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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 full burden of disclosure. It describes the output contents but does not explicitly state that the call is read-only, cheap, or safe, nor does it describe any operational characteristics such as caching or response 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?

Two short, front-loaded sentences with no redundancy. The list of what the tool returns and the call-first instruction are immediately visible and every element 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?

Since there is no output schema, the description compensates by naming the main return components: columns, numeric flags, row count, and provenance banner. It stops short of giving exact structure or examples, but for a simple metadata tool this is nearly adequate.

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?

With zero parameters and full schema coverage, there is nothing for the description to add about parameter meaning. Baseline 4 is appropriate 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?

States exactly what the tool surfaces — columns, numeric columns, row count, and provenance banner of the Vatnix dataset — and explicitly instructs to call it first. This clearly separates it from sibling tools that retrieve rows, compute stats, or search 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?

'Call this first to learn the schema' provides explicit usage context and timing. It does not name alternatives or state when not to use it, but the instruction is enough to guide an agent toward correct sequencing.

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 the ordering behavior ('in the order given'), which is beyond the schema, but it does not disclose error handling (e.g., missing values or column), output format, or whether it is read-only. For a read operation this is acceptable, but more detail would improve transparency.

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 that front-loads the core functionality and includes a usage hint at the end. It is concise with no unnecessary words or repetition, making it easy to parse 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?

For a simple two-parameter tool with no output schema or annotations, the description covers the essential behavior but leaves gaps: it does not specify what happens when no rows match, how column names are validated, or the exact format of the returned data. It is adequate for basic use but not comprehensive for edge cases.

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 is the sole source for parameter meaning. It explains 'column' as the filtering column and 'values' as the list of values to match, and explicitly notes that row order follows the order of the given values. This adds meaningful semantics beyond the raw schema types and constraints.

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 Vatnix dataset filtered by a column matching any of the given values, in the order provided. It conveys the core purpose and the ordering constraint, but it does not explicitly differentiate from sibling tools like dataset_row or dataset_search, though the title 'Compare rows side by side' hints at the comparison intent.

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' implies a use case for comparing specific values side by side, but it does not explicitly state when to use this tool over alternatives or when not to use it. There is no mention of exclusions or routing to siblings, leaving the context implied 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses the exact informational content and frames the operation as read-only via 'Read this'. Side effects are not mentioned, but none are plausibly expected for a zero-parameter metadata lookup.

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 tight sentences with no wasted words. The first sentence enumerates content; the second states the use case. The title adds a concise complement.

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 tool, the description is complete. It lists the returned fields and the intended use, which is everything an agent needs to call it and interpret the result.

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 there are no parameter meanings to clarify. The description adds no parameter information, which is appropriate and matches 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?

States exactly what the tool returns: source, computed date, licence, and citation for the Vatnix dataset. The title reinforces the purpose, and the resource is specific enough to distinguish it from sibling data-manipulation 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 instruction 'Read this to attribute a figure correctly' gives a clear when-to-use trigger. It does not explicitly name alternatives or exclusions, but with zero parameters and a unique metadata purpose, the guidance is sufficient.

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 behavioral disclosure burden. It reveals important behavior—case-insensitive exact matching—but does not describe output shape, multiplicity of results, or behavior for missing/unknown columns or values.

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 focused sentence with no filler, and the title immediately conveys the tool's purpose. Minor overlap between title and description keeps it from a perfect score.

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 tool, the description is mostly adequate: it defines the filtering behavior and case sensitivity. However, the absence of an output schema and annotations leaves gaps around return format, multiple matches, and error behavior.

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 clarify the parameters. It does connect 'column' and 'value' to the equality condition and adds case-insensitive semantics, but it does not explain valid column names, value formatting, or edge-case handling beyond the schema's basic string types.

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 lookup behavior: return rows in the Vatnix dataset where a column exactly equals a given value, with case-insensitive matching. This distinguishes it from a general search tool, though it does not explicitly name the sibling alternative.

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?

Usage context is implied rather than stated: the exact-match, case-insensitive semantics suggest this tool is for precise key lookups rather than fuzzy searching. There is no explicit guidance on when to use this tool versus dataset_search or other 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 carries the behavioral burden. It discloses case-insensitive matching, whole-row returns based on cell content, and a result cap of 50. These are useful behavioral details not otherwise available.

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 concise sentence with no filler, front-loading the core behavior before the limit. Every clause contributes necessary 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 search tool with no output schema, the description gives enough context: matching rule, case sensitivity, result size cap, and the fact that rows are returned. Minor omissions like ordering or empty-result behavior are acceptable at this complexity level.

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 adds real value by clarifying the query matches cells case-insensitively and that results are rows up to 50. The limit's meaning is reinforced beyond the schema's min/max constraints.

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?

States a specific action (search) and resource (Vatnix dataset rows) with precise matching semantics: cells containing the query, case-insensitively, up to 50 results. This clearly distinguishes it from sibling tools like dataset_stats 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 makes the usage context clear: use this tool to find rows whose cells match a query. It does not explicitly exclude alternatives or name siblings, but the row-search scope is unambiguous enough for an agent to select it.

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. It discloses that non-numeric rows are excluded and counted, and that grouping commas and currency are handled, which are key behavioral traits. However, it does not explicitly state that the tool is read-only (obvious but not stated) or describe error behavior for missing columns, though these are minor for a stats 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?

A single, information-dense sentence. It front-loads the statistics list, then adds data-handling caveats. No filler or redundancy; every clause contributes value. Efficient and well-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 simple tool with one parameter and no output schema, the description covers the essential behavior: what statistics are computed and how data quirks are handled. The list of stats effectively implies the return structure. It lacks explicit details on error cases (e.g., missing column) but is otherwise adequate 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?

The schema only defines 'column' as a string with minLength 1 (0% coverage). The description adds meaning by indicating the column must be numeric and that formatting like commas and currency is handled. This clarifies the expected input beyond the bare schema, though it does not explicitly define the column name format or validation rules.

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), a resource (numeric column of the Vatnix dataset), and the exact set of statistics (count, min, max, mean, median, sum). It also adds data-handling details (commas/currency, non-numeric rows) that clearly distinguish it from siblings like dataset_row or dataset_top.

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 purpose is clear, but there is no explicit guidance on when to use this tool versus the sibling tools (e.g., dataset_row, dataset_top). Usage is implied by the stats focus, but no exclusions or alternative recommendations are given. A brief note like 'Use this when you need aggregate statistics, not raw rows' would improve it.

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 available, the description carries the full burden of behavioral disclosure. It states the core ranking behavior but says nothing about what is returned (full rows vs. values), default limit behavior, or tie/null handling.

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 that is front-loaded with the core behavior and ends with a compact use-case phrase. It is concise and readable, though the quoted phrase partly restates the title.

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, with three straightforward parameters, so the description is functionally usable. However, with no output schema and no annotations, omitting the return shape and default limit leaves an agent with incomplete context for interpreting results.

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%, and the description adds the important constraint that column must be numeric, plus framing ascending as highest/lowest. It does not clarify limit defaults or provide further semantics for the 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?

States a specific operation: returning the highest or lowest rows of the Vatnix dataset ranked by a numeric column. The quoted use case 'which is the most/least X' clarifies intent, though it does not explicitly contrast sibling tools like dataset_stats 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 implies when to use the tool through 'which is the most/least X', but it never states explicit conditions, exclusions, or alternatives among the sibling tools. An agent has to infer the decision boundary.

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