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

The description enumerates the returned data elements (columns, numeric indicators, row count, provenance banner) and implies a read-only schema inspection, providing adequate transparency without annotations.

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 with an actionable instruction, containing no redundant or extraneous content.

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?

Although there is no output schema, the description names the key output categories and explains why to call the tool first, giving sufficient context for a simple schema-introspection endpoint.

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 no parameters, so parameter documentation is not needed; the description fully covers the input space by omission.

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 columns, numeric flags, row count, and provenance banner for the Patientvo dataset, and explicitly instructs to call it first to learn the schema.

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?

It gives explicit guidance to call this tool first to learn the schema, which is sufficient for a zero-parameter introductory schema tool.

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 must carry the full behavioral burden. It explains that rows are selected when the column matches any of the given values and that ordering follows the given values, but it does not mention exact-match semantics, case sensitivity, handling of missing values, or the output shape.

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 packs the core purpose, filtering logic, ordering behavior, and intended use case. There is no redundancy or 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?

For a simple two-parameter filtering tool, the description provides enough context to understand what the tool does and when to use it. It lacks explicit notes on output format or edge cases, but these are not critical given the simplicity and the absence of an output schema.

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 only types and constraints, with no field descriptions, but the description explains both parameters: 'column' is the field to match and 'values' is the list of allowed values. The 'any of' wording clarifies that this is an OR-style filter, and 'in the order given' clarifies ordering 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 indicates that the tool retrieves rows from the Patientvo dataset based on matching a column against a list of values, and the title adds the side-by-side comparison intent. It is distinguishable from sibling tools like dataset_row or dataset_top because it selects multiple rows by specific column values.

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' gives some guidance on when to use the tool, and the 'any of the given values' wording implies comparing multiple specific values. However, it does not explicitly contrast with sibling tools such as dataset_search or dataset_row, so the guidance is present but not fully explicit.

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 implies a read-only, metadata-retrieval action and enumerates the content returned. It does not disclose behavior like response format, whether data is cached/stale, or any errors, but for a provenance-only tool the gap is modest.

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, information-dense sentence plus a short directive, with no filler. The core content is front-loaded and 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 parameterless metadata-retrieval tool with no output schema, the description enumerates all four key return elements (source, computed date, licence, citation) and the intended usage. Nothing essential is missing for an agent to invoke and interpret the result.

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 schema already conveys all relevant input semantics. The description usefully reinforces what the returned provenance fields are, exceeding the minimum baseline 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 identifies the tool's purpose: returning the dataset's source, computed date, licence, and citation. The title reinforces this, and the stated use case ('attribute a figure correctly') distinguishes it from the sibling row/column/stat/search 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?

It explicitly says when to use it: when the user needs attribution/citation information. It does not spell out exclusions or compare itself to alternatives, but given the sibling list, the use case is clear enough.

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 behavior on its own. It states the matching condition (case-insensitive equality) but does not mention whether multiple rows can be returned, if there are result limits, ordering, or error handling when no matches occur. It is not misleading, but it is far from a transparent account of the tool's runtime 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?

The description is a single, compact sentence that conveys the core functionality without extraneous words. It is well-structured and front-loads the dataset name and operation, making it easy to scan. No redundancy or 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?

Given the simple schema (two strings) and the presence of sibling tools, the description adequately conveys the basic lookup operation. It differentiates from search by emphasizing 'exactly', but it does not mention return cardinality, limits, or when to prefer this over a broader search. It is sufficient for a trivial tool but lacks context to fully disambiguate all use cases.

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 the 'value' parameter is matched case-insensitively and 'exactly', which adds semantics beyond the schema. However, it does not explain what 'column' refers to (e.g., column name format, whether it must exist) or that both parameters are required to perform a lookup. It provides partial enrichment 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 clearly states that the tool returns rows where a column equals a provided value, and explicitly notes case-insensitivity. The title reinforces the 'exact key' intent, distinguishing it from search-like tools. However, the description is phrased passively ('The rows... where a column equals') rather than as a direct command, and does not explicitly name the dataset as 'Patientvo' in a way that clarifies the resource being queried.

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 its alternatives (e.g., dataset_search for partial matches, dataset_top for ranked rows). There is no mention of prerequisites, limitations, or scenarios where this tool is preferred. The description gives no actionable direction for selecting this over siblings.

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 usefully discloses case-insensitive matching and the 50-row limit, but it does not explain how rows are ordered or selected when more than 50 match, nor what the returned row objects contain.

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 states the core behavior and key constraints with no wasted words. It is concise without sacrificing the most important operational details.

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 search tool, the description is mostly adequate: it explains the query semantics and the result cap. However, with no output schema and no usage guidance, it leaves uncertainty about result ordering, which 50 rows are selected, and how to choose this tool over siblings.

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%: the query parameter has a description, while limit does not. The description adds meaningful semantics by stating case-insensitive cell matching and the 50-row cap, though the limit information largely duplicates the schema's maximum value.

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 operation: find rows of the Patientvo dataset whose cells match a query, with case-insensitive matching and a 50-row cap. It implies row-level search rather than schema/statistics operations, which helps distinguish it from siblings like dataset_columns and dataset_stats, though it does not explicitly name an alternative.

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 siblings such as dataset_row, dataset_top, or dataset_columns. The description implies general text-search usage, but it does not state exclusions, alternatives, or conditions that would select this tool over the others.

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?

Without annotations, the description provides meaningful behavioral details: it mentions that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. This gives an agent insight into data preprocessing and potential edge cases, though it omits broader error conditions 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 concise and well-structured: it lists the output statistics, specifies the dataset, and notes important data handling details in parentheses. Every sentence adds value, with no redundant or vague wording.

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 a single parameter and a straightforward output, the description covers the core functionality and edge-case handling (currency, non-numeric rows). It does not specify the exact output format or return type, but this is not critical given the absence of an output schema and the simplicity of the task.

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 minimally described in the schema (string, minLength 1). The description clarifies that it refers to a numeric column in the dataset, but does not elaborate on naming conventions, case sensitivity, or what happens if the column does not exist. The added context is modest beyond the 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 action (computing summary statistics) and the specific resource (the Patientvo dataset), listing the exact statistics (count, min, max, mean, median, sum) and the target (a numeric column). This unambiguously distinguishes it from sibling tools like 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 implies when to use it—when summary statistics are needed for a numeric column—and notes that non-numeric rows are handled. It does not explicitly compare against sibling tools, but the distinct purpose makes the appropriate context clear enough 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. It discloses the core behavior (ranking by numeric column, returning top/bottom rows) but omits details like return format, default limit, or error handling. It does not contradict any annotations, and the behavior stated is accurate, though sparse.

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 states the essential operation and use case efficiently. The structure is optimal for quick agent scanning.

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, but with no output schema, the description should clarify the return type (e.g., a list of rows) and any limits or defaults. It does not mention pagination, output structure, or behavior when the column is non-numeric. The missing details could lead to incorrect assumptions about the 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 low (33%—only 'ascending' has a description). The description adds minimal parameter insight: it hints that 'column' must be numeric but does not explain 'limit' or 'ascending' defaults or behavior. Given the low coverage, the description fails to compensate, leaving agents without sufficient parameter guidance.

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 clearly state that the tool ranks rows by a numeric column, returning the highest or lowest values. It is distinct from siblings like dataset_stats (which likely aggregates) or dataset_row (which fetches a single row), though it does not explicitly name alternatives. The verb 'rank' is specific and the resource (Patientvo dataset rows) is identified.

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 'which is the most/least X' but does not provide explicit guidance on when to use this tool versus alternatives like dataset_stats or dataset_search. There are no exclusions or comparative context. The intended use case is inferable but not clearly articulated.

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