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

Without annotations, the description carries the full burden of disclosing behavior. It lists the returned information (columns, numeric flags, row count, provenance banner) and implies a read-only inspection, but it does not explicitly state that the tool has no side effects. This is a minor omission but not misleading.

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 conveys all necessary information without redundancy. It is well-structured for quick comprehension.

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 this is a schema-inspection tool with no parameters, the description provides all necessary context: what it returns, what dataset it applies to, and when to call it. No additional details are needed for an agent 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?

The tool has no parameters, and the description does not need to explain any parameter semantics. Since the schema confirms zero parameters, the description is perfectly adequate in this regard.

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 the columns, numeric flags, row count, and provenance banner of the Longtailo dataset. It also explicitly directs the agent to call this first to learn the schema, 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 description includes a direct usage instruction: 'Call this first to learn the schema.' This tells the agent exactly when to use this tool, which is sufficient given that it has no parameters.

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 only mentions that rows are returned 'in the order given', which is a useful ordering detail, but it omits other critical behaviors such as whether the operation is read-only, what the response format looks like, or how edge cases (e.g., no matching rows) are handled. This is insufficient for a tool with no annotation support.

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 conveys the core behavior and use case without any fluff. The key constraint (order given) is stated early, making it 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?

Given the tool has no output schema and no annotations, the description should clarify what the agent can expect. It does not mention the response format, whether all columns are returned, or what happens with empty results. It also lacks any performance or error-handling notes. For a simple two-parameter tool, this is still a notable gap in 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 implicitly explains both parameters: 'column' is the column to filter on and 'values' are the values to match, and it specifies that the order of 'values' determines the output order. However, schema coverage is 0%, so the description must compensate fully; it provides the core semantics but lacks details like exact matching rules, case sensitivity, or whether the column must be a string.

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 Longtailo dataset filtered by a column matching any of the given values, preserving the order of values. It also specifies the intended use case ('X vs Y' questions), making it easy to distinguish from sibling tools like dataset_search 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 phrase 'for "X vs Y" questions' gives a clear context for when to use this tool, implying it is for comparing specific values rather than general searching or single-row retrieval. However, it does not explicitly name alternatives or state when not to use it, so it misses full exclusion 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It states what data is returned but does not mention side effects, errors, or authentication requirements. For a simple metadata lookup with zero parameters, the implied read-only behavior is reasonably clear, but not fully detailed.

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 two sentences with no redundancy. The substantive content is front-loaded, and the practical purpose is stated in a concise second sentence.

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 metadata tool with no output schema, the description adequately lists the returned fields (source, date, licence, citation) and explains why to use it. It omits details about output formatting, but that is a minor gap for such a straightforward 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 covers 100%, so no parameter explanation is needed. The description appropriately focuses on the returned fields rather than arguments, matching the baseline for parameterless tools.

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 the tool as returning provenance metadata—source, computed date, licence, and citation—for the Longtailo dataset. This distinguishes it from the other dataset_* sibling tools, though it lacks an explicit verb like 'returns' or 'retrieves'.

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, specific use case for when to invoke the tool. It does not explicitly exclude alternatives, but the sibling tools are plainly unrelated to provenance or citation.

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 behavioral disclosure burden and does add one useful non-obvious behavior: matching is exact but case-insensitive. It does not cover result cardinality, ordering, limits, or error behavior when no row matches, which are relevant for a lookup 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 with no filler, and it front-loads the key qualifiers: exact match and case-insensitivity. It is slightly awkward as a noun phrase rather than an actionable statement, but it is appropriately compact.

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 lookup, the core semantics are present. However, with no output schema and no annotations, it does not state whether the response is a single row or an array of all matches, nor what happens on zero or multiple matches. This is a minimum-viable description rather than a complete one.

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 explains the roles of column (the field to match on) and value (the case-insensitive exact value), but mostly at the level already implied by the parameter names; there is no detail on format, escaping, or behavior if the column does not exist.

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 the resource (rows of the Longtailo dataset) and the operation (match rows where a column equals a value exactly, case-insensitive). It lacks an explicit imperative verb like 'returns' or 'gets', but the title supplies that, and 'exactly' distinguishes it from fuzzy lookup 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 'exactly' and 'case-insensitive' wording implies use when an exact key is known, but there is no explicit when-to-use guidance or mention of alternatives. In particular, dataset_search is a sibling but is never referenced, so the agent must infer the boundary between exact row lookup and search.

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. The description implies a read-only search operation but does not explicitly state that it has no side effects. This is a minor gap because search operations are typically expected to be non-mutating.

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 unnecessary details. It communicates the essential functionality and constraints 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?

The description provides enough context to understand what the tool does, including the dataset name, search behavior, case-insensitivity, and limit. It does not specify the output format, but that is not essential for basic usage.

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 as 'text to look for in any cell', and the tool description reinforces it. The 'limit' parameter is described via 'up to 50' in the description, though the schema lacks a description for it. Overall, parameters are adequately explained.

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 Longtailo dataset matching a case-insensitive query, with an optional limit. This is specific and distinguishes it from sibling tools like dataset_row or dataset_columns.

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 say when to use this tool over alternatives, but the search-centric purpose is self-evident. It lacks explicit guidance on choosing between dataset_search and dataset_top or dataset_row, but it is not misleading.

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 how non-numeric rows are treated (excluded and counted) and mentions handling of commas/currency, giving some insight into edge cases. However, it does not mention potential errors, limitations, or whether the tool has any side effects, which leaves some behavioral aspects unspecified.

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 without extraneous details. It is well-structured and front-loads the core purpose, making it easy to read and understand quickly.

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 self-contained for a simple tool with one parameter and no output schema. It names the dataset and lists the returned statistics, providing enough context for a user to know what to expect without needing additional documentation.

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', is described as being a numeric column of the dataset, which adds meaningful context beyond the schema's generic 'string' type. This helps the user understand what kind of value to provide, though it could be more explicit about column naming conventions.

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Longtailo dataset. The resource and operation are specific and unambiguous, making it easy to distinguish from other dataset 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 explains that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which guides the user on data preprocessing expectations. While it does not explicitly compare to sibling tools, the purpose is clear enough for a user to infer when this tool is appropriate.

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 says highest/lowest by numeric column, but does not state whether the operation is read-only, how ties are handled, what happens with non-numeric values, what the default limit is, or what the returned row shape looks like.

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, front-loaded sentence with no filler. It loses one point because it partly restates the title and uses a somewhat decorative em-dash phrase rather than adding fully independent behavioral detail.

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 three-parameter tool with no output schema and no annotations, the description plus schema are minimally adequate for an agent to select and invoke the tool. Missing context includes defaults, handling of invalid columns, and expected return format, which would improve call correctness.

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%, so the description must compensate. It adds meaning for 'column' by requiring a numeric column and explains 'ascending' via highest/lowest ordering, but it offers no explanation of 'limit', whose semantics are left to the schema's bounds.

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, resource, and scope: return the highest or lowest rows of the dataset by a numeric column. The clarifying phrase 'which is the most/least X' makes the intent unmistakable and distinguishes it from sibling tools like dataset_row 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 implies the use case: answer top/bottom numeric ranking questions. However, it does not explicitly mention when to prefer this tool over alternatives such as dataset_search or dataset_stats, nor does it state any exclusions.

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