site

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

The description accurately discloses the tool's output (columns, numeric flags, row count, provenance banner) without any annotations. It implies a read-only operation but does not explicitly state side effects or error behavior; however, for a simple schema inspection tool, this level of transparency is sufficient.

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 packs all necessary information: what it returns and when to use it. No fluff or redundancy, making it highly efficient for an agent to parse.

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 the tool's simplicity (no parameters, simple output), the description fully covers its purpose and usage context. The instruction to call it first adds valuable context for sequencing with sibling tools, making it complete for its intended role.

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% (empty properties). The description adds no parameter details, but since there are none, the baseline of 4 applies. There is no gap in parameter understanding.

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 columns, numeric flags, row count, and provenance banner, and explicitly instructs to call it first to learn the schema. This makes the purpose unambiguous and distinct from sibling tools that handle other dataset 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?

The phrase 'Call this first to learn the schema' provides explicit guidance on when to use this tool, positioning it as the initial step before other dataset actions. This is a clear usage directive that helps an agent decide when to invoke it.

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 discloses that rows are returned in the order of the input values, which is useful, but it does not clarify whether full rows are returned or only the specified column, nor does it mention potential errors, pagination, or performance implications. The description is thin on behavioral detail.

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 action and includes the key ordering behavior. It contains no redundant phrases and is appropriately sized for a simple tool, though it could be slightly more structured to separate the core function from the usage hint.

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 that there is no output schema, the description should clarify what the returned rows look like, but it does not. It explains the filtering and ordering but omits return format details (full rows vs. column values) and any limits or defaults. For a simple two-parameter tool, this is a moderate gap, but the core calling information is present.

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 explain the parameters. It does clarify that 'column' is the column to filter on and 'values' are the values to match, and it implies a minimum of two values via the 'X vs Y' phrasing. However, it does not specify exact matching semantics (e.g., case sensitivity, exact string match) or how the column name should be provided, so it only partially compensates for the missing schema descriptions.

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 Orgbix dataset filtered by a column matching any of the given values, preserving the given order. It is specific about the operation and resource, and the 'X vs Y' hint distinguishes it from general search or single-row retrieval. It does not explicitly name sibling tools, but the wording makes its niche evident.

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 side-by-side comparisons ('X vs Y' questions) and mentions the ordering behavior, which gives context on when it is appropriate. However, it does not explicitly say when not to use it or name alternative tools such as dataset_row or dataset_search, leaving some inference required.

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 does so by explicitly framing the operation as a read ('Read this') and enumerating the exact information returned: source, date computed, licence, and citation. It does not detail output formatting, but that is minor for a parameterless metadata 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?

Two sentences with no filler; the core content is front-loaded and every clause serves the purpose. It is an ideal length for a trivial parameterless tool.

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?

Without an output schema, the description sufficiently explains the returned information. It could mention whether the result is a formatted string or structured fields, but for provenance attribution the stated content list is enough for correct tool selection and 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?

There are zero parameters and 100% schema description coverage, so there is nothing to explain at the parameter level. The description still adds value by clarifying what the output fields will be.

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?

Clearly identifies a specific resource (the Orgbix dataset) and the content it provides: source, computation date, licence, and citation. The phrase 'attribute a figure correctly' gives a concrete use case that separates it from sibling tools focused on columns, rows, stats, and 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?

'Read this to attribute a figure correctly' explicitly tells the agent when to invoke this tool. It does not explicitly list exclusions, but none of the sibling tools overlap with citation/provenance duties, so the guidance 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?

The description implies a read-only lookup but does not explicitly state side effects, permissions, or lack thereof. With no annotations, the description carries the full burden and only partially addresses 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 with no redundancy. It directly conveys the core functionality without extraneous 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?

For a simple lookup with two string parameters and no output schema, the description is sufficiently complete. It does not address potential output format or error handling, but these are not explicitly required.

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 clarifies that 'value' is matched exactly and 'column' is the target column, adding some meaning beyond the schema. However, it does not provide details on allowed formats, enumerations, or edge cases.

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 where a column equals a value exactly, with case-insensitivity. It is specific about the operation but does not explicitly distinguish from sibling tools like 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 guidance is provided about when to use this tool versus alternatives. The description only states what it does, not when it should be 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 provided, the description carries the burden of disclosing behavior. It does disclose case-insensitive substring matching and the 50-row limit, which are meaningful behavioral traits. It does not mention result ordering, default limit behavior, or explicitly state that the operation is 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?

One dense sentence conveys the dataset, the matching rule, the case sensitivity behavior, and the result cap. There is 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 simple two-parameter search tool, the description is largely complete: an agent knows what to match, how matching behaves, and the maximum result size. It falls slightly short by not describing the default when limit is omitted, result ordering, or the returned row structure, especially since there is no output schema.

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 as 'text to look for in any cell'. The description adds case-insensitive matching and the 50-row cap, but it does not explain how the optional limit parameter maps to that cap or whether 50 is the default. With only 50% schema description coverage, this is partial compensation.

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 states a precise behavior: returning rows of the Orgbix dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This is a specific verb-resource pairing and clearly separates search from sibling tools like dataset_stats, dataset_top, 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 intended use is implied by the matching behavior, but there is no explicit guidance about when to prefer this tool over alternatives or when not to use it. Sibling tools such as dataset_row, dataset_stats, and dataset_compare are not mentioned as 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 does disclose useful edge-case behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it does not mention how missing values are treated or what happens when the column does not contain numeric data.

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 a parenthetical edge-case note. It is well-structured and contains 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?

The description lists the returned statistics and key edge cases, which is sufficient for an agent to understand the tool's behavior. There is no output schema, but the absence of a formal return format is not critical given the simple output.

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 specifies that 'column' is a required string, so the description adds meaning by clarifying it should be a numeric column. It explains that formatting is handled, but it does not enumerate valid column names or provide examples, 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 clearly states that the tool computes summary statistics for a numeric column and lists exactly which statistics are returned. It does not explicitly contrast with sibling tools like dataset_top or dataset_search, 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 gives no explicit guidance about when to use this tool versus alternatives such as dataset_search or dataset_top. It mentions handling of formatted numbers and non-numeric rows, but not when the tool should be 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?

The description states the core behavior (returning highest/lowest rows by a numeric column) and the ascending parameter indicates ordering direction. However, it does not mention default limit behavior, handling of ties or nulls, error conditions for non-numeric columns, or output format. Since there are no annotations, more behavioral disclosure would be expected.

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 to the point, using a clear format with an example query. It avoids unnecessary detail while conveying the essential purpose. The structure is clean and easy to parse.

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 and the description covers the main intent, but important contextual details are missing: no default for limit, no mention of output schema or row structure, and no error handling notes. Given the presence of sibling tools, a sentence on when to choose dataset_top would improve completeness.

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?

Only the ascending parameter has a description in the schema; column and limit lack descriptions. The tool description mentions 'numeric column' which hints at the column parameter, but limit is not explained at all. With only 33% schema coverage, the description does not sufficiently compensate for the missing parameter 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 states the tool ranks rows by a numeric column and returns the highest or lowest values, with the example query 'which is the most/least X'. The title reinforces this purpose. It is easily distinguishable from sibling tools like dataset_search or 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 does not explicitly state when to use this tool versus alternatives such as dataset_stats or dataset_search. It provides an example question but no guidance on selection criteria or scenarios. Sibling tools are listed but no comparisons are made.

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