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

No annotations, so description carries burden. It explicitly lists the output contents (columns, numeric flags, row count, provenance banner) and the purpose of schema discovery, which transparently sets expectations for a read-only introspection 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?

One sentence that packs the core output and usage instruction without redundancy; efficient 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?

Provides enough context for a schema-first workflow: identifies the dataset (Yearendo), lists the key output fields, and instructs to call first. No missing information that would cause misuse.

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?

Tool has zero parameters and empty schema, so description adds no parameter-specific meaning. Baseline 3 applies because coverage is 100% (empty 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?

States it provides columns, numeric flags, row count, and provenance banner, plus explicit instruction to call first for schema – clearly distinguishes from siblings like dataset_stats or 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?

Provides explicit usage instruction: 'Call this first to learn the schema.' This tells the agent when to use it, though it doesn't explicitly contrast with alternatives, the priority is 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 bears full responsibility for behavioral disclosure. It states rows are returned in the given order, which is a useful behavioral note, but it does not explicitly confirm it is a read-only operation, describe the output format, or address edge cases like no matches or value limits. The behavior is only minimally 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 a single, concise sentence that front-loads the core operation and the order guarantee. It is appropriately brief without unnecessary fluff, though it could be slightly more explicit about the output without adding bulk.

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 is incomplete for safe invocation. It does not specify the return structure (e.g., list of rows, side-by-side view), potential errors, or the constraint of up to 10 values (though schema enforces this). The 'side by side' aspect from the title is not elaborated, leaving the agent uncertain about the exact result shape.

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 0% schema description coverage, the description must compensate. It explains the roles of 'column' and 'values' indirectly ('whose column is any of the given values') and adds the behavioral detail that order is preserved. However, it does not explicitly define 'column' as a dataset column name or clarify the exact matching semantics (exact match vs. substring), leaving some ambiguity.

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: retrieving rows from the Yearendo dataset filtered by a column matching given values, with order preserved. It distinguishes itself from siblings via the 'X vs Y questions' purpose, which is a clear differentiator from tools like dataset_search or dataset_row.

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 usage hint ('for X vs Y questions') but does not explicitly mention when NOT to use it or reference sibling alternatives. The intended use case is implied but not contrasted with other dataset tools, leaving the agent to infer when this tool is preferred.

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 implies an informational/read-only tool but does not explicitly state that it has no side effects, does not modify data, or makes no external calls. This 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?

The description is concise, consisting of two short sentences that provide the essential information without redundancy 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 no parameters and no output schema, the description adequately covers what the tool returns and why it would be used. No additional context is necessary for 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 coverage is effectively 100% since there is nothing to document. The baseline for 0 params is 4, and the description adds no conflicting or missing parameter information.

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 provenance metadata (source, computed date, licence, citation) for the Yearendo dataset. It is distinct from sibling tools that handle columns, rows, search, stats, and top, and the purpose is immediately understandable.

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 'Read this to attribute a figure correctly' gives a general usage hint, but it does not explicitly state when to choose this tool over siblings or when not to use it. No alternative tools are mentioned.

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?

There are no annotations describing read-only behavior or side effects. The description implies a query operation but does not mention whether it modifies data, nor does it describe output format, pagination, or limits.

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 no redundant wording. It directly states the tool's purpose without unnecessary elaboration.

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 lacks an output schema and does not specify return structure, error handling, or edge cases. It provides minimal context beyond the basic filtering behavior, leaving important operational details 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 description references 'column' and 'value' in context, clarifying that column is the field name and value is the exact match value. However, it lacks details on data types, possible values, or behavior when the column does not exist.

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 Yearendo dataset where a column matches a value exactly, which is distinct from the sibling dataset_search tool. It conveys the core filtering action effectively.

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 provided on when to use this tool versus alternatives like dataset_search or dataset_top. The description implies exact-match use but does not state conditions 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?

With no annotations, the description carries the disclosure burden. It adds case-insensitive matching and the 50-row cap, both useful, but it does not specify default limit behavior, ordering, or what happens when a query matches no rows.

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. The resource and operation are 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 two-parameter read-only search tool, the description covers the core invocation information: matching behavior, case sensitivity, resource, and row cap. Missing output structure and ordering details are secondary for this 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 already describes query as 'text to look for in any cell,' and the description adds case-insensitivity and clarifies that the limit caps returned rows at 50. This goes beyond the raw integer type and maximum 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 the specific operation, resource, and matching semantics: rows of the Yearendo dataset whose cells contain the query. The case-insensitive and 50-row qualifiers distinguish it from sibling tools like dataset_row and 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 clear: call this when you need content-based row retrieval across all cells. It does not explicitly name alternatives or exclusions, but the context is unambiguous enough for an agent to select it appropriately.

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 reveals that grouping commas and currency formats are handled, and that non-numeric rows are excluded and counted. This goes beyond a bare statement of what the tool computes, though it does not cover output format or all edge cases.

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 well-structured sentence. It front-loads the computed statistics, then adds the data-handling caveats, with 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 one-parameter tool with no annotations and no output schema, the description communicates what the tool computes, on what resource, and how messy data is handled. It is nearly complete for an agent to call it correctly, missing only explicit usage boundaries and return formatting details.

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 non-empty string, and schema description coverage is 0%. The description adds that the column must be numeric and belong to the Yearendo dataset, helping somewhat, but it does not specify valid column names, case sensitivity, or how missing values are treated beyond non-numeric rows.

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 computation: count, min, max, mean, median, and sum for a numeric column of the Yearendo dataset. It is specific about the resource and operation, though it does not explicitly differentiate itself from siblings like dataset_top 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?

No explicit guidance is given about when to use this tool versus alternatives, and no exclusions or sibling comparisons are mentioned. The intended use is implied by the title and description, but the agent is left to infer when dataset_stats is the right choice.

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 behavioral burden. It discloses that the tool ranks by a numeric column and supports both highest and lowest ordering. However, it does not mention tie-handling, behavior on non-numeric columns, null values, or what the returned rows look like, leaving some uncertainty.

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 that communicates the core behavior and the mental model ('most/least X'). It is slightly awkward with the dash and quote, but it contains no filler and is appropriately short.

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 small tool with 3 parameters and no output schema, the description is adequate but not fully complete. It gives the key ranking behavior and mentions the numeric-column requirement, but it does not describe the return format or the limit semantics beyond what the schema already states.

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% since only 'ascending' has a description. The description adds meaning by clarifying that 'column' must be numeric, but it does not explain the 'limit' parameter or otherwise compensate for the schema's sparse parameter documentation.

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 identify the operation: ranking rows of the Yearendo dataset by a numeric column and returning the highest or lowest values. The phrase 'which is the most/least X' gives a concrete purpose. It does not explicitly contrast with siblings like dataset_stats or dataset_search, but the behavior is distinct enough to be inferred.

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 to use the tool: when the user asks for the most or least values of some numeric field. However, it offers no explicit guidance on when not to use it or which sibling tool to prefer for related tasks such as search or summary statistics.

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