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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 and it does disclose the returned content in reasonable detail. However, it does not explicitly state that the call is read-only, has no side effects, or describe any error/empty-dataset behavior. This is acceptable for a schema metadata tool but not richly 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 compact sentences, with the concrete output content front-loaded and the usage directive placed second. Every clause contributes information and there is no repetition of the title or name.

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, the description covers the main outputs and the recommended usage order. It does not describe the exact result shape or whether the provenance banner is plain text, but the simplicity of the tool keeps the gap small.

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 a 100% schema coverage baseline of 4, so the description is not responsible for explaining parameter meaning. The description still adds value by clarifying what the output contains, which is more than the empty schema provides.

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 (ProbeLedger dataset) and the exact information returned: columns, numeric flags, row count, and provenance banner. It also frames its role as the schema-learning entry point, which clearly distinguishes it from sibling exploration 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 instruction 'Call this first to learn the schema' gives explicit placement in the workflow, implying it should precede tools like dataset_search or dataset_stats. It does not name alternatives or state when not to use it, but the 'first' directive provides enough guidance for 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 behavioral disclosure. It does reveal two important behaviors: rows are filtered by equality on the column and are returned in the order given by the values array. However, it does not mention output format, exact-match semantics, case sensitivity, or pagination, which would make behavior more 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 compact sentence with no filler. It front-loads the core filtering behavior and ends with the use-case context, so 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 filtering tool, the description covers the essential details: what is filtered, how values map to rows, and the ordering behavior. It would be more complete if it explicitly described the output shape or exact-match constraints, but the title and description together give enough context 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 description coverage is 0%, so the description must compensate, and it does. It explains that 'column' is the field to match and 'values' are the values to match, and it adds the crucial semantic that output row order follows the order of the values array. This goes beyond the bare schema names and gives the agent actionable meaning.

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: rows of the ProbeLedger dataset filtered by a column matching given values, with order preserved. The 'X vs Y' phrasing signals the comparison use case and helps distinguish it from siblings like dataset_search and dataset_row, though it does not explicitly name those alternatives.

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 context ('for X vs Y questions') that implies when this tool is appropriate, but it does not explicitly state when to use it instead of sibling tools like dataset_search or dataset_row. There are no exclusion criteria 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 behavioral burden. It transparently lists what the tool returns (source, computed date, licence, citation) and signals a read-only intent via 'Read this.' There are no hidden side effects or surprising behaviors described, which is acceptable for this simple no-parameter 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 tightly packed sentence with the most important content front-loaded (source, date, licence, citation) followed by a direct usage instruction. Every word earns its place; there is no fluff 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 zero-parameter metadata retrieval tool with no output schema, the description fully covers what the agent needs to know: what data is returned and why to call it. The sibling context makes the uniqueness of provenance self-evident, so nothing important is missing.

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 the baseline is 4. The description adds no parameter details, but none are needed since the input schema is empty and no input configuration exists.

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 the specific resource (ProbeLedger dataset) and the exact metadata returned: source, computed date, licence, and citation. It clearly frames the tool as the provenance/citation lookup, which makes its purpose obvious against siblings like dataset_stats or dataset_search, though it does not explicitly call out sibling 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 description gives a clear usage directive: 'Read this to attribute a figure correctly.' This tells the agent when to use the tool (when citation or attribution is needed). It does not mention alternatives or exclusions, but for a simple metadata tool 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 full burden of behavioral disclosure. It discloses case-insensitivity and exact matching but does not mention read-only nature, pagination, behavior when no matches are found, or whether multiple rows are returned. For a lookup tool, this is a significant gap.

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, and the core action is front-loaded. It is concise and efficient, though the phrasing could be slightly clearer. It earns a high score for being appropriately sized.

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, output schema, and parameter descriptions, the description is incomplete. It does not explain return format, error handling, or whether the match is on the entire string or substring. It also does not state if case-insensitivity applies to both column and value. For a tool with two parameters and no schema descriptions, more context is needed.

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?

With zero schema description coverage, the description must explain the parameters. It mentions 'column' and 'value' implicitly but does not explicitly define that column is a column name and value is the exact string to match. It adds some meaning but leaves ambiguity about whether column is a name or index, and whether value should be a literal string. It 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 clearly states that the tool retrieves rows from the ProbeLedger dataset where a column equals a value exactly, with case-insensitivity. This is a specific verb and resource, distinguishing it from dataset_search which likely performs broader searches. However, the phrasing 'The rows of the ProbeLedger dataset where...' is slightly ambiguous about whether one or many rows are returned, and it could be more direct.

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 the tool is for exact, case-insensitive matches, but it does not explicitly state when to use this tool versus siblings like dataset_search. There is no mention of when not to use it, nor any comparison to alternatives. The usage context is only implied, not explicitly guided.

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 full responsibility for behavior. It discloses case-insensitive matching, matching across any cell, and a hard cap of 50 results. It stops short of stating default limit behavior, sorting, or empty-result behavior, but the core operational traits are present.

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 wasted words. The result type (Rows of the dataset) is front-loaded, followed by the matching condition, case sensitivity, and limit in a natural, scannable order.

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 purpose and constraints are clear enough for a simple two-parameter search tool, but the optional limit has no stated default and there is no mention of what happens when no rows match or what the returned row structure contains. These gaps matter for an agent deciding how to invoke 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?

Schema coverage is only 50%, covering query but not limit. The description compensates by clarifying that query is searched case-insensitively across cells and that limit represents an upper bound of 50 returned rows, adding meaning beyond the schema's bare numeric constraints.

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 and resource: returns rows of the ProbeLedger dataset whose cells contain the query. The case-insensitivity and 50-row cap further refine the scope, distinguishing it from sibling tools like dataset_stats or dataset_columns which serve different lookup 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?

Gives no explicit guidance about when to use this tool versus alternatives. The described behavior implies it is for content-based row search, but no when-to-use conditions or exclusions are stated, 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?

No annotations are present, so the description carries the burden. It discloses handling of grouping commas, currency, and non-numeric rows (excluded and counted), which is useful. However, it doesn't describe the return format, error handling, or performance characteristics.

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 front-loads the statistics list and then adds handling details. No wasted words, though the sentence is slightly dense.

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 one-parameter tool with no output schema, the description covers the core functionality and edge cases (formatting, non-numeric rows). It lacks an explicit return format but is otherwise sufficient.

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 compensate. It implies the 'column' parameter is a numeric column name, but doesn't clarify syntax or constraints beyond that. Some value is added, but not full compensation.

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 computes count, min, max, mean, median, and sum for a numeric column of the ProbeLedger dataset. This is specific and unambiguous, distinguishing it from sibling tools like dataset_row or dataset_top.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer it over dataset_search or dataset_top, or any exclusions.

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 of behavioral disclosure. It mentions sorting direction (highest/lowest) but does not disclose behavior on ties, missing values, limit enforcement, determinism, or any side effects (presumably read-only but unstated). The description is too thin for a mutation-free tool to fully inform the agent.

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 wasted words. It conveys the core purpose and a practical interpretation in an efficient manner, 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?

This is a simple tool, but without an output schema the description should indicate what the tool returns (e.g., a list of rows). It does not mention the return format, error conditions, or how the 'limit' interacts with sorting. Several important operational details are missing for an agent to call it confidently.

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% (only 'ascending' has a description). The description adds the detail that the column must be numeric, which is helpful, but it does not explain the 'limit' parameter or its maximum value, nor clarify the default behavior beyond what the schema already states. It fails to compensate for the low schema coverage.

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 ('rank rows') with a resource ('ProbeLedger dataset') and a clear scope (by a numeric column). It also includes a natural language interpretation ('which is the most/least X'), making the tool's intent immediately clear and distinct from sibling tools that handle columns, rows, search, or 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 description implies a use case (finding extreme rows by a numeric metric) but does not explicitly state when to prefer this tool over alternatives like dataset_search or dataset_stats, nor does it mention any exclusions. It provides enough context for a basic understanding but lacks direct routing guidance.

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