and currency are handled, and non-numeric rows are excluded and counted, which is useful preprocessing context. However, it does not mention error cases or output format, which would be helpful but not critical for a simple statistics 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 sentence that front-loads the statistics list and then adds concise preprocessing details. There is no unnecessary wording, making it appropriately sized and easy to parse.

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 with one parameter and no output schema. The description covers the purpose, numeric column requirement, and data handling. It does not specify the return format, but for a statistics tool, the output is largely self-evident. Given the lack of annotations and output schema, it 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?

The schema provides no description for the 'column' parameter (0% coverage), so the description must clarify it. The description indicates the column must be numeric and explains how non-numeric rows are handled, adding meaning beyond the bare schema. It does not explicitly restate that 'column' is the parameter name, but it is clearly implied.

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 states the tool computes count, min, max, mean, median, and sum for a numeric column of the Reconzy dataset. This is a specific verb+resource that clearly distinguishes it from siblings like dataset_columns or dataset_top, which handle different dataset aspects.

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 summary statistics on a numeric column are needed, but it does not explicitly mention when not to use it or name alternative tools. There is no exclusion or routing to siblings, so the guidance is only implied.

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 must disclose behavioral details by itself. It adds the numeric-column constraint and the highest/lowest behavior, but it omits how limit is applied, how ties or nulls are treated, whether validation fails on non-numeric columns, and what the returned rows look like. This is minimal disclosure for an unannotated 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 one concise sentence with the key distinction front-loaded: highest/lowest rows by a numeric column. It contains no filler or needless repetition, though it could have used the available space to include more operational 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 lack of annotations and output schema, the description does not fully equip an agent to call the tool correctly. It omits return format, limit behavior, and edge-case handling, and it provides no usage guidance relative to sibling tools. For a simple tool this is still below minimum viable completeness.

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 for the undocumented parameters. It clarifies that column should be numeric and loosely maps ascending to lowest/highest, but it does not explain the limit parameter at all, nor does it state the default row count or how limit interacts with ranking. This is insufficient compensation for the 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 clearly states that the tool returns the highest or lowest rows of the dataset based on a numeric column, and the phrase 'which is the most/least X' makes the intent concrete. It doesn't explicitly contrast with siblings like dataset_search or dataset_stats, but the extreme-ranking purpose is identifiable from the name, title, and description.

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 guidance on when to use this tool versus alternatives such as dataset_search, dataset_stats, or dataset_compare. The description implies a top/bottom-N ranking use case, but it provides no exclusions, conditions, or alternative routing, leaving the agent to infer applicability.

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