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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 behavioral burden. It discloses the exact output contents (columns, numeric flags, row count, provenance banner), implying a read-only metadata operation. It omits potential edge cases or cost notes, but for a schema-inspection 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?

Two sentences with no filler: the first enumerates the outputs, the second gives the usage directive. Information is front-loaded and every word 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, simple metadata tool with no output schema, the description fully covers what an agent needs: what it returns and when to call it. There are no missing parameters or return-format requirements to document.

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?

There are zero parameters, so the input schema is trivially covered at 100%. Per the baseline for 0-parameter tools, a score of 4 is appropriate; the description adds no parameter-specific info because none is needed.

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 the columns, which are numeric, row count, and provenance banner for the PerDiemDesk dataset. It uses specific resource and content terms, and the instruction 'Call this first to learn the schema' distinguishes it from sibling data-access 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 gives an explicit usage directive: 'Call this first to learn the schema,' positioning it as the entry point before other dataset tools. It does not name alternatives, but the timing guidance is clear and practical.

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 carry the full behavioral burden. It says rows are returned in the given order for matching values, but it does not explicitly state that this is a read-only operation, how results are formatted, or what happens when no values match.

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 brief and contains no filler. It communicates the main selection rule and ordering behavior in one sentence, though the grammar is slightly awkward.

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 has no output schema and no parameter descriptions, so the description should provide enough context for an agent to know what is returned and how to call it safely. It gives the selection rule but omits return format, edge cases, and explicit relationship to sibling tools.

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 maps 'column' and 'values' by saying 'whose column is any of the given values', and it mentions ordering. However, schema description coverage is 0%, and the description does not explain constraints like minimum/maximum values, string types, or how values should be formatted.

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 clearly indicate the tool retrieves rows from the PerDiemDesk dataset for given column values, which distinguishes it from sibling tools that search, summarize, or list columns. The phrase 'for "X vs Y" questions' adds useful intent context.

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 comparison questions but does not explicitly state when to prefer this tool over siblings like dataset_row, dataset_search, or dataset_top. No alternative guidance or selection criteria are provided.

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. It states exactly what information will be returned (source, computed date, license, citation), which is the core behavioral trait. It does not explicitly state it is read-only or side-effect-free, but the nature of the tool makes that clear. The description adds value beyond a simple 'get provenance' by enumerating the fields.

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 redundant wording. The core information is front-loaded in the first sentence, and the usage directive is concise and actionable. Every word 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 simple, parameterless metadata lookup tool with no output schema, the description is fully sufficient. It tells the agent what the tool returns and when to use it. No additional context is needed for correct invocation or 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?

The tool has zero parameters, and the schema coverage is trivially 100%. The description does not need to explain any parameters. The baseline for 0-parameter tools is 4, and the description fulfills that by not attempting to describe nonexistent 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 identifies the tool's purpose: it returns provenance metadata (source, computed date, license, citation) for the PerDiemDesk dataset. It is distinct from sibling tools which focus on columns, comparison, rows, search, stats, and top values, so an agent can easily differentiate.

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 provides a clear usage context: 'Read this to attribute a figure correctly.' This implies the tool should be used when attribution is needed. It doesn't explicitly list exclusions or alternative tools, but the distinct purpose makes it obvious that it is not for data manipulation or queries.

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?

The description explains the matching behavior, including exactness and case-insensitivity, but does not state whether the operation is read-only or mention edge cases like no matches, multiple matches, or errors. With no annotations provided, the description carries the burden of transparency and only partially satisfies it.

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 title and description are very short and directly convey the core purpose without unnecessary detail. The one-sentence description is front-loaded and easy to scan.

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 provides enough information for a basic exact-match row lookup, but with no output schema it omits return format, result multiplicity, sorting, pagination, and no-result behavior. This leaves some context missing for an agent planning to invoke 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 input schema has no parameter descriptions and the prose only says 'a column equals a value exactly.' It does not clarify which columns are valid, what value format is expected, or provide examples, so the parameters are only minimally understood.

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 PerDiemDesk dataset where a column exactly matches a value, case-insensitively. This distinguishes it from the sibling dataset_search tool, though it does not explicitly name that 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?

The description does not explicitly say when to use this tool versus dataset_search or other sibling tools. It implies exact-match lookup through the wording, but provides no direct guidance on selection criteria or alternatives.

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?

The description reveals key behavior: it returns rows that contain the query (case-insensitive) and limits results to 50. However, it does not explicitly state whether the operation is read-only or if any side effects occur, and no annotations are provided to clarify. This leaves some behavioral aspects 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 succinct and directly to the point, capturing the essential functionality in a single sentence. No redundant words or unnecessary details are present.

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 search operation, the description provides sufficient context: it specifies the dataset, the matching criterion, case sensitivity, and the result cap. Since there is no output schema, it does not need to explain return formats, and edge cases like empty results or error handling are not critical for this context.

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 query parameter has a clear schema description ('text to look for in any cell'), but the limit parameter lacks a schema description. The tool description mentions 'up to 50,' which partially clarifies the limit's purpose, but the meaning of the limit parameter itself is still not fully explicit.

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 specifies the action (search), the resource (PerDiemDesk dataset), and the scope (rows whose cells contain the query, case-insensitive, up to 50). It does not explicitly name a sibling tool to distinguish itself, but the purpose is unambiguous.

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 no explicit guidance on when to use this tool versus the sibling tools (e.g., dataset_columns, dataset_stats). Usage must be inferred from the tool's name and description, but no conditional advice or alternative selection criteria are given.

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 burden of behavioral disclosure. It usefully reveals that grouping commas and currency are normalized and that non-numeric rows are excluded and counted, which are non-obvious behaviors an agent needs to know.

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 conveys the full metric set and the important data-handling caveats. Every element is useful, and the key output names are front-loaded before the behavioral notes.

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, low-complexity tool, the description covers selection and invocation well. The main gap is the absence of an output schema and no mention of the return format, leaving the exact structure of the results slightly unspecified.

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 defines 'column' as a string with minLength 1, and schema description coverage is 0%, so the description must add meaning. It clarifies that the parameter should be a numeric column of the PerDiemDesk dataset, but it does not provide examples, exact column-name expectations, or how to discover valid column names.

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 concrete operation—computing count, min, max, mean, median, and sum—for a numeric column of a specific dataset. It clearly differentiates from siblings like dataset_columns and dataset_search by specifying the statistical aggregation scope.

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 use for summarizing numeric columns in the PerDiemDesk dataset and notes non-numeric rows are excluded. However, it does not explicitly state when to prefer this tool over sibling tools or mention any circumstances where it shouldn't be used.

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 only states that rows are ranked by a numeric column, but fails to mention how ties are handled, what happens with non-numeric columns, whether nulls are excluded, or what the default limit is. The schema hints at limit and ascending, but the description adds no additional behavioral context.

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 with an illustrative example. It is front-loaded with the core action (ranking rows). While it is not structured, it is appropriately brief and avoids unnecessary verbosity.

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 three parameters and no output schema, the description is too sparse. It does not mention the output format (e.g., which columns are returned, order), default limit, error conditions, or any constraints. An agent would have to infer many details, making it incomplete for confident 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?

Schema description coverage is only 33% (only 'ascending' has a description). The tool description does not explain 'column' or 'limit' beyond what the schema already states. It adds a hint that the column should be numeric, but that's already implied by the title. For a tool with low schema coverage, the description should compensate but doesn't.

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 of a specific dataset (PerDiemDesk) by a numeric column, returning the highest or lowest rows. The phrase 'which is the most/least X' provides a concrete usage example. This distinctly separates it from siblings like dataset_search (searching) and dataset_stats (aggregates).

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 on when to use this tool versus alternatives. The description does not mention any conditions for selecting this over dataset_stats or dataset_search, nor does it exclude any scenarios. The agent is left to infer when ranking top/bottom rows is appropriate.

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