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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. It discloses the exact content returned (columns, numeric flags, row count, provenance banner), which is transparent for a read-only schema tool. It does not mention response format or side effects, but for a zero-parameter tool, this is adequate.

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 efficiently communicates the tool's output and usage. It front-loads the key information and includes the 'call first' directive without any fluff.

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 description fully covers the tool's functionality for a schema-introspection tool with no parameters. It specifies the data returned and the recommended usage, which is sufficient for an agent to invoke it correctly. The absence of an output schema is compensated by the clear enumeration of what the tool provides.

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 doesn't need to explain any. According to the baseline, a 4 is appropriate since there are no parameters to document.

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 the tool returns the columns, numeric flags, row count, and provenance banner of the Lanyardo dataset, which is a specific and clear purpose. It also distinguishes itself from siblings like dataset_stats and dataset_provenance by focusing on schema learning.

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 instructs to 'Call this first to learn the schema,' providing clear when-to-use guidance. It does not explicitly contrast with alternatives, but the 'first' positioning implies precedence over other dataset 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 burden. It discloses the matching behavior ('whose column is any of the given values') and the ordering behavior ('in the order given'). It does not mention read-only status, but that is implied as a data query tool. Overall, the behavior is transparent enough for the intended use.

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 that front-loads the key behavior and use case. No redundant words or unnecessary details. The title is also concise and descriptive.

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 output schema, the description indicates the result is 'rows of the Lanyardo dataset', which suggests full-row objects. It does not specify whether all columns are returned or how no-match results are handled, but for a straightforward comparison tool this is mostly adequate. The ordering behavior is clearly stated, which is important for side-by-side comparison.

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 types and constraints, with no per-parameter descriptions. The description explains the role of both parameters: 'column' is the field to match against, and 'values' are the list of allowed values, with the output ordered by that list. This adds meaningful context beyond the raw 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 title 'Compare rows side by side' and description 'The rows of the Lanyardo dataset whose column is any of the given values, in the order given' clearly state the tool's function. It retrieves matching rows based on a column and value list, which is distinct from sibling tools like dataset_row (single row), dataset_search (search across columns), and dataset_stats (aggregate statistics).

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 states the use case 'for "X vs Y" questions', which guides when to use this tool. It does not explicitly name sibling alternatives, but the use case hint is sufficient to differentiate it from the other dataset 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, but the description implies a read-only operation (retrieving metadata). There is no mention of side effects, destructive actions, or authentication requirements. Since the tool simply returns provenance information, the risk is low, but the lack of explicit statement about non-destructiveness leaves 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?

The description is concise, using one sentence to list the key elements and another to give a practical use case. No redundant words or unnecessary details. The structure is clear and front-loaded with the core 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 tool that returns metadata, the description is complete enough: it tells what to expect (source, date, licence, citation) and when to use it (attribution). It does not specify the exact output format or whether it returns a single object or list, but that is likely self-evident for a provenance tool. Overall, adequate for 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 tool has zero parameters, so the description does not need to explain parameter behavior. The schema coverage is 100% (empty schema), and the description focuses on the output semantics. Given no parameters, a baseline score of 4 is appropriate.

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 provides source, computation date, licence, and citation for the Lanyardo dataset. This distinguishes it from sibling tools like dataset_row or dataset_stats, which focus on data content or statistics. The title reinforces the purpose of provenance and citation.

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 includes an explicit usage hint: 'Read this to attribute a figure correctly.' This tells the agent when to use the tool (when citation or attribution is needed). However, it does not explicitly mention when not to use it or compare with alternatives, but the context of siblings makes this clear.

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 discloses meaningful behavior: exact equality, case-insensitive matching, and returning rows. It does not explicitly state read-only behavior or error cases, but 'look up' strongly implies a safe retrieval.

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, direct sentence with no unnecessary words or repetition. It efficiently captures the tool's 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?

There is no output schema, and the description only says rows are returned; it does not clarify whether one or multiple rows are returned, the row shape, ordering, or possible errors. This is adequate for a simple lookup but leaves some gaps.

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 minLength, but the description explains the core semantics: a column is compared to a value for exact case-insensitive equality. It does not enumerate valid columns or value formatting, but the two parameters are adequately clarified.

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 the operation: retrieve rows from the Lanyardo dataset where a column exactly equals a value, with case-insensitivity. This distinguishes it from broader search or comparison 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 description clearly implies exact-match lookups and notes case-insensitivity, giving good context. It does not explicitly name dataset_search as the alternative for non-exact queries, but the contrast is reasonably clear.

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 present, the description must carry the behavioral burden. It clearly indicates a read-only operation (selecting rows), which is a positive. However, it does not describe edge cases such as empty results, case-insensitivity limits, or any error behavior, and it does not explicitly state that it does not modify data, though that is strongly implied.

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 extremely concise—a single sentence with no unnecessary words. It delivers the essential information without fluff, 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?

For a simple search tool, the description covers the main context: what dataset is searched, what counts as a match, and the result size cap. The absence of an output schema means the agent does not know the exact result format, but the description's 'rows' phrasing gives a reasonable hint. Overall, it is sufficient for 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 schema already describes both parameters, and the description adds useful semantic detail by specifying that the query is matched against cells and that matching is case-insensitive. It does not elaborate on the 'limit' parameter beyond the schema's min/max, but the core semantics are well covered.

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 purpose: it searches the Lanyardo dataset for rows whose cells contain the given query. The behavior is unambiguous and easily distinguishable from the sibling tools by its search-and-filter nature.

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 does not provide explicit guidance on when to use this tool versus alternatives such as dataset_row or dataset_stats. It does not mention any prerequisites or conditions that would make this tool the preferred choice, leaving the agent to infer applicability from the name and behavior.

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 usefully explains that grouping commas and currency are handled and that non-numeric rows are excluded and counted, adding context beyond the schema. This is helpful but does not cover edge cases like an empty column or return format.

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, efficient sentence that front-loads the core statistics and appends relevant data-cleaning notes. Every word earns its place; there is no redundant phrasing or 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?

Given there is no output schema and no annotations, the description covers the core functionality and data-cleaning behavior, which is essential. However, it does not specify the return structure (e.g., whether results are returned as a dict) or how to handle a column that is entirely non-numeric. These are moderate gaps for an otherwise simple 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 coverage is 0% — the description never mentions the 'column' parameter directly. While it refers to 'a numeric column,' it does not explicitly state that the 'column' parameter specifies the column name, leaving the agent to infer this from context. For a tool with a single parameter, this is a significant 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 states a specific verb ('compute'), the resource ('numeric column of the Lanyardo dataset'), and enumerates the exact statistics (count, min, max, mean, median, sum). This clearly distinguishes it from sibling tools like dataset_columns, dataset_search, or dataset_top, which serve different 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?

The description implies it should be used for numeric column statistics, but does not explicitly name alternatives or state when not to use it. An agent can infer the use case from 'numeric column,' but there is no direct guidance on choosing between this and 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. It discloses the highest/lowest behavior and the role of the 'ascending' parameter, but does not mention edge cases (e.g., null values, tie-breaking, sorting stability), the return format, or any side effects. It adds modest behavioral context beyond the title but leaves significant gaps.

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 that front-loads the purpose and includes a clarifying example. There is no fluff or redundancy, making it appropriately sized 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?

Given no output schema and no annotations, the description should explain what the tool returns (e.g., full rows vs. just values), the default limit, and error conditions. None of these are addressed. For a ranking tool, an agent would benefit from knowing whether it returns the actual rows or only identifiers, and what happens with non-numeric columns. The description is too sparse for a complete understanding.

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% (ascending is described). The description adds value by clarifying that the column must be numeric, which is not in the schema. However, it does not explain the 'limit' parameter's default or behavior, and the 'ascending' parameter is already described in the schema. It partially compensates for low coverage but not completely.

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 action: 'Rank rows by a numeric column,' and clarifies it returns the highest or lowest rows via the 'ascending' parameter. The phrase 'which is the most/least X' makes the intent unmistakable and distinguishes it from siblings like dataset_stats (which computes aggregates) and dataset_search (which searches).

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 ranking questions ('which is the most/least X') but does not explicitly mention when to prefer this over sibling tools or when not to use it. There are no exclusions or alternative references, so an agent must infer the right context.

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