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

With no annotations provided, the description carries the burden, and it discloses what the call returns: columns, numeric status, row count, and provenance banner. It implies a read-only schema-introspection behavior. It could more explicitly state that no dataset transformation occurs, but for a zero-parameter informational tool this 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?

A single sentence that front-loads the concrete outputs and places the actionable guidance ('Call this first') at the end. Every phrase earns its place; there is no 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 zero-parameter tool with no output schema and no annotations, the description is complete: it enumerates the return contents and gives workflow ordering. An agent has everything it needs to decide to call this tool first.

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 schema is an empty object with 100% coverage, and the description correctly avoids inventing 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 uses specific verbs and resources: it tells the agent what this tool returns (columns, numeric flags, row count, provenance banner) and frames it as the schema-learning entry point. It is clear, though it does not explicitly name or differentiate from sibling tools.

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 context: 'Call this first to learn the schema,' which tells an agent when this tool should be used in the workflow. It does not mention exclusions or alternatives, but the placement guidance is genuinely useful.

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?

There are no annotations provided, so the description carries the full burden. The description only states the functional behavior and does not disclose any side effects, permissions, or read-only guarantees. It is not explicit about whether the tool modifies data or requires specific access, which limits 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 a single, concise sentence that packs all essential information without redundancy. It is well-structured and does not include any extraneous details.

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 provides sufficient context for a simple tool: it mentions the dataset name, the matching logic, and the ordering behavior. It does not specify the output format (e.g., whether all columns are returned), but this is a minor omission given the simplicity of the tool and the clarity of the stated behavior.

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 effectively explains both parameters: 'column' as the field to match against and 'values' as the list of allowed values. It also clarifies that the output order follows the order of the 'values' array. This fully compensates for the lack of per-parameter descriptions in the schema.

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 function: it retrieves rows from the Rebadgo dataset where a specified column matches any of the given values, preserving the order of the values. It also provides a specific use case ('X vs Y' questions), making the purpose unambiguous and distinct from 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 gives a partial usage hint by mentioning 'X vs Y' questions, implying it is suited for comparing specific values. However, it does not explicitly contrast with sibling tools like dataset_search or dataset_row, leaving some ambiguity about when to prefer this tool over 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?

The description discloses what information is returned (source, date computed, licence, citation) and frames the tool as a read-only informational resource. It does not detail return formatting or error behavior, but for a simple metadata retrieval tool this 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 concise, using two short sentences to convey purpose and use. No unnecessary words or redundant details are present.

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 parameterless provenance metadata tool, the description provides enough context: it names the exact fields returned and the practical reason to invoke the tool. No output schema is provided, but the listed content is sufficiently complete for the intended 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?

The tool has no parameters, so there is no parameter schema to elaborate on. The description fully covers the tool's behavior without needing to explain parameter meanings.

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: providing provenance information for the Rebadgo dataset, including source, date computed, licence, and citation. It also gives an actionable reason to use it ('Read this to attribute a figure correctly'), distinguishing it from the dataset-related sibling tools.

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 the tool—when provenance or citation details are needed for attribution. It does not explicitly contrast with sibling tools, but the intended use case is clear enough from the phrasing.

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 exist, so the description carries the full burden. It usefully discloses exact and case-insensitive matching, but omits other behavioral details such as whether multiple rows can be returned, what happens on no match, or how column names are handled.

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 wasted words. The key differentiator (exact, case-insensitive) is included. It could be more direct by starting with a verb like 'Returns', but overall it is concise and well-structured.

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 read tool with no output schema and no annotations, the description covers the basic operation. However, it remains incomplete because it does not explicitly state that all matching rows are returned, and it does not clarify the boundary with the sibling dataset_search tool, leaving meaningful 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?

Schema description coverage is 0%, so the description must compensate. It does explain the relationship between the two parameters: 'column' names the column and 'value' is the value to match exactly. However, it lacks details about valid column names, value format constraints, and whether the column name matching is itself case-sensitive.

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 Rebadgo dataset where a column equals a value exactly, with case-insensitive matching. This communicates the core function clearly, and the exact-match emphasis helps distinguish it from fuzzy search tools like dataset_search, though it does not 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 its siblings such as dataset_search or dataset_top. The description only states the matching semantics, leaving the agent to infer the appropriate use case.

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 correctly states that it returns rows, is case-insensitive, and can cap results. It does not mention side effects or privileged operations, but as a search it is implicitly read-only. This is sufficient 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 one concise sentence that covers the core functionality, case-sensitivity, and result cap. No filler or redundant information. It is well-structured and immediately understandable.

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 gives enough context for an agent to invoke the search correctly, including the matching rule and output limit. There is no output schema, but the description implies the output is rows from the dataset. For a search tool, this is reasonably complete, though specifying the exact output format or data fields would enhance 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?

The description explains both parameters: 'query' is the text to search for, and 'limit' is implied by 'up to 50'. However, the schema only provides a description for 'query', leaving 'limit' without a dedicated schema description. The tool description partially compensates, but a more explicit parameter breakdown would improve clarity.

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 function: it returns rows from a dataset that match a query, with case-insensitive matching and an optional limit. This unambiguously distinguishes it from siblings like dataset_row (which likely fetches a specific row) and dataset_stats (which computes 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 explains the core behavior (search rows for query text) and the limit. While it does not explicitly contrast with alternatives, the sibling names are self-explanatory and the search semantics are clear enough for an agent to decide when to use 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is valuable. It does not describe error behavior or output format, but the listed statistics implicitly define the return. This is solid coverage for a read-only stats 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?

The description is a single sentence that packs all essential information: the computed statistics, the target dataset, and edge-case handling. There is no redundancy or filler, and the most important information 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 simple tool with one parameter and no output schema, the description covers the key aspects: what it computes, the handling of formatting and non-numeric values. It omits potential error cases (e.g., column not found) and does not specify return structure, but these are minor for this scope. Overall, it is sufficiently complete for an agent to use 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 input schema only declares a required string 'column' with no description, so schema coverage is 0%. The description adds that the column must be numeric and implies it is a column identifier, but it does not specify the exact format (e.g., name vs. index) or provide validation hints. It partially compensates for the schema gap but could be more explicit.

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 enumerates the exact statistical operations (count, min, max, mean, median, sum) and the target resource (numeric column of the Rebadgo dataset). This unambiguously distinguishes it from sibling tools like dataset_top or dataset_row, 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 provides clear context: it's for numeric columns and specifies how data is handled (commas, currency, non-numeric rows). It does not explicitly mention alternatives or when not to use it, but the context is sufficient for an agent to infer its scope. Missing an explicit when-not clause 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?

With no annotations, the description carries the full burden of behavioral disclosure. It does state the core read-oriented behavior: returning rows ranked by a numeric column. However, it does not disclose result shape, tie-handling, default ordering behavior beyond the basic highest/lowest idea, or any additional constraints.

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 filler. It is slightly redundant with the title, but it is front-loaded 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 tool with no annotations and no output schema, the description is too thin. It leaves out the meaning of `limit`, the need for a numeric column is only implied, and there is no mention of what the returned data looks like or how it differs from sibling tools.

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 low at 33%, and the description compensates only partially. It clarifies that `column` should be numeric and that ascending relates to highest/lowest, but it never explains the `limit` parameter or how many rows will be returned. The name `dataset_top` implies a limit, but the description does not state 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 identifies a clear, specific operation: retrieving the highest or lowest rows of the Rebadgo dataset by a numeric column. It conveys the ranking intent precisely and differentiates the tool from siblings like dataset_row, dataset_stats, and 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 description gives no guidance about when to use this tool versus alternatives. It does not mention which sibling tools would be better for different needs, such as when to use dataset_stats instead of dataset_top.

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