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

With no annotations, the description carries the behavioral burden. It discloses what data is returned (columns, numeric flags, row count, provenance banner) but does not state side effects, output format, or whether it is purely read-only. The provided detail is useful but not comprehensive.

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 waste. The core deliverables are listed up front, and the usage hint is a separate actionable sentence. 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 zero-parameter metadata tool, the description covers what it returns and when to call it. There is no output schema, but the listed items (columns, numeric flags, row count, provenance banner) give enough context for an agent to invoke it appropriately.

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 schema already fully covers parameter semantics. The description does not need to add parameter details, and the baseline of 4 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 identifies what the tool returns: columns, numeric flags, row count, and provenance banner for the MowRouteWorks dataset. It stops short of a strong verb, but the resource and output are unambiguous and it is distinguishable from siblings like dataset_stats or dataset_top.

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 says 'Call this first to learn the schema,' which gives clear timing guidance relative to other dataset tools. It does not name alternatives or exclusions, so it misses the top score, but the usage context 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?

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal key behaviors: rows are filtered by 'any of the given values' and returned 'in the order given.' However, it does not disclose the return format, whether full rows are returned, case-sensitivity, behavior when no values match, or confirm this is a read-only operation.

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 sentence and includes only relevant information: dataset, selection logic, ordering, and intended use case. It is concise but structurally awkward, leading with a noun phrase and appending the use case via an em-dash, which slightly reduces clarity.

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 there is no output schema and no annotations, the description should clarify what the result looks like. It explains when to use the tool and what rows are selected, but not how the comparison is presented (despite the title mentioning 'side by side') nor how edge cases like no matches or invalid column names are handled. This is adequate for tool selection but incomplete for fully interpreting 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?

Schema description coverage is 0%, so the description must explain the parameters itself. It does: 'column' is the dataset column to match against, and 'values' are the candidate values any of which may match. This directly ties both required parameters to the tool's behavior, though it does not elaborate on value formatting or matching precision.

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 the resource (MowRouteWorks dataset rows) and the selection logic (column matches any of the given values, preserving order), which clearly conveys what the tool does. It lacks an explicit verb like 'returns' or 'retrieves', and the phrasing is a noun phrase rather than a complete command, so it is clear but not maximally direct.

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 explicit guidance on when to use this tool: when comparing specific entities represented as row values. It does not name alternative sibling tools or give exclusion criteria, but the use case is clear enough to route an agent toward this tool.

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 clearly describes the informational output (source, date, licence, citation) and the intent to read provenance, which is sufficient for a zero-parameter read-only 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 short sentences deliver the tool's purpose and content with no filler. The title reinforces the same message without redundant repetition in the description.

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 provenance lookup with no output schema, the description is complete: it lists exactly what information will be returned and when to use it. Nothing essential is missing.

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, so the baseline is 4. The description needs to provide no parameter meanings; it fully covers what an agent needs to understand before invoking the tool.

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 a specific resource (MowRouteWorks dataset), a clear function (provide provenance), and the concrete content (source, date computed, licence, citation). It is distinct from all sibling tools, which address columns, comparison, rows, search, stats, and top-level summaries.

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 explicit context: use this when attributing a figure correctly. It does not mention when not to use it or name alternative tools, but the zero-parameter, resource-specific nature of the tool makes the intended use 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?

With no annotations, the description must carry the full burden of behavioral disclosure. It mentions case-insensitive matching, which is helpful, but omits return format, pagination, limits, and behavior when no matches are found.

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 action. Every word contributes value, with no fluff 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?

For a tool with no output schema and no annotations, the description does not explain the return structure, pagination, or error handling. An agent lacks essential information about what the response will contain or how to handle edge cases.

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 'column' is a column name and 'value' is the value to match exactly, adding meaning beyond the schema's basic string type. However, it does not specify constraints like column existence or value formatting, leaving gaps given 0% schema coverage.

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 retrieves rows from the MowRouteWorks dataset where a column equals a value exactly, with case-insensitivity. It effectively communicates the core function but does not explicitly differentiate from siblings 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 given on when to use this tool versus alternatives such as dataset_search. The description implies exact matching but does not state when this tool is preferred over 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does mention case-insensitivity and a limit of 50, which are useful. However, it does not explain return format, ordering, pagination behavior, or error handling when no matches are found. These gaps are notable but the core behavior is disclosed.

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, tightly worded sentence that front-loads the core action and constraints. There is zero redundancy or filler, and it conveys all essential information efficiently.

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 two parameters and no output schema, the description covers the essential behavior (matching rows, limit) but omits details like result ordering, pagination, and what happens with zero matches. It is adequate for basic use but not exhaustive, which is expected given the tool's simplicity.

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 a description for 'query' (text to look for in any cell) but not for 'limit'. The description adds the meaning of the limit (up to 50) and reinforces case-insensitivity. Since schema coverage is only 50%, the description partially compensates by clarifying the limit, but does not add further detail about either parameter.

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 action: returning rows from a specific dataset (MowRouteWorks) where any cell contains the query, with case-insensitivity and a limit of 50. This is specific and distinguishes it from siblings like dataset_row (single row) or dataset_stats (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 description implies when to use it (when you need to find rows containing certain text) but provides no explicit guidance on when not to use it or how it compares to alternatives like dataset_top or dataset_row. The usage context is inferred rather than stated, so it's adequate but not fully explicit.

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 does mention handling of grouping commas and currency, and that non-numeric rows are excluded and counted—useful edge-case behavior. However, it does not describe the output format, error conditions (e.g., all non-numeric values, missing column), or whether the result is a single object. This is partial disclosure, so a 3 is appropriate.

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 packs all key information—statistics, dataset name, formatting handling, and non-numeric exclusion—without any wasted words. It is front-loaded with the primary purpose and then adds behavioral nuances, making it both concise and structured effectively.

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 simple one-parameter tool with no output schema, the description covers the essentials: what stats are computed, how formatting is handled, and how non-numeric rows are treated. It does not explicitly state the return format (e.g., object with fields), but the list of stats implies it. For a tool of this complexity, this is nearly complete, so a 4 is justified.

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 the parameter name 'column' with a type string and minLength. The description adds critical meaning: it specifies that the column is from the MowRouteWorks dataset and must be numeric. It also hints at handling of formatting (commas/currency). This meaningfully compensates for the 0% schema description coverage, earning a 4.

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 ('compute') and resource ('numeric column of the MowRouteWorks dataset'), and lists the exact statistics (count, min, max, mean, median, sum). It clearly distinguishes from sibling tools like dataset_columns (which lists columns) or dataset_row (which fetches rows) by its focus on summary 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 description clearly implies use for obtaining summary statistics on a numeric column, but does not explicitly mention alternatives or exclusions. The context is clear enough—if you need column stats, this is the tool—but it lacks an explicit 'use X instead when...' statement. This earns a 4 for clear context with no exclusions.

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 explains the ranking intent but does not mention default ordering for the absent 'ascending' flag, limits on returned rows, handling of non-numeric columns, or any other runtime 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, focused sentence with no filler. The key ranking behavior is front-loaded, and the illustrative 'most/least X' phrase adds clarity without unnecessary length.

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 covers the core selection logic, but with no annotations and no output schema, an agent is left without explicit information about return shape, row limits, or default ordering behavior. The schema fills some gaps for 'ascending' but not all.

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 low at 33%, so the description should compensate. It adds useful meaning for 'column' by specifying it must be numeric and for 'ascending' by describing lowest vs. highest ordering, but it does not clarify the 'limit' parameter or its default/cap behavior.

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 — returning the highest or lowest rows — and identifies the exact dataset and column type. The clarifying phrase 'which is the most/least X' makes the purpose immediately understandable and distinguishes it from sibling tools like dataset_stats 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?

The use case is implied by the description: rank rows by a numeric column. However, the description does not explicitly state when to prefer this over sibling tools such as dataset_search or dataset_stats, nor does it mention any exclusions or alternatives.

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