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

With no annotations, the description carries the behavioral burden and it does well by enumerating the exact output contents: columns, numeric flags, row count, and provenance banner. It does not explicitly state 'read-only' or describe edge cases, but 'learn the schema' strongly implies a safe, non-mutating introspection call.

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 sentences, both meaningful. The first packs the tool's output into a compact list; the second provides actionable ordering guidance. There is no filler or redundant restatement of the title.

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 no parameters and no output schema, the description covers the main things an agent needs: what information is returned and when to call it. It leaves minor ambiguity around the exact meaning of 'provenance banner' and does not describe the response structure, but that is unlikely to prevent 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?

The tool has zero parameters and the schema is therefore completely covered. Baseline for a zero-parameter tool is 4, and no parameter-specific explanation is needed or missing.

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, which are numeric, the row count, and the provenance banner, and frames it as the schema-learning entry point. 'Call this first to learn the schema' gives the tool a distinct role among the dataset_* siblings, even though some elements overlap with dataset_stats and dataset_provenance.

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 when-to-use guidance and positions it as the initial discovery step. It does not explicitly name alternatives or state when not to use it, but for a zero-parameter schema introspection tool, the usage 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 available, the description carries the full behavioral disclosure burden and does reasonably well: it states the selection criterion (column value equals any of the given values) and the ordering behavior (rows follow the order of the given values). It does not explicitly declare read-only status or describe edge cases like no matches, but the core retrieval 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?

The description is a single sentence with no filler, front-loading the substantive behavior and ending with a brief purpose clause. Every clause contributes useful information, making it both concise and well-structured.

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 query tool with no output schema, the description covers the essential invocation details: dataset, column to filter on, values to match, and ordering. It leaves some details unspecified, such as the exact output row shape and behavior for missing or unknown values, but it is adequate for an agent to call the tool correctly in the intended comparison scenario.

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 maps 'column' to the field on which rows are filtered and 'values' to the candidate values whose order determines the result ordering. This adds real meaning beyond the bare JSON schema types and 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?

The description clearly identifies the operation: returning rows from a dataset filtered by equality against a list of values, in a specific order, to support side-by-side comparison. The title adds the verb 'Compare' and the 'X vs Y' phrasing reinforces the comparison purpose. It does not explicitly name sibling tools, but the semantics are specific enough to distinguish it from a generic search or 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?

The description provides a clear use context: 'for "X vs Y" questions', indicating when an agent should select this tool for comparison scenarios. It does not enumerate exclusions or alternatives, but the stated purpose is sufficient for basic routing among 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 of disclosing behavior. It clearly states this is a read-only tool returning provenance metadata (source, date, licence, citation), which is enough to infer there are no side effects. It could mention return formatting, but that is not necessary for such a simple 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 two sentences with no filler. The most useful information (what the tool returns) is front-loaded, and the intended use case is stated crisply.

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, read-only metadata utility, the description covers everything an agent needs: the content of the data, its purpose, and when to consult it. The absence of an output schema is not a gap because the description enumerates the fields returned.

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 there is nothing to document. The description adds no parameter information, but none is needed; a baseline of 4 is appropriate when parameters are absent.

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 resource (dataset provenance) and the specific facts it provides: source, computation date, licence, and citation. It also frames its purpose as helping the user attribute a figure correctly, which distinguishes it from sibling tools like dataset_columns or 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 description explicitly tells the user when to use it: 'Read this to attribute a figure correctly.' It gives a clear use case, though it does not enumerate when not to use it or compare it to alternatives. For a simple metadata tool, this is 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does disclose the exact, case-insensitive matching behavior, but it does not mention whether multiple matching rows are returned, whether the operation is read-only, or how no-match cases are handled.

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 that front-loads the resource, the operation, and the key matching behavior. There is no filler or redundant 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 lookup, the core semantics are present, but the absence of output schema and the lack of guidance on return cardinality, no-match behavior, or when to prefer dataset_search leaves notable gaps. An agent would need additional information to confidently handle edge 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?

Schema description coverage is 0%, so the description must add meaning to the parameters. It indicates that one parameter is a column name and the other is the value to compare, and it adds the case-insensitive behavior. However, it does not explicitly name the parameters or describe expectations such as column name format or value matching syntax.

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 LPreportly dataset where a column equals a value exactly, with case-insensitive matching. It conveys a specific lookup operation and distinguishes itself from the broader dataset_search sibling by emphasizing exact matching, though it does not explicitly name siblings.

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 wording implies when this tool is appropriate, but the description does not explicitly state when to use it versus alternatives like dataset_search, nor does it provide any exclusions or conditions. This makes usage guidance mostly inferential.

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 and handles it well by revealing case-insensitive matching, cell-wide search scope, and a 50-row upper bound. It does not mention default limit behavior or result ordering, but these are minor for a simple read-only 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 communicates the essential behavior, scope, and limits without wasted words. Every clause contributes information.

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 search tool with a small schema and no output schema, the description gives enough context for an agent to invoke it correctly. It could mention the default limit value or explicitly note the read-only nature, but the core semantics are fully covered.

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 only documents the query parameter in detail; the description compensates by explaining that query applies to any cell and by tying the limit to 'up to 50'. This adds meaningful semantics beyond the bare parameter names and ranges.

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 verb-resource relationship: it returns rows of the LPreportly dataset matching a query across any cell. The case-insensitive and 50-row cap details further distinguish it from sibling tools like dataset_row or 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 is implied by the description: search for rows containing text in any cell. However, it does not explicitly state when to prefer this tool over sibling tools such as dataset_row or dataset_top, nor does it list any exclusion 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?

With no annotations, the description carries the behavioral burden and does a good job: it discloses that grouping commas and currency symbols are handled, and that non-numeric rows are excluded and counted. This is genuinely useful beyond the title and schema. It does not mention failure modes or return shape, but the core quirks are surfaced.

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 that front-loads the computed statistics and follows with the key edge-case behaviors. Every clause earns its place; there is no fluff or repetition of the schema.

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 tool with no output schema, the description is nearly complete: it states what statistics are returned and how messy numeric input is handled. A minor gap is the exact output shape and ambiguous phrasing around whether 'count' includes or excludes the non-numeric rows, but the overall context is sufficient for simple use.

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 only provides a string parameter named 'column', and description coverage is 0%. The description compensates by clarifying that the column must be numeric and by explaining how formatted numbers and non-numeric values are treated. This gives the agent enough semantic context to pass a valid column name.

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 together clearly define a summary-statistics tool for a numeric column, enumerating the exact outputs (count, min, max, mean, median, sum). This is specific enough to distinguish it from siblings like dataset_columns and dataset_row: the agent immediately knows it aggregates rather than lists or searches 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 description implies when the tool should be used: whenever an agent needs numeric summary statistics for a dataset column. However, it does not explicitly state when not to use it or mention an alternative, such as dataset_top or dataset_search, leaving some routing inference to the 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 provided, the description carries the full burden of behavioral disclosure. It mentions highest/lowest rows and numeric columns, but it doesn't state behavior around limit defaults, empty results, ties, non-numeric values, or whether the full row or only the selected column is returned. This leaves significant behavioral ambiguity.

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 short and mostly front-loaded, but the phrasing is awkward ('The highest (or lowest) rows of the LPreportly dataset...') and includes the confusing 'LPreportly' typo. The trailing quote adds a use-case but could have been integrated more cleanly. It is concise but not well-structured.

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 no output schema, no annotations, and only 33% parameter coverage, the description is too thin. It doesn't clarify the output shape, the meaning of 'limit', or edge-case behavior. An agent would need to infer or test to use this tool reliably, especially with six sibling tools that could overlap in function.

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 ties 'highest/lowest' to ascending but says nothing about 'column' semantics beyond 'numeric' or about 'limit', including its default and upper bound. This is insufficient compensation for the undocumented schema fields.

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 ranks rows by a numeric column and provides a use-case ('which is the most/least X'), so an agent can grasp the core function. It doesn't explicitly differentiate from sibling tools like dataset_stats or dataset_row, and the odd placeholder 'LPreportly' adds minor confusion.

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 alternatives such as dataset_stats or dataset_search. The description only says what it does, not in what situations it should be preferred, nor any exclusions like 'use dataset_row for exact matches'.

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