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

The description enumerates the exact output contents (columns, numeric indicators, row count, provenance banner), which sets clear expectations. It does not explicitly state that the operation is read-only or non-destructive, but the nature of returning schema metadata makes that implicit. Slight deduction for not mentioning potential errors or side effects, though none are expected.

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 extremely concise—two sentences—while conveying the purpose, the output contents, and usage priority. Every word adds value, with no redundancy or 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?

Given there is no output schema, the description fully specifies what the tool returns and when to use it. It is sufficiently complete for an agent to know the tool's role within the sibling toolset and act accordingly.

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 the input schema is fully covered by an empty properties object. The description adds no parameter details because none exist. Per the baseline for high schema coverage, a score of 3 is appropriate.

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 columns, numeric flags, row count, and a provenance banner for the Yieldzo dataset. It also explicitly identifies this as the first call to learn the schema, distinguishing it from sibling tools like dataset_compare 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 description gives direct usage guidance: 'Call this first to learn the schema.' This tells the agent exactly when to invoke this tool, making the usage context unambiguous.

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, and the description does not disclose side effects, output structure, or error behavior. As a read-only operation, missing annotation lowers transparency; the description does not compensate.

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 conveys the core functionality without unnecessary verbosity. It lacks structural elements like examples but remains efficient.

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 sibling tools, the purpose is fairly clear, and the description adds the ordering detail. However, it does not specify the output format or edge cases (e.g., no matches), so completeness is not full.

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 that 'column' is the field to filter on and 'values' are the matching values, with ordering respected. This adds meaning beyond the raw schema, though the exact matching semantics (e.g., exact vs substring) are not detailed.

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 filters rows based on a column matching any of the given values, and mentions the use case for 'X vs Y' questions. However, it could be more explicit about the comparison semantics and the exact return format.

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 explicit guidance on when to use this tool instead of alternatives like dataset_search or dataset_row. The phrase 'for X vs Y questions' offers some context but does not differentiate from 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?

There are no annotations, so the description carries the full burden. It implies a read-only metadata lookup with 'Read this to attribute a figure correctly', but it does not explicitly state that there are no side effects, or describe error or availability 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, compact sentence that conveys the content and use case without unnecessary detail or repetition.

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 sufficient for a simple metadata retrieval tool: it names the exact pieces of information returned and the intended purpose. It does not describe an output format, but no output schema exists and the scope is narrow.

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 the baseline is 4. The description does not need to explain parameter meanings because there are none.

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 provenance metadata (source, date computed, licence, citation) for the Yieldzo dataset and that it is meant for attribution. It lacks a strong imperative verb, but the purpose is unmistakable.

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 use case ('attribute a figure correctly') but does not explicitly contrast with sibling dataset tools or mention when this tool should be preferred over alternatives. The guidance is implicit 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 discloses the exact-match and case-insensitive behavior, which is useful, but does not mention return format, handling of multiple matches, or error conditions (e.g., missing column). The description is not misleading, but it is minimal and leaves several 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 front-loads the core action and matching rule. It avoids redundancy and contains no filler. Every word contributes to the meaning, 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?

For a simple two-parameter tool, the description explains the matching rule but omits essential usage context: it does not indicate that 'column' should be an existing column name (and how to discover that), whether all matching rows are returned, or what the output looks like. With no output schema and no annotations, these gaps leave an agent uncertain about invocation details and result interpretation.

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 description coverage is 0%, so the description must compensate for parameter semantics. It mentions 'a column equals a value' which maps to the 'column' and 'value' parameters, but does not clarify what constitutes a valid column name, how to obtain it, or the expected format of the value (e.g., string vs numeric). The description adds little beyond the schema's type and minLength constraints, leaving agents to infer parameter 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 clearly states that the tool returns rows where a column equals a value exactly, and specifies case-insensitivity. The title 'Look a row up by an exact key' reinforces the purpose and differentiates it from dataset_search, though it does not explicitly name sibling tools. This is clear enough for an agent to grasp the core function.

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 use for exact-match lookups ('equals a value exactly'), which distinguishes it from a fuzzy search like dataset_search. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention prerequisites such as knowing valid column names or how to obtain them. Guidance is implied but not actionable beyond the matching condition.

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 state case-insensitive matching and the 50-row cap, which are useful behavioral traits. However, it does not disclose whether the operation is read-only, what happens when no rows match, or the exact structure of returned rows. It adds some context but leaves significant gaps.

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 of 11 words, highly efficient and front-loaded with the resource and core behavior. Every word adds value, and there is no redundant or filler content.

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 matching logic and result cap. However, it omits details about the output format (e.g., array of rows, structure of each row) and any sorting or pagination behavior. Given the lack of an output schema, these details would help an agent correctly interpret the response. It is adequate but not 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?

Schema description coverage is only 50% (query has a description, limit does not). The description partially compensates by referencing the query ('contain the query') and the limit ('up to 50'), but does not fully clarify that limit is an optional cap on result count. It adds some meaning beyond the schema but not enough to fully compensate for the undocumented limit 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 states the specific action (search the dataset for rows) with a clear resource (Yieldzo dataset) and the matching condition (cells contain query, case-insensitive). This distinguishes it from siblings like dataset_row (fetch specific row) or dataset_columns (list columns) without needing to inspect their schemas.

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 text search but does not explicitly state when to use it over alternatives or when not to use it. No mention of exclusions or alternative tools, leaving the agent to infer context from sibling names. The core purpose is clear, but guidance on selection is absent.

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 reveals that grouping commas and currency are handled, non-numeric rows are excluded and counted, and it lists the returned statistics. This goes beyond a simple 'compute stats' and provides meaningful context. However, it does not disclose error handling for invalid columns or empty datasets, which prevents a 5.

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 front-loads the list of statistics and then provides two parenthetical clarifications. It is efficient and avoids redundancy, though the parenthetical about number handling is somewhat dense. Overall, it is appropriately sized and well-structured for its purpose.

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's simplicity (one parameter, no output schema, no annotations), the description is largely complete. It explains what stats are returned, how numeric formatting is normalized, and how non-numeric rows are treated. Missing details include potential error messages or behavior if the column does not exist or contains no numeric values, but these are minor for a simple stats tool. The absence of an output schema is partially mitigated by listing the returned stats.

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 documented only as a string in the schema with no description (0% coverage). The tool description adds crucial meaning by specifying it must be a numeric column and that formatting (commas, currency) is handled. This compensates for the schema's silence and helps the agent understand what value to provide, though it could be more explicit about accepted formats.

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 Yieldzo dataset. The verb 'compute' is implied, the resource is named, and the specific stats are listed. This distinguishes it from siblings like dataset_row (returns rows) and dataset_search (searches) without ambiguity.

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 statistics but does not explicitly state when to prefer this over alternatives. It does not mention exclusions or conditions like 'use this instead of dataset_top when you need aggregate measures.' However, the clear purpose partially compensates, so a 3 is appropriate for lacking explicit routing 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 of behavioral disclosure. It does not state that this is a read-only operation, what happens if the column is non-numeric, whether ties are broken, or if there are pagination/limits beyond the schema's maximum. For a ranking tool, the absence of such details leaves the agent uncertain about edge cases.

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, tight sentence that front-loads the core action and includes a clarifying example in quotes. Every word contributes value, and there is no redundant phrasing. It is concise without sacrificing 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?

For a relatively simple tool with three parameters and no output schema, the description provides the essential purpose but omits important behavioral context such as error handling, non-numeric column behavior, and sorting stability. Given that annotations are absent, the description should carry more weight to fully equip the agent, but it covers the main intent adequately.

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 description coverage is 33%, with only the 'ascending' parameter described. The description adds a key semantic that 'column' must be numeric, which goes beyond the schema's generic string type. However, it does not explain 'limit' beyond the schema's min/max, nor does it clarify the default behavior for 'ascending' (though the schema says default highest first). It adds some meaning but not comprehensive parameter guidance.

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 clear verb and resource: it ranks rows of the Yieldzo dataset by a numeric column, supporting both highest and lowest. The phrase 'which is the most/least X' gives a concrete use case, distinguishing it from siblings like dataset_stats (which likely aggregates) or dataset_search (which likely filters). It does not explicitly name an alternative, but the purpose is 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 implies when to use the tool (when you want top or bottom rows by a metric) but provides no explicit guidance on when not to use it or which sibling to prefer. It does not mention that dataset_stats might be better for aggregate summaries, or that dataset_search is for filtering. Without any exclusions or alternatives, the agent must infer the decision context.

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