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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 discloses the return contents (columns, numeric flags, row count, provenance banner), clearly implying a read-only metadata operation. It does not explicitly state that no data rows are returned or describe output formatting, but for a zero-parameter introspection tool this is adequate.

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, information-dense sentence. It front-loads the concrete return items and ends with actionable guidance ('Call this first'), with no filler or redundant repetition of the tool name or 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?

For a no-argument metadata tool with no output schema, the description covers both when to call it and what it returns. The only minor gap is the unspecified shape of the 'provenance banner,' but this does not prevent correct invocation or sibling 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?

The input schema has no properties, so there are no parameters to document; the 0-param baseline applies. The description adds relevant semantic context by explaining what the returned schema information will contain, which is the only meaningful guidance needed 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 clearly identifies the tool as a schema-overview call for the Cafmlane dataset, listing the exact information returned: columns, numeric flags, row count, and provenance banner. It lacks an explicit verb naming the operation and does not explicitly contrast with siblings, but 'Call this first to learn the schema' makes the purpose 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 gives an explicit usage trigger: 'Call this first to learn the schema,' which tells an agent when this tool should be invoked. It does not, however, state when to prefer siblings like dataset_row or dataset_stats, so exclusion guidance is missing.

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 discloses the ordering behavior ('in the order given') and the filtering mechanism ('whose column is any of the given values'), which is helpful. However, it does not mention what happens if no rows match, whether the result includes the column used for filtering, edge cases like duplicate values, or any performance or data-volume implications. For a read-style comparison tool, the safety profile is implicit but not stated.

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 front-loads the essential behavior (filtering and ordering) and ties it to a clear use case. It earns its place with two useful pieces of information: the exact match filter and the ordering guarantee. It loses one point because the schema is minimal and the description could have briefly stated the two parameter names, but overall it is 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?

Given the absence of an output schema, the description does not explain return values or the result format, but that is likely acceptable for a simple comparison tool. It covers the key behavior: selecting rows by column values and preserving order. It omits edge-case behavior (empty results, duplicate values) and does not state whether the output is rendered as a comparison table, which could be relevant for 'X vs Y' questions. Still, for the tool's likely simplicity, the description is largely 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 0%, so the description must compensate. It explains that 'column' is the field used to filter rows and that 'values' are the specific values to match, and it adds the ordering semantics ('in the order given'). It does not explicitly name the parameter keys in the description; an agent must infer the mapping from context. It also does not clarify whether 'given values' corresponds exactly to the 'values' array order, though that is fairly evident from the phrasing.

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 ('Compare rows side by side') and a specific resource (the Cafmlane dataset rows filtered by column values), plus the use case ('X vs Y' questions). It distinguishes this tool from siblings like dataset_row (single-row access) and dataset_search (fuzzy/query-based access), and makes clear it returns multiple rows in a specified order.

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' gives an explicit use-case context, and the instruction that rows are returned 'in the order given' tells the agent exactly how to construct the values array. It clearly implies when this tool is appropriate versus alternatives: when comparing specific row values side by side rather than retrieving one row or searching broadly.

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 disclosure burden. It does state the returned content (source, date, licence, citation) and implies a read-only action, but it does not describe whether the data is generated on demand, batch-provided, or what the output format looks like. For a zero-parameter metadata tool this is acceptable but minimally 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?

The description is a single, tight sentence that front-loads the most useful information (source, date, licence, citation) and immediately gives the usage context. The title reinforces the purpose without repeating the description verbatim. No wasted words.

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, the description covers the core purpose, the data elements included, and the intended use case. Given the low complexity and the clear sibling context, nothing essential is missing for an agent to select and invoke this tool 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 tool has zero parameters and schema coverage is 100% (empty properties object), so there are no parameter semantics to explain. The description appropriately focuses on what provenance information is returned rather than input handling.

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 resource (the Cafmlane dataset) and the purpose: returning provenance metadata (source, computed date, licence, citation) for attribution. The title and first clause together distinguish this from sibling tools that explore columns, rows, stats, or search, since this is the only provenance-specific tool.

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 says to use this tool when attribution is needed: 'Read this to attribute a figure correctly.' It implies this is the appropriate tool among the dataset siblings for citation/provenance needs, though it does not explicitly name alternatives or state when not to use 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?

Annotations are absent, so the description carries the full behavioral burden. It does disclose useful behavior—exact matching and case-insensitivity—but leaves ambiguity about cardinality (title says 'a row', description says 'rows'), no-match behavior, and the shape of the returned result.

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, efficient 14-word sentence with no filler, and the core matching semantics are front-loaded. The awkward phrasing 'where a column equals a value exactly' and the lack of an active verb keep 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-string-parameter lookup with no annotations and no output schema, the description conveys the essential matching logic. However, it does not resolve the singular/plural result ambiguity, describe the return format, or differentiate itself from the dataset_search sibling, leaving meaningful gaps for an agent deciding to call it.

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 establishes the relationship between the two parameters—column is matched against value via exact case-insensitive equality—but it never specifies whether 'column' is a header name or an index, and it gives no examples or value-format 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 clearly identifies the resource (rows of the Cafmlane dataset) and the operation (filter to rows where a column exactly equals a value, case-insensitively), reinforced by the title 'Look a row up by an exact key.' It does not explicitly name sibling tools, and it is a sentence fragment rather than an active-verb statement, so it falls just 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?

There is no guidance on when to use this tool versus siblings such as dataset_search (likely the fuzzy/partial-match alternative) or dataset_compare. The exact-match phrasing weakly implies a use case, but no conditions, exclusions, or alternatives are 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, the description carries the full behavioral burden and does disclose real traits: case-insensitive matching, cell-containment (not whole-cell equality), row-level results, and a 50-row cap. It omits details like pagination/ordering, but for a read-only search the disclosed behavior is sufficient.

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 that front-loads the resource and result type, then packs in matching semantics, case sensitivity, and the row cap. Every word earns its place with no redundancy against the schema.

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 read-only search tool, the description covers the essentials: result type, match behavior, and limit. The absence of an output schema means return-format details are not spelled out, but nothing an agent needs to safely invoke the tool 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?

Schema coverage is only 50%, with the limit parameter undocumented. The description partially compensates: it adds 'case-insensitive' to the query semantics beyond the schema's 'text to look for in any cell,' and ties the limit to the 'up to 50' cap. It does not explain the optionality or default of limit, but it meaningfully enriches both 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 description names the exact resource (Cafmlane dataset), the operation (retrieve rows), and the matching criterion (cells containing the query), adding case-insensitivity and a 50-row cap. This clearly differentiates it from any tool that operates on columns or aggregates rather than row-level text 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 use case is implied: invoke this when you need rows whose cells contain a given text query. However, there are no explicit when-to-use instructions, exclusions, or references to alternative tools, so the guidance remains implicit 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses that grouping commas and currency symbols are parsed and that non-numeric rows are excluded and counted. These are meaningful behavioral traits beyond a generic 'compute stats' statement.

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 concise sentence that front-loads the computed statistics and adds the important data-cleaning caveats in a parenthetical. Every word earns its place, with no repetition of schema details.

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 covers operations and edge-case behavior, but there is no output schema and the description does not state the return format or exact key names. The phrase 'non-numeric rows are excluded and counted' is slightly ambiguous about whether the count is returned separately. For a simple one-parameter tool this is a minor gap, but still incomplete.

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%, but the description compensates by explaining that the 'column' parameter must identify a numeric column in the Cafmlane dataset. It also clarifies how formatting in that column is interpreted. The exact identifier format is not specified, but with a single minLength string 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 a specific resource ('numeric column of the Cafmlane dataset') and lists the exact operations computed (count, min, max, mean, median, sum). This leaves no ambiguity about what the tool does and clearly differentiates it from sibling tools like dataset_search 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 description gives clear context: use this tool for numeric column summary statistics on the Cafmlane dataset. It also notes how non-numeric rows and formatted numbers are handled, but it does not explicitly state when not to use it or name alternative 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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose that the tool ranks by a numeric column and can return either the highest or lowest rows. However, it does not mention default behaviors such as the default limit or default ordering (ascending), nor does it clarify whether the result includes ties or how the column is validated. For a read-oriented tool, these gaps are moderate.

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 concise and front-loads the core functionality: ranking rows by a numeric column. The example framing ('which is the most/least X') makes the intent understandable quickly. It earns its place without excessive detail, though it could benefit from a sentence about ordering semantics.

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 simple tool design (3 parameters, 1 required) and no output schema, the description covers the essential purpose and basic usage but leaves some gaps. It does not clarify whether 'column' must be numeric even though the description implies it, nor does it state the default ordering direction. The description is adequate for simple use but not complete for an agent to confidently invoke without additional inference.

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 33%, meaning only the 'ascending' parameter has a description. The description adds the concept of ranking (highest/lowest rows), which complements the schema's parameter list. However, it does not explain the meaning or constraints of 'column' or 'limit' beyond what the schema shows, so the added value is only moderate.

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 purpose: rank rows of the Cafmlane dataset by a numeric column and return the highest or lowest rows. It maps to the tool name well and conveys the core functionality. While it doesn't explicitly differentiate from sibling tools, the purpose is specific enough to be understood on its own.

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: when you need to find rows with the highest or lowest values in a numeric column ('which is the most/least X'). It does not explicitly state when to use this tool versus alternatives, and there are no named alternatives. The guidance is clear enough for a simple ranking tool but lacks explicit exclusions.

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