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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 full burden of behavioral disclosure. It states the tool's return content and the fact that it is a schema-learning step, but does not explicitly mention read-only behavior, side effects, or any caveats. For a metadata tool, the lack of side-effect disclosure 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?

A single, dense sentence that packs in all essential information: what it returns, the dataset name, and a usage directive. No wasted words, and the key purpose is front-loaded.

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, no-output-schema metadata tool, the description provides sufficient context: what information is returned and when to use it. It does not detail the exact format of the output, but that is not critical for such a simple retrieval 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?

The tool has zero parameters and the schema is empty (100% coverage). The description does not need to explain parameters, and the baseline for 0-param tools is 4. No additional parameter semantics are required.

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's purpose: it returns columns, numeric flags, row count, and a provenance banner for the Mashzo dataset. It also instructs to call it first to learn the schema, which distinguishes it as an entry point among siblings.

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 explicitly says 'Call this first to learn the schema,' giving a clear when-to-use directive. It does not explicitly mention alternatives or when not to use, but the priority instruction is strong enough to guide 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, the description carries the full burden of behavioral disclosure. It does reveal the ordering behavior ('in the order given') and the filtering logic, but it omits details such as whether all columns are returned, what happens with no matches, or any side effects. For a read-only query tool, this is partial but not severe.

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, front-loaded sentence that conveys the core behavior and use case with zero filler. Every clause earns its place, and the most important constraint (row filtering and ordering) appears first.

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 (2 parameters, no nested objects, no output schema), so the description is mostly adequate. However, it does not state the return format, whether all columns are included, or any failure behavior. Given the simplicity and the absence of annotations, this is a minor gap but not crippling.

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 0%, so the description must compensate. It explains that 'column' is the field to filter on and 'values' are the matching values, and notes that rows follow the given value order. This adds meaning beyond the bare schema names, but it doesn't clarify data types, value format, or edge cases like empty strings, 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 states a specific verb ('compare') and resource ('rows of the Mashzo dataset'), with precise scoping: rows whose column matches any of the given values, in the order given. It also names the use case ('X vs Y questions'), which clearly distinguishes it from siblings like dataset_search 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?

The phrase 'for X vs Y questions' implies a comparison scenario, which helps an agent decide when to use it. However, it does not explicitly contrast with alternatives like dataset_search or dataset_row, nor state when not to use it. The guidance is implied rather than 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 provided, the description carries the full burden of behavioral disclosure. It clearly lists the information returned (source, date, licence, citation), implying a read-only, informational operation. It doesn't mention any side effects or permissions, which is appropriate for a metadata retrieval tool. The description is transparent enough for an agent to know what to expect.

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 lists the content, and the second explains the use case. Every word earns its place, and the purpose is front-loaded.

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 explains what the tool returns and why to use it. It could mention the format of the citation or whether it returns a single object, but that's not essential for an agent to call it correctly. The description is sufficiently complete for 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 tool has zero parameters, so the schema is trivially complete. The description doesn't need to explain any parameter semantics because none exist. The baseline of 4 is appropriate given the absence of parameters.

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 exactly what the tool provides: the source, computed date, licence, and citation for the Mashzo dataset. It uses specific nouns and clearly differentiates from sibling tools like dataset_columns, dataset_search, and dataset_stats by focusing on provenance rather than data manipulation.

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 clear use case. While it doesn't explicitly state when not to use it or mention alternatives, the purpose is so specific that the context is obvious. It would benefit from an explicit 'use this when you need citation information' but the implicit guidance is strong.

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 burden. It discloses the case-insensitive exact-match behavior, which is a key trait. However, it does not state whether the tool returns a single row or multiple rows (the title says 'a row', the description says 'rows'), nor what happens on no match, and it omits any note about being read-only or any side effects. Partial behavioral disclosure.

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 with no redundancy, and the core matching behavior is front-loaded. It is appropriately concise for the simplicity of the tool, though it could be slightly more informative without becoming verbose.

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 2-parameter tool with no output schema and no annotations, the description should cover usage context, return behavior, and edge cases. It lacks any mention of when to use it, what the output looks like, or how it differs from dataset_search. The ambiguity between 'a row' and 'rows' adds to the incompleteness. The description is adequate only for the most basic call.

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 only clarifies that 'column' refers to a dataset column and 'value' is the exact value to match, which is already inferable from parameter names. It does not explain allowed column names, value formatting, case-sensitivity of column names, or any constraints beyond schema's minLength. Minimal added meaning.

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 (Mashzo dataset rows) and the matching criterion (column equals value exactly, case-insensitive). The title provides a clearer verb ('Look a row up'), but the description itself is declarative. It does not explicitly distinguish from sibling tools like dataset_search, but the 'exactly' qualifier implies a contrast with fuzzy search. Clear purpose, but no sibling 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?

No guidance on when to use this tool versus alternatives. It does not mention that this is for exact lookups and that dataset_search might be for broader queries, nor any conditions or exclusions. The agent is left to infer usage from the 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 provided, the description carries the full burden. It discloses case-insensitive matching, substring containment (via 'contain'), and a result cap of 50. However, it does not mention result ordering, pagination, whether all columns are searched (though 'cells' implies all), or the exact return format. These gaps keep it from being fully transparent, though core behaviors are covered.

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 (rows containing query) and includes key modifiers (case-insensitive, up to 50). There is zero waste; every element adds value. It is well-structured and immediately scannable.

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 is adequate for a simple search tool but missing some contextual details: it does not specify the return format (full row objects vs IDs), result ordering, or whether there is a default limit if the limit parameter is omitted. Given the lack of an output schema and annotations, these details would help an agent invoke it correctly, but the core functionality is clear enough for basic use.

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 in the schema, while limit has only type and constraints. The description adds no parameter-specific meaning beyond the schema, but the schema itself adequately explains limit via min/max. The description's 'up to 50' reinforces the limit but doesn't clarify default behavior. Since coverage is moderate, a 3 is fair.

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 of a specific dataset whose cells contain the query, with case-insensitivity and a limit of 50. It uses a specific verb (implicitly 'search') and resource (Mashzo dataset), and it is distinct from siblings like dataset_row (single row) or dataset_stats (statistics). However, it does not explicitly name sibling alternatives to differentiate, so a 4 is appropriate.

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: if you need to find rows containing specific text across any cell, use this tool. It does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. The context is clear but lacks explicit exclusions or comparisons 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?

With no annotations, the description carries the full burden and does a good job: it discloses that grouping commas and currency symbols are handled, and that non-numeric rows are excluded and counted. This goes beyond a generic 'compute stats' statement, though it leaves some ambiguity about how the excluded count is represented.

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 filler: the main promised outputs are listed first, followed by two important behavioral caveats. This is appropriately sized and front-loaded.

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 low complexity, single required parameter, and no output schema, the description is largely complete for selecting and invoking the tool. It covers the computed values, numeric parsing, and exclusion behavior. The main gap is the lack of explicit return-format details, but the listed statistics make the likely output predictable.

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 zero description coverage. The description adds meaning by specifying that the column must be a numeric column of the Mashzo dataset, but it does not provide example values, formatting requirements, or behavior for invalid/unmatched column 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 a concrete, specific operation: computing count, min, max, mean, median, and sum for a numeric column of the Mashzo dataset. It clearly distinguishes the tool from siblings like dataset_row, dataset_search, and dataset_top by focusing on statistical 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 implies usage for numeric-column summary statistics but does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions. A clear context is present, but the agent is left to infer the choice versus 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the ascending option implicitly but does not disclose the limit cap, tie handling, behavior with non-numeric columns, or output format. Minimal behavioral context beyond the core action.

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, succinct sentence that front-loads the purpose. It contains no unnecessary words or repetition, though it could include more detail without becoming verbose.

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 specify return values, error conditions, the maximum limit (50), or handling of edge cases. An agent would need to infer or test to use 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?

Schema description coverage is only 33%, and the description adds little beyond that. It hints at the 'ascending' parameter with 'highest (or lowest)' but does not explain the 'column' requirement (numeric) or the 'limit' parameter's cap and default. It fails to compensate for the low 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 states the tool ranks rows by a numeric column and returns the highest or lowest, which clearly identifies its function. It distinguishes from siblings like dataset_search and dataset_stats by focusing on top/bottom rows, though it doesn't explicitly name alternatives.

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. The description implies a use case (finding most/least X) but does not mention exclusions or when other dataset tools would be more appropriate.

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