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

No annotations are present, and the description does not explicitly state whether the operation is read-only or has side effects. While it is implied to be a harmless schema retrieval, the absence of explicit transparency about effects leaves some 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 a single, focused sentence that conveys all necessary information without redundancy. It is well-structured and front-loads the core purpose.

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?

Even without an output schema, the description lists the exact information returned (columns, numeric flags, row count, provenance banner) and gives a usage hint. This fully equips an agent to understand the tool's functionality and expected results.

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 no parameters, and the schema coverage is 100% by default. The description correctly omits parameter details, and no additional explanation is needed for this empty parameter list.

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 precisely states the tool's function: retrieving columns, identifying numeric ones, row count, and provenance banner for the HardscapeDesk dataset. It is specific and unambiguous, clearly distinguishing it from sibling tools that perform other operations.

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?

Explicitly instructs to call this tool first to learn the schema, providing clear when-to-use guidance. This helps the agent sequence it appropriately before other dataset operations.

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, and the description does not mention whether the operation is read-only, has side effects, requires authentication, or is subject to rate limits. It only describes the data returned, not the operational behavior.

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 essential behavior without unnecessary filler. It is easy to read and front-loads the key 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?

While the description conveys the basic filtering and ordering, it does not specify whether each value corresponds to exactly one row (implying a 1:1 mapping for side-by-side comparison) or whether duplicate matches are included. It also omits details about the output format, which could be important for correct usage.

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 'values' and explains that rows are matched when the column equals any of the given values. However, it does not clarify the exact matching semantics (e.g., exact string match) or whether multiple rows per value are returned, leaving some meaning to be inferred from the schema 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 states the tool returns rows from the HardscapeDesk dataset filtered by a column matching any of the given values, with order preserved for side-by-side comparison. This is specific and distinguishes it from generic row retrieval.

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 use case ('for X vs Y questions') that hints at when to use it, but does not explicitly contrast with alternatives like dataset_search or dataset_row, leaving some ambiguity about when one should be preferred over the other.

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 states what information is returned but does not explicitly mention side effects, return format, or error behavior. With no annotations to fill that gap, the description carries the burden, though the read-only nature 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 two short sentences, directly naming the returned fields and the intended use. There is no redundant wording or unnecessary 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?

Given no input parameters and no output schema, the description fully covers what the tool provides and why it matters. It gives enough context for an agent to select and invoke the tool correctly.

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 trivially complete and there is nothing to explain. The baseline of 4 applies because the description adds no parameter-specific meaning where 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 identifies the tool as returning provenance metadata: source, computation date, licence, and citation. It is unmistakably distinct from sibling tools that focus on data access or 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 phrase 'Read this to attribute a figure correctly' gives a direct when-to-use instruction. It does not enumerate alternatives, but given the tool's single-purpose nature and zero parameters, this is sufficient.

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 discloses the core behavior: exact matching with case-insensitivity, and specifies the dataset. However, it does not disclose behavior for edge cases (e.g., no matches, multiple matches, invalid column) or return format. This is a moderate disclosure, adequate but with 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 concise sentence that front-loads the key information: the dataset and the matching rule. No filler words, every part contributes.

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 lookup tool with two string parameters and no output schema, the description provides the essential information: dataset name, matching criteria, and case-insensitivity. It doesn't mention error handling or pagination, but these are not critical for a basic lookup. It is reasonably complete.

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 string types with minLength. The description adds meaning by explaining that 'column' is the column name and 'value' is the value to match, and clarifies the matching is exact and case-insensitive. This compensates for the 0% schema description coverage, though it doesn't specify format details like whether column must be an existing column.

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 rows from the HardscapeDesk dataset where a specified column matches a value exactly, case-insensitively. It uses a specific verb ('returns') and resource, and distinguishes from siblings like dataset_search (which likely does fuzzy search) and dataset_top (which returns top 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?

There is no guidance on when to use this tool versus alternatives. The description does not mention any conditions or exclusions, nor does it reference sibling tools. An agent would have to infer usage from the name and description 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 full burden of behavioral disclosure. It discloses case-insensitivity and the limit cap, but does not mention whether the operation is read-only, any side effects, return format details, or pagination behavior. Adequate but minimal for a 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 sentence with no waste, front-loading the core behavior and constraints. Every word contributes to the agent's understanding.

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, so the description must specify what is returned. It says 'Rows of the HardscapeDesk dataset' which implies the output, but it does not clarify whether full rows are returned, how results are ordered, or if any other metadata is included. For a search tool this is a moderate gap.

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 50%: the query parameter has a description ('text to look for in any cell'), while limit lacks a description but has min/max constraints. The description adds case-insensitive behavior, which is not in the schema, but does not explain limit beyond the schema's maximum. Baseline 3 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 states a specific verb ('search'), a specific resource ('HardscapeDesk dataset'), and precise criteria (cells containing query, case-insensitive, up to 50). It clearly distinguishes this from sibling tools like dataset_stats or dataset_row, which have 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 does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions. Usage is implied by its search functionality, but no explicit guidance or comparison with siblings is provided, leaving the agent to infer context.

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 behavioral disclosure. It transparently notes that grouping commas and currency symbols are parsed, and that non-numeric rows are excluded and counted. This gives the agent insight into edge-case handling. However, it does not state the output format (e.g., a JSON object with named fields) or behavior for empty columns, which would strengthen 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 sentence that front-loads the core statistics list and places the additional handling details in a parenthetical. It is efficient and avoids redundancy, though the parenthetical is slightly dense. The structure is clear 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?

For a tool with one parameter and no output schema, the description covers the essential points: the dataset, the expected column type, the computed metrics, and special-case handling. It lacks an explicit statement about the return shape, but the list of statistics strongly implies a structured result. Minor gaps remain but the tool is largely self-explanatory.

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 a generic 'column' parameter with no description (coverage 0%), so the description must add meaning. It does so by specifying that the column must be numeric and that formatted values (commas, currency) are handled. This goes beyond the schema and clarifies expected input semantics.

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 explicitly names the operation (summary statistics), the resource (a numeric column of the HardscapeDesk dataset), and the specific statistics computed (count, min, max, mean, median, sum). It also mentions handling of formatted numbers, which clearly differentiates it from sibling tools like dataset_row or dataset_top, which likely return raw rows or top values.

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 that the tool is used to obtain descriptive statistics for a numeric column, but it does not explicitly state when to prefer this over sibling tools like dataset_top or dataset_search, nor does it provide exclusion criteria (e.g., 'use dataset_row for individual records'). The context is clear enough for basic selection, but no alternatives 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?

The description discloses the core ordering behavior and the ascending parameter is documented in the schema. However, with no annotations, it does not fully describe returned row shape, default limit behavior, tie handling, or whether the operation is strictly read-only.

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 one concise sentence that front-loads the key behavior and includes a useful natural-language query template. No unnecessary words or repetition.

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 ranking tool, the description plus schema is largely sufficient to understand what the tool does and how to call it. Minor gaps like default limit and return format prevent a perfect score, but the overall context is adequate.

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 (only ascending has a description), but the description adds the important constraint that the column must be numeric. It does not clarify the default limit or explain how limit interacts with ranking, leaving some parameter semantics incomplete.

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 tool ranks dataset rows by a numeric column and returns the highest or lowest values. The phrase 'which is the most/least X' makes the purpose concrete and distinguishes it from sibling tools like dataset_search 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 description gives a clear use case: retrieving top or bottom rows by a numeric column. It does not explicitly name alternatives or exclusions, but the 'most/least X' framing provides enough context for when this tool is appropriate.

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