site

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

With no annotations, the description carries the burden of behavioral disclosure. It explains what information is returned and implies a read-only introspection operation, but it does not explicitly state that it makes no modifications or describe edge cases or errors. This is adequate 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?

Two sentences, front-loaded with the return content and immediately followed by the usage instruction. Every word earns its place.

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?

Without an output schema, the description names the key return components (columns, numeric flags, row count, provenance banner), which is sufficient for a zero-parameter schema-introspection tool. It could describe the exact output shape, but the listing is complete enough for an agent to call and inspect.

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 description adds no parameter-specific semantics, but none are needed since the input schema is empty.

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 what the tool returns: columns, numeric flags, row count, and provenance banner for the RunbookDesk dataset. It also frames the tool as a schema-learning first call, which distinguishes it from the sibling tools focused on rows, stats, search, and provenance.

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' gives explicit guidance on when to use it. It does not mention alternatives or exclusions, but the instruction to call it first is a clear usage signal.

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 must carry behavioral disclosure. It does convey the key behavior: returning rows filtered by column membership and preserving the given value order. However, it does not describe the output format, exact-match/case-sensitivity behavior, or what happens when no rows match, which leaves some behavioral ambiguity.

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 with no filler. The core selection behavior is front-loaded, and the 'X vs Y' use-case tag earns its place as a routing signal. Every part of the sentence contributes meaning.

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 low-complexity two-parameter tool with no output schema, the description is nearly complete: it explains what the tool does, what the inputs mean, and when to use it. The main omissions are return-shape details and edge-case behavior, which are less critical here because the operation is a straightforward row selection.

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: 'column' is the field being filtered on, and 'values' are the set of values whose rows are returned in the given order. The phrase 'is any of the given values' communicates equality semantics beyond what the bare schema property names provide.

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 operation precisely: it selects rows from the RunbookDesk dataset whose column matches any of the given values, preserving the given order. This clearly distinguishes it from sibling tools like dataset_stats or dataset_top, which obviously do different things. The phrase 'for X vs Y questions' reinforces the intended comparison role.

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 names the intended use case: 'for X vs Y questions' — i.e., comparing specific rows side by side. It does not mention alternatives or exclusion criteria relative to siblings like dataset_search, but the usage context is clear enough for an agent to select this tool appropriately.

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. It discloses what the tool returns (source, date, licence, citation) and implies a read-only metadata lookup, but it does not explicitly state that there are no side effects or describe the output structure.

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 short sentences, each earning its place: the first enumerates the returned metadata, and the second states the practical purpose. 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 parameterless provenance lookup, the description is complete: it identifies the dataset, the metadata fields returned, and the correct usage context. No prerequisites, side effects, or alternatives are needed.

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 takes zero parameters, so there is nothing for the description to explain beyond what the empty schema already shows. The baseline of 4 applies here.

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 specific resource (RunbookDesk dataset) and the exact contents returned: source, computation date, licence, and citation. This clearly distinguishes it from sibling tools like dataset_columns, dataset_search, or dataset_stats, which handle data exploration rather than provenance.

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' provides a clear, actionable use case. It does not explicitly contrast this tool with its siblings or state when not to use it, but the intended context is evident.

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 only mentions case-insensitive matching; it does not specify whether the operation is read-only, the return format, pagination, or any error conditions. This is a significant gap 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 concise sentence with no redundant phrasing. It front-loads the core functionality and the case-insensitive detail. While it is minimal, it is appropriately sized for its content, though additional information could have been included without harming conciseness.

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 absence of an output schema and annotations, the description is incomplete. It does not mention the shape of the result (e.g., a list of rows, a single row), any limitations, or how the exact match interacts with data types. An agent would need to make assumptions about the return value and error handling.

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%, and the description does not explain the 'column' or 'value' parameters at all. The schema provides only basic type and length constraints, so the agent has no context on what values are valid or how they map to the dataset. The description fails to compensate for this lack of 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 operation clearly: retrieving rows from the RunbookDesk dataset where a column equals a value exactly, and it adds the case-insensitive detail. It distinguishes itself from search tools by emphasizing exact matching, though it doesn't explicitly name alternatives. The title reinforces the purpose.

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?

There is no guidance on when to use this tool versus the sibling tools like dataset_search or dataset_compare. The description implies exact match, but it does not explicitly state conditions for selection or exclusions, leaving the agent to infer the appropriate context.

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 burden and does add useful traits: case-insensitive substring containment and an upper bound of 50 rows. It does not disclose ordering, result shape, pagination behavior, or default limit behavior, leaving some uncertainty.

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 conveys the core behavior with no filler. Every element earns its place: what is searched, matching semantics, and result cap.

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 search tool, the description plus schema covers required usage adequately: the query is required, limit is optional, and matching behavior is stated. No output schema exists, so the lack of detailed return-format information is a minor gap rather than a fatal 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 only 50%, so the description partially compensates. It clarifies query semantics by adding case-insensitivity and cell containment, and 'up to 50' maps to the limit parameter, but it does not explain default limit behavior or how limit interacts with result ordering.

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: searching rows of the RunbookDesk dataset by cell content. It specifies case-insensitive matching and a 50-row cap, which conveys the core function. It does not explicitly contrast with sibling tools like dataset_row or dataset_top, so it loses the top score.

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: find dataset rows where any cell contains a query string. However, it gives no explicit guidance on when to choose this over siblings such as dataset_row for exact access or dataset_stats for summary views, and it provides 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 behavioral disclosure burden. It usefully discloses that grouping commas and currency formats are handled and that non-numeric rows are excluded and counted. However, it does not clarify output structure, behavior on an empty or fully non-numeric column, or whether 'count' refers to valid numeric rows, excluded rows, or both.

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 lists the exact computed statistics and adds necessary parsing/exclusion caveats in a parenthetical. 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 one-parameter tool, the description covers the core operation well. However, it lacks an output schema, does not specify the return format, and leaves edge-case behavior (empty columns, all non-numeric values, missing columns) ambiguous. More detail would make it fully reliable for an agent.

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 for the single 'column' parameter. It does so by clarifying that the column should be numeric and that formatted values such as commas and currency are accepted. This gives meaningful interpretation beyond the bare string 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 clearly states the tool computes count, min, max, mean, median, and sum for a numeric column of the RunbookDesk dataset. It is specific about the resource and operation, though it does not explicitly differentiate itself from sibling tools such as dataset_top or dataset_compare.

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 intended use is implied: use this when summary statistics for a numeric dataset column are needed. However, there is no explicit guidance on when to prefer this over siblings or when it would not be appropriate, such as for non-numeric or categorical columns.

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 states the basic highest/lowest ordering behavior but does not mention default limit behavior, tie-handling, error cases, or the shape of the returned result. This is thin for a tool that must be invoked safely.

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 with no filler and the core behavior is front-loaded. It loses a point because the title and description are somewhat redundant, both saying essentially 'rank rows by a numeric column.'

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, no output schema, and an under-described third parameter, this definition is too sparse. It does not tell the agent what a successful call returns, what the default limit is, or how invalid numeric columns are handled, leaving important gaps for correct invocation.

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 documents only one of three parameters semantically (ascending), so coverage is 33%. The description adds meaning for 'column' by specifying it must be numeric and clarifies ascending vs. descending, but it says nothing about the 'limit' parameter beyond what the schema's min/max imply.

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 highest or lowest rows of the dataset based on a numeric column, with a concrete use case ('which is the most/least X'). This distinguishes it from row retrieval or search, though it does not explicitly name a sibling or state what it is not.

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 'most/least X' phrasing implies when the tool is appropriate, but the description gives no explicit guidance about when to prefer it over dataset_stats, dataset_row, or dataset_search. It provides context but no exclusions or alternative routing.

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