site

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

No annotations are provided, so the description bears full responsibility for disclosing side effects or safety. It only lists returned data without mentioning whether the operation is read-only, or if it has any side effects or dependencies, leaving room for uncertainty.

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 that efficiently enumerates the returned elements and includes a clear call-to-action. No extraneous information is present, and the structure is straightforward.

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 absence of an output schema and parameters, the description provides a reasonable summary of what the tool returns and advises when to use it. It could be slightly more detailed about the format of the output, but it covers the essential context for initial usage.

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 100% (empty). Since there are no parameters to describe, the baseline score of 4 is appropriate, and the description does not need to elaborate on parameter details.

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 returns: columns, numeric flags, row count, and provenance banner. It also explicitly positions itself as the first call to learn the schema, distinguishing its purpose from the sibling 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 phrase 'Call this first to learn the schema' provides a direct usage guideline, indicating that this tool should be invoked before others. However, it does not explicitly contrast with sibling tools, though the recommendation to call first implies a priority.

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 disclose behavioral traits, but it only mentions ordering ('in the order given'). It does not state whether the operation is read-only, what happens if no rows match, or whether the results include all columns. The lack of explicit non-destructive assurance is a significant gap for a query 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, efficient sentence that front-loads the core behavior and includes a purpose hint at the end. It is concise with no redundant phrasing, though the phrasing 'The rows of the Netsheetly dataset whose column is any of the given values' is slightly awkward but still compact.

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 tool with two parameters and no output schema, the description provides the essential filtering logic and ordering. However, it does not mention return format, pagination, or error handling, and the title's 'side by side' is not reflected in the description, which could lead to ambiguity about how rows are presented. It is adequate but not 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 description coverage is 0%, so the description must clarify parameter meaning. It explains that 'column' is the field to compare and 'values' are the values to match, which is essential semantics. However, it does not detail constraints like the minItems=2 or maxItems=10, nor the string type, leaving some interpretation to the agent.

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 tool returns rows from the Netsheetly dataset filtered by a column matching any of the given values, preserving the given order. This clearly indicates the resource and action, and the 'X vs Y questions' hint helps distinguish it from sibling tools like dataset_search or dataset_row, though it does not explicitly contrast them.

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 provides a usage hint ('for X vs Y questions') but does not explicitly state when to avoid this tool or how it differs from alternatives like dataset_row or dataset_search. The context is implied rather than explicit, leaving the agent to infer the selection criteria.

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 to indicate read-only or destructive behavior, so the description carries the full burden. The description implies a read-only metadata lookup by naming the provenance fields, but it does not explicitly state that no data is modified, nor does it mention any side effects or error conditions.

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 redundant wording. It front-loads the key content (source, date, license, citation) and ends with a clear purpose statement, making it efficient 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 description names all the key provenance fields an agent would need for attribution and gives a clear context of use. It does not specify the exact return format or citation style, but given the absence of an output schema and the simple nature of the tool, this is adequate.

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 100%, so there is nothing for the description to add about parameter meanings. The description focuses on the returned metadata, which is appropriate for a parameterless 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 states the tool provides provenance metadata for the Netsheetly dataset, specifically the source, computed date, license, and citation. This is distinct from sibling tools focused on columns, rows, search, stats, and comparison, making its purpose immediately identifiable.

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 explicitly tells the agent to read this tool when attributing a figure, giving a clear use case. It does not explicitly contrast with sibling tools, but the phrase 'Read this to attribute a figure correctly' provides sufficient guidance for when this tool is 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?

No annotations are provided, so the description carries the full burden. The verb 'look up' suggests a read-only operation, but it does not explicitly state that no modifications occur or mention any permissions or side effects.

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 that effectively conveys the core functionality without 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 tool has low complexity with two parameters and no output schema. The description sufficiently explains the purpose and parameters, with only a minor gap regarding the column identifier 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?

The description clarifies that 'column' is a column identifier and 'value' is the value to match, and adds the important matching behavior (exact, case-insensitive). It does not specify the format of the column identifier (e.g., name vs. index), but the overall 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?

States a specific verb 'look up' and resource 'row', and clearly defines the matching condition (exact, case-insensitive). This distinguishes it from siblings like dataset_search which likely performs fuzzy or partial matching.

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?

Implies the use case for exact-match queries through the description, though it does not explicitly mention alternatives or when not to use it. The clarity of the matching condition provides sufficient guidance.

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. It discloses case-insensitive matching and row-level granularity, which are meaningful behavioral traits. It does not explicitly say it is read-only or describe ordering, but for a search operation the core behavior is clearly conveyed.

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, tightly constructed sentence that leads with the resource and operation and packs in case-insensitivity and the row limit with zero filler. 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?

The core matching behavior is described, but the description omits important operational details: what fields each returned row contains, the default limit when 'limit' is omitted, and behavior when no rows match. Since there is no output schema, these gaps leave an agent uncertain about the response 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?

Schema description coverage is only 50%, and the description contributes little beyond the schema: it restates the query concept ('cells contain the query') but does not explain the 'limit' parameter or how it interacts with the 50 cap. The limit parameter remains semantically empty in both schema and 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?

Description uses a specific verb and resource ('search the dataset'), and precisely defines behavior: rows whose cells contain the query, case-insensitive, max 50. This clearly differentiates it from siblings like dataset_row (a single row) and dataset_stats.

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: full-text search across cells for a given query. However, there is no explicit statement of when to prefer this over alternatives like dataset_row or dataset_compare, nor exclusions for 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?

The description clearly discloses the read-only nature of the operation and explicitly mentions edge-case behavior (non-numeric rows are excluded and counted). No annotations exist, but the description adequately communicates the tool's behavior and output.

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 lists the output statistics and notes handling of formatting and non-numeric rows. No unnecessary details or redundant phrasing.

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 the description covers purpose, input semantics, edge cases, and output content. No additional annotations are needed for this straightforward operation.

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 single parameter 'column' is well described in the context of the operation as a numeric column of the dataset. The schema provides only a string type, but the description clarifies its semantic meaning, compensating for the sparse 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 states the specific operation: computing summary statistics (count, min, max, mean, median, sum) for a numeric column. It distinguishes itself from other dataset tools by focusing on numeric column statistics without overlapping with row/search/top operations.

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 does not mention when to use this tool versus the sibling tools. No explicit conditions or alternatives are provided, leaving the agent to infer suitability based solely on the tool name and description.

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 does communicate the read-only ranking behavior and the highest/lowest orientation, but it omits details such as default ordering, tie handling, behavior for non-numeric columns, and the output shape. The schema covers defaults and bounds, but the description itself adds limited behavioral context.

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 concept front-loaded. It avoids redundant restatement of the schema, though the em-dash quote construction is slightly informal and could be tightened without losing 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?

Given the low parameter coverage and no output schema, the description is only minimally complete. The schema fills in parameter constraints, but the description does not clarify return shape, default limit behavior, or when the tool is the right choice versus siblings. It is adequate for a simple tool but leaves meaningful gaps.

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 meaning for the column parameter by specifying it must be numeric, and 'highest (or lowest)' maps to the ascending flag. However, it does not explain the limit parameter or its maximum of 50, which is a notable gap for a top-N 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 states the operation: returning the highest or lowest rows of the dataset by a numeric column, framed as answering 'which is the most/least X'. This distinguishes it from siblings like dataset_search, dataset_row, or dataset_stats, which address filtering, single-row lookup, or aggregation rather than ranking.

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 top-N or bottom-N questions via 'most/least X', but it does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions or conditions. Usage context is present but only by implication.

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