site

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 what the tool returns (columns, numeric flags, row count, provenance banner) but does not explicitly state whether it is read-only, has side effects, or requires authentication. However, the phrase 'learn the schema' implies a safe, informational query, which provides some 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 two sentences, front-loading the purpose and ending with a clear usage directive. Every word contributes value with no fluff 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, parameterless tool with no output schema, the description fully covers what the agent needs: it lists the output components and explicitly states when to call it (first). This is sufficient for an agent to invoke it correctly without ambiguity.

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 semantics because none exist, and the baseline for 0 params is 4. The description's mention of specific output fields aligns with the schema's absence of inputs.

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 (Depreo dataset) and the specific information it provides: columns, numeric flags, row count, and provenance banner. It also says 'Call this first to learn the schema,' which distinguishes it from sibling tools and signals its role as the introductory schema tool.

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 directive 'Call this first' provides explicit timing guidance, making it clear this is the initial step before using other dataset tools. It doesn't explicitly say when not to use it, but the strong 'first' instruction effectively communicates its place among 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?

With no annotations, the description carries the full burden. It discloses the key behaviors: filtering by column equality, returning rows, and preserving the order of the given values. However, it does not mention safety (read-only nature), pagination, limits, or behavior when no rows match, leaving gaps for a tool without annotation support.

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, front-loaded sentence that conveys the core behavior and usage context with no wasted words. The key scoping detail ('in the order given') appears before the trailing usage note.

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 is simple with only two parameters and no output schema, but the description does not hint at the return format or edge cases (e.g., empty results). While adequate for straightforward invocation, an agent might need more detail about what 'rows side by side' actually returns.

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. It successfully explains the role of both parameters: 'column' is the field to filter on, and 'values' are the values to match, with the order of values being significant. It adds meaning beyond the bare 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 states what the tool does: it returns rows from the Depreo dataset where the specified column matches any of the given values, preserving the order of values. This is a specific behavior that distinguishes compare from row-level and search siblings, though the verb 'returns' is implied rather than explicit.

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 'for "X vs Y" questions' provides a clear usage context, but it does not name alternatives or explicitly state when not to use this tool. The guidance is implied rather than a structured when-to-use/when-not-to-use comparison with 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not explicitly state that the operation is read-only or free of side effects, though that is implied by the nature of a provenance tool. It also does not mention any auth, rate limits, or return format. For a simple metadata fetch, this is adequate but not exhaustive.

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 concise sentences with no filler. The first sentence front-loads the content (source, date, licence, citation), and the second gives the usage directive. Every word contributes value.

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 zero-parameter tool with no output schema, the description fully covers what the tool does and when to use it. There is no missing information that an agent would need to invoke it 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 baseline is 4. The description does not need to explain any parameter semantics, and it correctly omits any such details.

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 resource (Depreo dataset provenance) and the specific attributes it provides: source, date computed, licence, and citation. This distinguishes it from sibling tools that deal with data content (columns, search, stats, etc.) rather than metadata. The title reinforces the purpose, but the description adds the concrete items returned.

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 second sentence gives explicit usage context: 'Read this to attribute a figure correctly.' This tells the agent when to invoke it (when attribution is needed). It does not explicitly state when not to use it or name alternatives, but given the sibling set, the purpose is clear enough to route correctly.

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 only mentions case-insensitive matching, but does not disclose whether the tool is read-only, how results are ordered, whether multiple rows are returned, error handling, or any side effects. This is minimal disclosure for a tool with zero annotation coverage.

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 is front-loaded with the core operation and includes the key detail of case-insensitivity. It is concise without fluff, though the phrasing 'The rows...' is slightly awkward. It earns its place by conveying the matching semantics.

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 no output schema and no annotations, the description is incomplete. It does not describe the return format (e.g., whether it returns a single row or a list), how to interpret results, or error behavior. It also does not clarify how this differs from dataset_search, which is critical for an agent to select the correct 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?

Schema description coverage is 0%, so the description must compensate for explaining parameters. It mentions 'column' and 'value' in context but does not define what valid column names are, the format of the value, or any constraints beyond minLength. The description adds minimal semantic value over the bare parameter 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 states that the tool returns rows from the Depreo dataset where a specified column exactly matches a value, and the title clarifies it's a lookup by exact key. It is specific enough to distinguish from dataset_search, which likely does fuzzy or broader matching, though it doesn't explicitly name the sibling.

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 provided on when to use this tool versus alternatives like dataset_search. It does not mention prerequisites, limitations, or exclusions. The description implies exact-match usage but does not explicitly state when to choose this over siblings.

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 present, so the description carries the behavioral burden. It discloses substring-like matching, case-insensitivity, and a 50-row cap, which is useful. However, it does not clarify whether 50 is a default or absolute maximum, whether results are ordered, or what fields the returned rows contain.

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, front-loaded with the core behavior, and compactly incorporates the key constraints. There is no filler or redundant restating of schema fields.

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 search tool with no output schema, the description covers what is searched, what is returned, and the result cap. It lacks an example or explicit ordering/pagination details, but the low complexity and sibling context make this a minor omission.

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 documents query ('text to look for in any cell'), but limit is only described by min/max. The description adds meaning by tying query to cell containment with case-insensitivity and clarifying the 'up to 50' behavior. It does not state the default when limit is omitted, leaving a small 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 clearly identifies the resource (Depreo dataset), the output (rows), and the matching rule (cells contain the query, case-insensitive). It is easy to distinguish from siblings like dataset_columns or dataset_stats, though it does not explicitly name an alternative, so it stops short of maximum differentiation.

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 clear context for when to use this tool: when an agent needs dataset rows matching text in any cell. It does not explicitly state when not to use it or point to alternatives, which prevents a 5.

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 behavioral burden. It discloses data-handling behavior: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. However, it does not describe output format or error behavior, leaving some transparency 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?

A single 25-word sentence packs the core result, resource, and parsing behavior with no filler. The key statistics are front-loaded, making it 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 one-parameter stats tool, the description is largely complete: it names the input, output statistics, and special parsing rules. Without an output schema, the exact return structure is unspecified, but the list of statistics implies it, and no critical missing information prevents 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 only parameter, 'column', has zero schema description coverage; the description adds that it refers to a numeric column name in the Depreo dataset, providing essential context. Yet it omits details like column naming conventions or behavior for missing columns, so it only partially compensates for the schema 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?

Description explicitly lists the statistics computed (count, min, max, mean, median, sum) on a numeric column of the Depreo dataset, establishing a clear verb+resource relationship. The title reinforces the purpose and distinguishes it from sibling tools that handle columns, search, comparison, provenance, rows, and 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 usage for computing summary statistics on a numeric column, but it does not explicitly state when to choose this over alternatives or exclude other cases. No comparison with sibling tools is provided, so an agent must infer usage from the function name and description.

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 present, so the description carries the full burden of behavioral disclosure. It states that rows are ranked highest or lowest, but it does not disclose behavior around limit defaults, tie handling, null values, or whether this is a pure read operation with no side effects.

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 behavior and adds a helpful usage gloss. Every word contributes, with no repetition of schema details 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?

For a simple 3-parameter read tool, the description plus schema gives a usable core: choose a numeric column, optionally set a limit and sort direction. But with no output schema and no annotations, it leaves ambiguity around the default limit, tie handling, and the exact row 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?

With only 33% schema description coverage, the description adds useful meaning by specifying that the column must be numeric and by framing highest/lowest ordering. However, the limit parameter is not explained beyond schema constraints, and no extra detail is given about default limit behavior or output shape.

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 the highest or lowest rows of the Depreo dataset by a numeric column, phrased as 'which is the most/least X'. This is a specific verb-and-resource description that identifies the ranking behavior. It does not explicitly distinguish itself from sibling tools, but the operation is distinct enough from search, stats, and 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?

The description implies a ranking use case with 'which is the most/least X' but gives no explicit guidance on when to use this tool versus alternatives. No sibling tool is named, and there is no when-not-to-use or prerequisite guidance.

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