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

With no annotations, the description carries the full behavioral disclosure burden. It explains the returned content and the call-first ordering, but does not describe the output format, potential errors, latency, or any side effects. For a zero-parameter metadata tool this is minimally acceptable but not rich.

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 short sentences with no filler. The first sentence front-loads the returned information, and the second gives a concise call-order instruction.

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, parameterless schema-discovery tool, the description tells the agent what it returns and why to call it first. The only gap is that the exact output structure is not specified, but the absence of an output schema makes this a minor omission.

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 an empty input schema, so there are no parameter semantics to clarify. The description correctly establishes that the tool is about the fixed Stagenix dataset context.

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 names the Stagenix dataset as the resource and lists the exact outputs: columns, numeric flags, row count, and provenance banner. It does not explicitly differentiate this tool from siblings like dataset_stats or dataset_provenance, so it stops short of a 5.

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 'Call this first to learn the schema' provides clear, actionable guidance about when to invoke the tool. However, it does not mention alternatives or situations where the tool should not be used.

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 mentions that rows are returned 'in the order given,' which is a useful behavior, but it does not disclose whether the operation is read-only, what the return format looks like, whether all columns are included, or any error handling or performance implications. For a tool with no annotation coverage, this is insufficient.

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 is front-loaded with the core action and includes the essential scoping information. There is no filler or redundancy; every part of the sentence earns its place, and the 'X vs Y' note is an efficient way to convey the intended use.

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 two parameters and no output schema, the description is mostly adequate but leaves some gaps. It does not specify what columns are returned (all vs. subset), nor does it clarify if this is a read-only operation or if it has any limitations. Given the lack of annotations, more detail on return format and usage constraints would improve completeness, but the core function is clear.

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 for parameter meaning. It does explain that 'column' is the field to match against and 'values' are the list of values to match, and that the order of values determines the output order. This adds semantic meaning beyond the raw schema. However, it does not elaborate on constraints like minimum/maximum number of values or the type of column values, though these are already in the schema, so the description adequately clarifies the intent.

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 of the Stagenix dataset where a specified column matches any of the given values, preserving the provided order. It is specific about the verb ('rows whose column is any of the given values') and resource ('Stagenix dataset'), and the mention of 'X vs Y' questions gives a concrete use case. However, it does not explicitly distinguish itself from siblings like dataset_row or dataset_search, leaving some ambiguity about when to prefer this over them.

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 offers a clear contextual hint ('for X vs Y questions') that implies when to use it, but it does not explicitly state when not to use it or mention alternative tools. Sibling tools like dataset_search or dataset_row exist, and the description does not guide the agent to choose this over them for any specific scenario beyond the implied comparison 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?

No annotations are provided, so the description carries the behavioral burden. 'Read this' implies a read-only retrieval, and the listed return fields clarify what the tool exposes. However, it does not explicitly state that there are no side effects, nor does it address access or output details beyond the listed fields.

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?

One sentence covers the tool's content, purpose, and usage context with no filler. The title and description work together efficiently, and every word contributes to understanding what the tool does.

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 retrieval tool with no output schema, the description is fully sufficient. It tells the agent what the tool returns, why to use it, and when it applies. No additional information is needed to invoke 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 input schema has zero parameters, so the baseline is 4. The description correctly adds no parameter-related claims because none exist; it would be inappropriate to penalize for missing parameter documentation.

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 exact resource (Stagenix dataset provenance) and the exact content returned: source, computed date, licence, and citation. It also gives the practical purpose ('attribute a figure correctly'), making the tool's function unmistakable and distinct from the sibling 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?

'Read this to attribute a figure correctly' clearly states when the tool should be used. It does not explicitly mention alternatives or exclusions, but the provenance focus makes it contextually obvious that this is not for data viewing or search 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?

No annotations exist, so the description carries the full behavioral burden. It usefully discloses that matching is exact and case-insensitive and implies multiple rows may be returned, but it does not describe the output shape, ordering, or behavior when no rows match.

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. It conveys the core matching semantics efficiently, though the phrasing is slightly nominal rather than action-oriented.

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 two-parameter lookup the description covers the essential selection semantics. However, the title says 'a row' while the description says 'the rows', leaving ambiguity about result cardinality, and there is no mention of no-match behavior or output format.

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 maps 'column' and 'value' to the two parameters and clarifies that equality is exact and case-insensitive, which is meaningful for such simple string 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 title 'Look a row up by an exact key' and the description clearly express an exact, case-insensitive equality lookup against a dataset column. It is distinguishable from sibling tools like dataset_search or dataset_top, though the description lacks a strong imperative verb.

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 exact match lookups but provides no explicit guidance on when to choose this tool over dataset_search or dataset_stats. There are no stated exclusions, prerequisites, or 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful traits like case-insensitive matching and a 50-row limit, but does not mention return format, ordering, empty-result behavior, or any side effects. These gaps are significant for a tool with no output schema.

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 zero waste. It states the resource, the operation, the matching behavior, and the result cap efficiently. Every word contributes value.

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 search tool with no output schema and siblings, the description is incomplete. It does not specify the return structure (e.g., array of row objects), how to handle results beyond 50, or any pagination/ordering details. It also lacks usage context relative to sibling tools, making it only partially complete for an agent to call 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 coverage is only 50% (query has a description, limit does not). The description adds meaning beyond the schema by clarifying the query matches any cell and that results are capped at 50. This compensates for the missing limit description and enriches query semantics.

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 searches rows of the Stagenix dataset for a query in any cell, with a limit of 50. It distinguishes itself from sibling tools like dataset_row (which likely fetches a specific row) and dataset_top (which returns top rows) by focusing on content-based 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 implies usage (search for rows containing a query) but does not explicitly state when to use this tool over alternatives like dataset_top or dataset_columns. It lacks exclusionary guidance or mention of specific scenarios where this tool is preferred.

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 meaningful detail: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It stops short of describing the exact output shape or edge cases like an all-non-numeric column, but the disclosed parsing behavior is valuable.

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 front-loads the operation and output list, with behavior caveats compactly in parentheses. There is no filler 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 one-parameter read-only summary tool, the description covers the main domain knowledge an agent needs, including numeric parsing and non-numeric handling. The absence of an output schema makes the exact return shape slightly implicit, and 'count' could be read ambiguously, but the listed statistics largely serve as the contract.

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%, but the description compensates by making clear that the single 'column' parameter must be a numeric column and by noting how formatted values (commas, currency) are normalized. It does not specify whether the column identifier is a display name or internal key, but with one parameter this is sufficient.

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 exact operations ('count, min, max, mean, median and sum') and the explicit resource ('a numeric column of the Stagenix dataset'), distinguishing it from sibling operations like row retrieval or column listing.

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 naming summary statistics for a numeric column, but the description gives no explicit guidance on when to prefer this tool over siblings such as dataset_row, dataset_search, or dataset_top, and 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?

With no annotations, the description carries the full behavioral burden. It conveys a read-only ranking behavior and the highest/lowest intent, but it does not disclose edge-case behavior like ties, nulls, default sort order, or what the returned rows look like. The schema partially covers defaults, but the description itself is minimal.

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 compact sentence with no wasted words, and the illustrative 'which is the most/least X' adds helpful context. It is slightly vague with 'Stagenix dataset,' but it remains appropriately concise.

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 read-only tool, the schema plus description are mostly workable, but the definition lacks explicit when-to-use guidance and does not describe output shape or default behavior. It is minimally viable but leaves gaps an agent would need to resolve.

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%, but the description adds the important meaning that `column` must be numeric and clarifies the 'most/least' interpretation. However, `limit` is left to its name and schema bounds, so the description does not fully compensate for the missing parameter documentation.

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 a specific action—returning the highest or lowest rows of the Stagenix dataset by a numeric column—which separates it from siblings like dataset_row or dataset_stats. It is specific about the resource and criterion, 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 given about when to use dataset_top versus dataset_search, dataset_stats, dataset_row, or other siblings. There are no exclusions, prerequisites, or alternative conditions, so the agent must infer usage from the name alone.

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