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

No annotations are provided, so the description carries the behavioral disclosure burden. It reasonably implies a read-only schema-introspection operation and enumerates the returned information. It does not explicitly state 'read-only' or side-effect absence, but the tool's nature makes that low risk.

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 that lists the essential outputs and the recommended call order. Every phrase earns its place and 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, no-output-schema introspection tool, the description is complete: it states what data is returned, scopes it to the specific dataset, and tells the agent when to invoke it. Nothing needed to call the tool correctly is missing.

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 is empty with zero parameters, so the baseline is 4. The description adds the relevant dataset context ('Runwayze dataset') and what the agent will learn, which is sufficient compensation given there are no parameters to explain.

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 exactly what the tool returns: columns, numeric flags, row count, and provenance banner for the Runwayze dataset. It also conveys the intended call sequence ('Call this first to learn the schema'), clearly distinguishing it from the 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 timing guidance by saying to call this first to learn the schema, which is clear context for use. It does not name alternatives or when-not-to-use cases, so it stops short of the strongest possible usage 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 states the operation is a retrieval of rows, implying a read-only action, but does not explicitly mention that it does not modify data. It also doesn't discuss edge cases like no matches, duplicate handling, or any limits beyond the input schema. The description covers the core behavior but lacks depth on potential side effects or guarantees.

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 primary action and includes the key detail about ordering. Every word contributes value; there is 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?

The tool is simple (2 parameters, no nested objects, no output schema). The description explains the inputs and purpose but does not specify the output format or any pagination/limit behavior. Given the simplicity, it is mostly complete for an agent to call correctly, but the lack of output details is a minor gap.

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 provides only types and constraints (column as string, values as array of strings with min 2, max 10). The description adds meaning by explaining that 'column' is the field to match against and 'values' are the specific values to filter for. It also clarifies that output order follows the order of 'values', which is not evident from the schema. With 0% schema coverage, the description effectively compensates.

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 action (return rows), the specific resource (Runwayze dataset), and the selection criterion (column matching any given values). It also mentions ordering, which distinguishes it from a generic search. The purpose is unambiguous and easily distinguishable 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 description provides a clear use case: 'for "X vs Y" questions'. This signals when to use the tool (comparing specific values in a column). It doesn't explicitly list exclusions or alternatives, but the context is clear enough for an agent to infer it's for targeted comparisons rather than broad queries.

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 of behavioral disclosure. It discloses the type of information returned (source, date, licence, citation), but does not explicitly state that the tool has no side effects or that it is read-only. Given the simple informational nature, the description is adequate but not exhaustive. It does not contradict anything, but could be more explicit about it being a safe read operation.

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 filler. The first sentence lists the returned details, and the second states the use case. It is front-loaded with the core information and efficiently 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 zero-parameter tool with no output schema, this description is complete. It tells the agent what information will be returned and why to use it. There are no missing prerequisites or edge cases given the simplicity of the 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, so the baseline is 4. The description does not need to explain any parameters, and it adds no parameter-related meaning because there are none. This is appropriate given the empty 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 returns source, date, licence, and citation for the Runwayze dataset. The verb 'Read this' and the resource 'Runwayze dataset' are specific. This distinguishes it from siblings like dataset_columns or dataset_stats, which deal with other aspects of the data.

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 tells the agent when to use it: 'Read this to attribute a figure correctly.' This provides clear context for invocation. However, it does not explicitly mention alternatives or when not to use it, though the purpose is clear enough relative to siblings.

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 must carry the behavioral disclosure burden. It reveals case-insensitive equality, which is useful, but it does not state whether one or all matching rows are returned, what happens on no match, or how the values are normalized. The singular title ('a row') versus plural description ('the rows') creates ambiguity about the return shape.

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 wasted words, and the exact-match condition is prominently stated. It is concise and readable, though it lacks an explicit verb and leaves some behavioral details unstated.

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 annotations and an output schema, the description needs to explain what the caller should expect after invocation. It does not clarify whether the result is a single record or a list, how matches are ordered, or how errors/missed keys are handled, so an agent is left with meaningful uncertainty even for a simple lookup.

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 identify `column` as the field to match and `value` as the value to compare, and it communicates the case-insensitive exact-match rule. However, it does not explain column name formatting or provide any additional detail beyond what the two parameter names already suggest.

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 Runwayze dataset where a column equals a value exactly, and it adds the case-insensitive qualifier. The verb is implied rather than explicit ('Look a row up' in the title), but the resource and matching behavior are specific enough to distinguish from a general 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 does not say when to use this tool versus siblings like dataset_search, dataset_top, or dataset_compare. It implies exact-match usage but gives no exclusions or alternative routing, leaving the agent to infer when dataset_row is the right choice.

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. It discloses meaningful traits: matching is case-insensitive, all cells are searched, and the result set is capped at 50 rows. It does not describe output structure or ordering, but the core behavior is transparent.

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 16-word sentence with no filler. It front-loads the operation and result, then appends the two most important modifiers: case-insensitivity and the 50-row 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 with no output schema, the description covers the essential facts an agent needs: what is searched, how matching behaves, and the maximum result count. Details like ordering or pagination would be nice but are not required for a correct first call.

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%, but the description compensates by explaining the query as a case-insensitive cell match and conveying the limit's effect as an implicit cap. The limit parameter is not named explicitly, yet its meaning is recoverable from 'up to 50.'

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 a specific verb and resource: rows of the Runwayze dataset that contain the query. It also adds concrete behavior (case-insensitive, up to 50), making it easy to distinguish 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 clearly implies when to use it — when you need to find rows by matching text in any cell — but it gives no explicit exclusions or guidance relative to sibling tools. The usage context is inferable rather than stated.

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 must carry the full behavioral burden. It discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. However, it does not mention error behavior (e.g., column not found), edge cases (all rows non-numeric), or the exact return format. This is adequate for a simple tool but leaves some 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, well-structured sentence. It front-loads the list of statistics, then adds the crucial handling details. There is no waste; every clause adds 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 simple one-parameter tool with no output schema, the description covers the core functionality, the column type expectation, and the handling of non-numeric and formatted values. It does not discuss error cases or return shape, but these are minor given the simplicity and the distinct sibling set. Overall, it is nearly 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?

The input schema provides only a 'column' string with minLength 1 and no description, so schema coverage is 0%. The description compensates by clarifying that the column must be numeric and that formatted numbers (commas, currency) are accepted, adding meaning beyond the raw schema. It does not provide examples but is sufficient for a single 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 a specific verb (compute) and resource (numeric column of the Runwayze dataset), and enumerates the exact statistics produced (count, min, max, mean, median, sum). This distinguishes it from all siblings, which handle columns, comparisons, provenance, rows, search, and top values—none of which compute summary 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 implies usage for numeric columns and notes how non-numeric rows are handled, but it does not explicitly state when to use this tool versus alternatives or when not to use it. Given the siblings are clearly different in purpose, the implication is strong, but no direct guidance or exclusions are provided.

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 tool returns the highest or lowest rows, implying sorting and limiting. However, it does not disclose output format, error behavior, or edge cases like ties or non-numeric columns. It is a basic but non-contradictory disclosure.

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, front-loaded with the core idea. It is concise and free of fluff, earning its place. There is no unnecessary information.

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 ranking tool, the description provides the essential purpose but omits details like return format and any caveats. With no output schema and low parameter coverage, the agent may not know what to expect in the response. It is adequate for basic use but not fully 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 33% (only 'ascending' has a description). The tool description does not explain the 'limit' or 'column' parameters beyond what the schema already provides. It implies ascending via 'highest/lowest' but does not explicitly link it to the parameter. Given the low coverage, the description should compensate but does not.

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 ranks rows of the Runwayze dataset by a numeric column to answer 'which is the most/least X'. This distinguishes it from siblings like dataset_row (single row) and dataset_search (search). It names the resource and the verb, making the purpose specific. However, it does not explicitly contrast with dataset_stats, but the intent is clear enough.

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 alternatives. The description does not mention any scenarios, exclusions, or alternative tools. An agent has to infer that this is for ranking rows, but there is no explicit when-to-use or when-not-to-use context.

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