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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 full responsibility for behavioral context. It describes what information is returned and that the call should come first, but it does not explicitly state that the operation is read-only, stateless, or free of side effects; the introspection-like contents make this mostly implicit.

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 short sentences with no filler. The returned content is front-loaded and the usage directive appears cleanly at the end, so every word contributes value.

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?

With no annotations and no output schema, the description compensates by listing the main return contents and giving an explicit first-step instruction. It is sufficient for a simple zero-parameter introspection tool, though it could add output shape details or explicitly note that no arguments are 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?

The tool has zero parameters, so the input schema is already complete and there are no parameters needing clarification. The baseline for a zero-parameter tool is 4, and the description adds useful context by naming the specific dataset and its purpose.

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 as returning the dataset's columns, numeric column flags, row count, and provenance banner, and it names the specific 'Subsroom dataset' resource. It does not explicitly differentiate itself from siblings like dataset_provenance or dataset_stats, so it falls just 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?

'Call this first to learn the schema' provides a clear directive for when to use the tool as the initial discovery step. It does not mention when to prefer siblings such as dataset_search or dataset_stats, but the stated first-step context 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 full burden. It discloses two key behaviors: rows are filtered by 'any of the given values' (OR condition) and returned in the order given. However, it omits what the response looks like (e.g., whether all columns are included, how 'side by side' comparison is formatted), edge cases like zero matches, and any pagination or limits. This is partial transparency.

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, dense sentence with no filler. It leads with the resource, then the condition, then the intended use case. Every word contributes to the meaning, and it is front-loaded with the core behavior.

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 there is no output schema and no annotations, the description should explain what the result looks like. It says rows are returned in order but does not clarify how 'side by side' comparison is represented (e.g., a table, a list of rows, combined columns). It also doesn't mention any limits or failure behavior. For a tool with only two parameters and no other structured metadata, this is a notable omission.

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 parameters. It does add meaning: 'whose column is any of the given values' indicates column is the field to filter on and values are the list of values to match. This gives a high-level semantic that the raw schema lacks. Yet it doesn't explain format constraints (e.g., that values are strings) or the maxItems limit, though those are in the schema. It compensates somewhat but not fully.

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 from the Subsroom dataset filtered by a column matching any of the given values, preserving order. It specifies the resource and the filtering mechanism, and it is distinct from siblings like dataset_row (single row) and dataset_search (search) because it targets exact value matching for comparison questions. However, it doesn't explicitly contrast with siblings, which keeps it from 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 to use it, giving a contextual trigger. Yet it does not explicitly say when not to use it or name alternatives among the sibling tools. It leaves the agent to infer that other tools are better for fuzzy search or statistics, which is a moderate gap.

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. It does state the content returned (source, date, licence, citation), but does not explicitly confirm read-only semantics, absence of side effects, or any operational requirements. For a simple metadata retrieval, this is adequate but not exhaustive, so a 3 is appropriate.

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, tightly worded sentence that front-loads the core content and then adds the usage directive. There is zero fluff; every word contributes to understanding. This is exemplary conciseness.

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 tool's simplicity (no parameters, no output schema), the description is fully complete. It explains what information is provided, for which dataset, and why one would use it. Nothing an agent needs to call it correctly 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 description does not need to explain parameter meaning. The baseline for 0 parameters is 4, and the description correctly focuses on the output and purpose rather than parameters.

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's function: it provides the source, computation date, licence, and citation for the Subsroom dataset. It also gives a specific use case ('to attribute a figure correctly'), which is distinct from the sibling tools that deal with columns, comparison, rows, search, stats, and top values. This is a precise verb+resource statement.

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 when to use the tool ('Read this to attribute a figure correctly'), but does not explicitly mention alternatives or when not to use it. Since the siblings are about data manipulation, the use case is implicit, but explicit exclusions are absent. This fits 'clear context, no 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?

With no annotations, the description carries the full burden. It discloses the case-insensitive exact-match behavior, but does not explicitly state that the operation is read-only, whether all matching rows are returned, what happens if no rows match, or whether the column name must be an exact dataset column. The title's 'exact key' also slightly conflicts with the description's case-insensitive caveat.

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 is front-loaded with the core action ('rows where a column equals a value'). The parenthetical case-insensitivity note is slightly awkward but compact. The title is also concise.

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 annotations, output schema, and any comparison to siblings, the description is too thin. It does not communicate the result shape, row limits, error behavior, or how this tool differs from dataset_search. An agent cannot fully anticipate the tool's behavior from this description alone.

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 interpret the parameters. It explains that 'column' and 'value' are the operands of an equality comparison and adds the case-insensitivity nuance. However, it leaves ambiguity about whether 'column' refers to a column name, column index, or arbitrary key, and does not specify value formatting beyond the schema's string type.

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 as filtering rows by equality on a column: 'The rows of the Subsroom dataset where a column equals a value exactly'. It distinguishes from dataset_search which implies a broader search, though the singular/plural mismatch between title ('a row') and description ('rows') causes minor ambiguity.

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 sibling tools like dataset_search or dataset_top. No conditions, exclusions, or alternatives are mentioned, leaving the agent to infer based on tool names alone.

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 disclosure burden. It reveals case-insensitive substring matching, cell-level coverage, and a maximum result count of 50. It does not disclose ordering, pagination, default limit, or output shape, but the core behavior is unambiguous.

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 15-word sentence that front-loads the operation ('Rows of the Subsroom dataset whose cells contain the query'). Every word earns its place, with no 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 two-parameter search, the description covers the core operation and limit. However, with no output schema and no annotations, it leaves gaps around default limit behavior, result ordering, and the exact structure of returned rows. These are not critical but would improve 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?

The schema describes only query, leaving limit to min/max constraints. The description adds case-insensitive matching semantics to the query and interprets the limit as a 50-row cap, which helps the agent understand the parameters' roles. It stops short of stating the default limit when limit is omitted.

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 exactly what the tool does: return rows of the Subsroom dataset where any cell contains the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes it from siblings like dataset_stats (aggregation), dataset_top (ranked rows), and dataset_row (single row fetch).

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 explicit guidance is given about when to use this vs alternatives, nor when not to use it. The purpose implies it is for arbitrary text search across rows, but the description does not mention sibling tools or prerequisites.

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 usefully states that grouping commas and currency are handled and that non-numeric rows are excluded and counted. There is minor ambiguity about whether 'count' includes excluded rows, but the core behavior is 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 two compact sentences with the computed statistics listed first and no filler. Every clause adds meaningful 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?

For a one-parameter, read-only statistics tool with no output schema, the description covers the dataset scope, the exact statistics computed, and handling of formatted and non-numeric values. It does not specify the output shape or an all-non-numeric edge case, but these are not critical for selecting and invoking 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?

With schema description coverage at 0%, the description must compensate, and it does: it explains that the single `column` parameter refers to a numeric column in the Subsroom dataset and describes how non-numeric or formatted values in that column are treated. This goes beyond the bare string type in 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 names a concrete operation—computing count, min, max, mean, median, and sum—on a numeric column of a specific dataset. This clearly 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?

It is clear that this tool is appropriate when summary statistics for a numeric column are needed, and the description notes how formatted numeric values are handled. However, it does not explicitly state when-not-to-use it or name alternative 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?

No annotations are provided, so the description carries the full disclosure burden. It accurately states the core ranking behavior (highest/lowest rows) and the numeric-column requirement, but says nothing about the default limit when omitted, error behavior for non-numeric or missing columns, or tie handling. An agent is left to guess at these edge behaviors.

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, front-loaded with behavior ("highest (or lowest) rows... by a numeric column") followed by a compact intent gloss. Every phrase earns its place; only the minor overlap with the title keeps it from a 5.

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 no annotations and no output schema, the description is thin. An agent can handle the happy path (valid numeric column, explicit limit) but cannot know the default limit behavior, the exact return shape, or what happens with a non-numeric or missing column — gaps that matter for correct invocation and result interpretation.

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. The tool description adds a real constraint by calling the column "numeric," which the schema does not enforce (column is just a string), and it clarifies the ranking semantics behind ascending. It adds nothing about limit, though limit's bounds are self-evident in 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 states a specific operation — returning "the highest (or lowest) rows of the Subsroom dataset by a numeric column" — and glosses the intent as "'which is the most/least X'." This is clearly distinguishable in flavor from siblings like dataset_search or dataset_stats, though it never names them explicitly.

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 quoted user-question framing ("which is the most/least X") provides an implicit trigger for when to use this tool. However, it gives no explicit when-not-to-use guidance and does not differentiate from dataset_stats or dataset_search, which could plausibly answer similar 'most/least' questions.

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