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

No annotations are provided, so the description carries the burden. It describes what information is returned but does not explicitly state that the operation is read-only or side-effect-free, though the zero-parameter schema strongly implies 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 description is concise and well-structured, covering all key outputs in a single sentence plus a clear usage directive. No unnecessary words or redundancy.

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 elements well enough for an agent to know what to expect, despite lacking an output schema. It does not detail output formatting, but that is not essential for this simple schema-introspection 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?

There are no parameters and the schema coverage is complete. The description does not need to explain parameters because none exist, so the baseline score applies.

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 schema-related details: columns, numeric flags, row count, and provenance banner. It also explicitly says to call it first, reinforcing its role as an introductory schema exploration 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?

It gives explicit usage guidance with 'Call this first to learn the schema', which tells the agent when to use it. It does not mention alternatives or when not to use it, but the first-step instruction is sufficient for typical schema discovery.

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. It discloses that rows are returned for any matching value and that the output order follows the given values, which are key behavioral details. It does not mention side effects or pagination, though as a query tool it is presumably read-only; the input schema does cap values at 10, but this is not reflected in the description.

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, direct sentence with no redundant information. It effectively conveys the core functionality 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?

For a tool with two simple parameters and no output schema, the description provides sufficient context to understand the operation. It explains the input (column and values) and the output (rows) in a straightforward manner, and the sibling tool list helps place it within the dataset tool family.

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 names and types with no per-parameter descriptions. The tool description clarifies that 'column' is the column to filter on and 'values' are the values to match, giving basic semantic meaning. It does not specify constraints like whether the column must exist or if matching is case-sensitive, but the overall meaning is adequate for basic use.

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 it retrieves rows from the TakeoffDeck dataset based on a column and a list of values, preserving the order of the values. It also differentiates itself from sibling tools like dataset_row and dataset_search by focusing on comparing multiple values, making the purpose 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 phrase 'for X vs Y questions' provides a clear usage hint that this tool is suited for comparative scenarios. However, it does not explicitly contrast it with alternatives such as dataset_search or dataset_stats, leaving some inference to the agent.

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 phrase 'Read this' implies a read-only operation, but the description does not explicitly state that it has no side effects or permissions. Since no annotations are present, the description carries this burden and is only partially 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?

Two concise sentences with no redundancy; every word contributes to the tool's purpose and usage.

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 covers the essential provenance elements and gives a clear use case, making it complete for the tool's simple scope.

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 the description adds no parameter-specific information. With full schema coverage (trivially), the baseline applies.

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 provides provenance info (source, date, licence, citation) for the dataset, and distinguishes it from sibling tools by focusing on attribution.

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 explicitly advises reading to attribute a figure correctly, giving a clear when-to-use scenario and practical guidance.

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. It implies a read-only lookup (no side effects mentioned), but it does not explicitly state that no mutations occur or whether it returns an error for no matches. 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 and to the point, capturing the core functionality in a single sentence without unnecessary detail. It is well-structured 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 sibling tools (e.g., dataset_search, dataset_columns) provide context that this is a lookup operation, but the description does not explicitly differentiate itself. It would benefit from noting that it returns exact matches (case-insensitive) as opposed to search which may support partial or fuzzy matching. Given the simplicity, it is adequate but not fully 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 parameter names and types; there are no per-parameter descriptions. The description mentions 'column' and 'value' in the context of matching, but does not clarify what values are valid (e.g., column names, data types) or edge cases (e.g., multiple matches). This leaves significant 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 the tool looks up rows in a dataset based on an exact column-value match. The verb 'look up' and the resource 'TakeoffDeck dataset' are specific, though it could be more explicit about the return format (e.g., single row vs. multiple 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?

The description indicates that matching is 'exactly' and 'case-insensitive', which distinguishes it from a fuzzy search. However, it does not explicitly state when to prefer this over the sibling tool 'dataset_search', leaving some ambiguity for the agent.

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 discloses important behavioral details: case-insensitive matching, cell containment semantics, and a result cap of 50. It does not mention default limit behavior, sorting, or whether it is read-only, but the core behavior is 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, focused sentence that communicates the essential behavior with no unnecessary words. It is well-structured 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?

For a simple search tool with no output schema or annotations, the description covers the main purpose, matching behavior, case sensitivity, and result limit. It lacks details like default limit or sorting order, but these are not critical for the agent to understand and use the tool effectively.

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 is described in the schema, and the description adds case-insensitivity. The limit parameter is not described in the schema, but the description clarifies the maximum result size with 'up to 50'. This adds meaningful context beyond the schema, though the default limit is not specified.

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 TakeoffDeck dataset where any cell contains the query, with case-insensitive matching and a limit of 50. This distinguishes it from the sibling tools like dataset_columns, dataset_stats, or dataset_top by focusing on row-level content 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?

The description explains what the tool does but does not explicitly say when to use it compared to the sibling tools. It implies usage for content-based row lookup, but it does not name alternatives or provide selection criteria.

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 important edge-case behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This gives useful insight into how the tool processes messy data, though it does not mention error handling or permissions.

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 essential information without redundancy. It lists the statistics, names the dataset, and notes data cleaning behavior, making it efficient 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 description lists the exact statistics returned, so the output content is clear. It also covers handling of formatted numbers and non-numeric rows. However, it does not describe the output format (e.g., JSON structure) or potential error conditions like missing columns.

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 no schema description (0% coverage). The description implies it refers to a column in the TakeoffDeck dataset, but it does not specify whether the column must exist, the exact string format, or whether the column is expected to be numeric (it says 'numeric column' but does not enforce it).

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 states that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column and names the specific dataset. This clearly distinguishes it from other dataset tools by focusing on statistical aggregation.

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 guidance on when to choose this tool over alternatives such as dataset_columns or dataset_row. It gives no selection criteria or context about which scenarios call for summary statistics versus other 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?

With no annotations, the description carries the full burden. It mentions ranking by a numeric column but does not disclose the limit parameter's role, default ordering, whether the operation is read-only, or the output format. The phrase 'highest (or lowest) rows' hints at sorting but omits key behavioral details.

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 the core action front-loaded. The appended phrase 'which is the most/least X' is slightly redundant but not harmful. It is appropriately sized.

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 read tool, the description lacks essential details such as how 'limit' affects the result and what the output looks like (full rows? subset of columns?). Without an output schema, the agent must infer the return structure. The description does not mention default ordering or error cases, making it incomplete.

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 adds that the column must be numeric, which is helpful, but it does not explain the 'limit' parameter (number of rows to return) or the default behavior of 'ascending'. With only 33% schema coverage, the description should compensate for the undocumented 'column' and 'limit' parameters, but only partially does.

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, identifying the resource (TakeoffDeck dataset) and the action (return highest/lowest rows). It is distinct from sibling tools like dataset_search, dataset_stats, etc., which serve 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 implies a use case ('which is the most/least X') but does not explicitly mention when to choose this over alternatives like dataset_search or dataset_stats. There is no guidance on when not to use it or what conditions make it appropriate.

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