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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 disclosure burden. It enumerates exactly what the tool returns — columns, numeric indicators, row count, and provenance banner — and frames the operation as schema discovery, which implies safe read-only behavior. It could be more explicit about output formatting, but the behavioral profile is largely 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 short sentences with no filler. The output contents are listed efficiently, and the most important guidance — 'Call this first' — is clear and memorable.

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 tool with no annotations and no output schema, the description gives enough context: what the tool returns, for which dataset, and when to call it. The only minor gap is that 'provenance banner' is not explained, but the overall context 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 input schema is empty and schema coverage is 100%, so there are no parameter semantics to document. The description adds no duplicated parameter detail and simply reinforces the intended first-step usage, matching the baseline for a zero-parameter 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 a specific purpose: retrieve the columns, numeric flags, row count, and provenance banner for the Medcontra dataset. The phrase 'Call this first to learn the schema' gives the tool a distinct role among the siblings, so an agent can identify when to use it.

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 call this tool first to learn the schema, which is strong contextual guidance. It does not name alternatives or exclusion cases, but for a zero-parameter schema-introspection tool this 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 carries the burden of behavioral disclosure. It reveals useful behaviors: matching 'any of the given values' (OR semantics) and preserving the supplied order. However, it does not explain what 'side by side' comparison means in the output, nor any result formatting or edge-case 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 front-loads the core behavior and use case. Every phrase contributes meaning, and there is no redundant or filler 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?

For a simple two-parameter tool, the description covers the main invocation details. However, with no output schema and no annotations, it leaves unspecified what the returned rows look like or how the side-by-side comparison is presented, which an agent might need to know for post-processing.

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 explains that 'column' is the field to match on and 'values' are the matching targets, with order semantics tied to the values array. This adds meaning beyond the bare schema, though it could be more explicit about each parameter individually.

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 (Medcontra dataset rows) and the operation (select rows whose column matches any given values, preserving the given order). It implicitly distinguishes itself from sibling tools like dataset_row by focusing on multiple-value lookups for comparison, though it doesn't explicitly name 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 provides a clear use case: 'for X vs Y questions,' which tells an agent when this tool is appropriate. It does not explicitly list exclusions or alternatives, but the context is sufficient to infer its purpose relative to 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 behavioral transparency burden. It clearly indicates this is a read-only metadata lookup and reveals what information will be returned. It does not describe the exact return format, but for a static provenance tool that is a minor 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?

A single sentence that front-loads the provenance content and then gives a practical use case. There is no repetition of the title and no wasted words.

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, the description fully covers what an agent needs: what information is available and why to call the tool. The absence of an output schema is mitigated by the explicit list of fields.

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 an empty input schema, so there is no parameter-level meaning for the description to add. The baseline of 4 for parameterless tools applies.

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 Medcontra dataset) and enumerates the exact provenance fields: source, computed date, licence, and citation. This makes it clearly distinct from sibling tools that operate on columns, rows, stats, search, or comparisons.

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 states when to use the tool: to attribute a figure correctly. It does not name alternatives or exclusion conditions, but the purpose is so distinct from the sibling data tools that an agent can reliably infer when it applies.

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 case-insensitivity but does not state whether zero, one, or many rows are returned, whether results are paginated or truncated, whether the operation is read-only, or what happens when no match is found.

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, economical sentence with no filler. The core exact-match semantics are front-loaded, and the case-insensitive caveat is attached without cluttering the main point.

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 tool with no annotations and no output schema, the description leaves meaningful gaps: it is unclear if multiple rows can be returned, what the result shape is, and how this differs from dataset_search. The core semantics are present, but an agent would need to infer too much before calling 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?

The input schema provides no parameter descriptions (0% coverage), and the description only vaguely maps 'column' and 'value' through the phrase 'where a column equals a value.' It does not explain valid column names, value formatting, or edge cases, so it only minimally 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 identifies the resource (rows of the Medcontra dataset) and the operation (retrieve rows where a column equals a value exactly, case-insensitively). It aligns with the title's 'exact key' idea and implies a distinction from dataset_search, though it does not explicitly name the distinction.

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 exact-match and case-insensitive phrasing implies this tool is for precise equality lookups rather than fuzzy or broad search. However, there is no explicit guidance on when to use this tool versus dataset_search, dataset_top, or dataset_columns, and no when-not-to-use conditions.

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 and does disclose useful traits: case-insensitive matching, substring/cell containment, and a maximum of 50 rows. It does not mention ordering, empty-result behavior, or a default limit, which are material gaps for a search 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?

A single front-loaded sentence states the target dataset, the matching rule, case-sensitivity behavior, and the row cap with no filler. Every element 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 low-complexity search tool with no output schema, the description provides the essential invocation contract: what is searched, how matching works, and the cap. It is slightly incomplete regarding result ordering and defaults, but those are minor for selecting and calling 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?

The schema covers only the query parameter; the description compensates by adding case-insensitivity to the query and clarifying the limit as 'up to 50'. This adds real meaning beyond the bare integer-field 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 uses a specific verb and resource: returning 'Rows of the Medcontra dataset' that contain the query, including case-insensitivity and a 50-row cap. This is clear, but it doesn't explicitly differentiate from siblings like dataset_row or dataset_top, so it falls 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 description implies the tool is for finding rows by substring match across cells, which is a clear use context. However, it never states when not to use it or names alternatives, leaving the agent to infer the boundary against the 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, the description carries the full burden, and it genuinely discloses two behavioral traits: grouping commas and currency are normalized, and non-numeric rows are excluded and counted. This goes beyond a bare operation statement into real data-handling context. It stops short of covering error cases (missing column, all-non-numeric column) or return format, which would round out the behavior profile.

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 tight sentence that front-loads the statistic list and defers the handling caveats; there is zero filler. Every phrase earns its place, and the length is proportionate to the tool's simplicity.

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 one-parameter stats tool, the description covers the core operation and the main data-cleaning quirks, which is most of what an agent needs. It omits the return structure and edge-case behavior, and since there is no output schema, that information is neither delegated nor provided. The gaps are modest but noticeable for a tool an agent must call blindly.

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 partially does: 'numeric column of the Medcontra dataset' tells the agent that `column` takes a column name whose values should be numeric. It does not explain how to discover valid column names or verify the column exists, and the acceptance of string-formatted numbers is only implicit through the comma/currency note.

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 exact outputs (count, min, max, mean, median, sum) and the resource (a numeric column of the Medcontra dataset), making the tool's function unambiguous. It does not explicitly differentiate it from siblings like dataset_top or dataset_search, though the enumerated statistic list largely does so implicitly. A stated verb like 'computes' is implied rather than present.

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 explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. The numeric-column qualifier and the note that commas/currency are handled imply a use case for messy numeric data, but an agent must infer when to choose this over dataset_row, dataset_search, or dataset_top. A pointer such as 'use dataset_columns to discover valid column names' would materially improve 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 full burden of behavioral disclosure. It does disclose the numeric-column constraint and the highest/lowest orientation, but it is silent on material behaviors: default limit when none is passed, handling of non-numeric or missing values, tie-breaking, and output shape. For a ranking tool these gaps are significant enough that an agent could call it and get surprising results.

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 zero filler. The core capability comes first, and the 'which is the most/least X' parenthetical earns its place by giving the agent a query-intent anchor. Nothing here is redundant or wasteful.

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?

Despite low complexity, the tool has no annotations and no output schema, so the description must carry more weight than it does. It fails to state the default limit, whether full rows are returned, or how non-numeric columns are handled, and it offers no routing guidance against overlapping siblings. A top-N ranking tool needs at least default behavior and output shape to be safely invocable.

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), so the description must compensate. It adds real value by specifying that 'column' must be numeric and by mapping highest/lowest to the ascending flag. However, 'limit' remains undocumented in both the schema and the description, and the highest/lowest semantics duplicate what the schema's ascending description already conveys.

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 the Medcontra dataset) and the operation (ranking by a numeric column, highest or lowest), with a natural-language mapping in 'which is the most/least X' that makes intent unmistakable. It distinguishes itself from siblings like dataset_search and dataset_stats by the ranking/ordering focus, though it names no sibling explicitly and the verb is implied rather than stated ('The highest... rows' vs. 'Returns the highest... rows').

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 only usage signal is the heuristic 'which is the most/least X', which implies when an agent might reach for this tool but offers no explicit when-to-use or when-not-to-use guidance. It never names alternatives, so nothing helps an agent decide between dataset_top and overlapping siblings like dataset_stats (aggregates) or dataset_search (matching rows).

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