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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 burden of behavioral disclosure. It reveals what the response contains and implies a read-only schema inspection, but it never explicitly states that the call has no side effects or other behavioral caveats. For a zero-parameter metadata tool this is acceptable, though not fully 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?

Two short sentences contain no filler. The first sentence front-loads every artifact the tool returns, and the second gives the usage instruction. Every clause 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?

For a tool whose sole job is to report dataset schema, the description covers the returned columns, numeric flags, row count, and provenance banner, and tells the agent when to call it. It omits output-format details, but no output schema exists and the content is simple enough to be self-explanatory.

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 properties and description coverage is 100%, so there are no parameter semantics to document. The baseline for zero-parameter tools is 4, and the description adds no parameter information, which is appropriate because no parameters exist.

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 what is exposed: columns, which of them are numeric, row count, and provenance banner. It also frames the tool as the schema-learning entry point, which implicitly distinguishes it from sibling tools. It lacks an explicit verb like 'returns' or 'gets,' but the intent is 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?

'Call this first to learn the schema' provides explicit sequencing guidance, which is a strong usage signal for an agent. It does not name alternatives or exclusions, so it stops short of a full when/when-not rule, but the context is clear.

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. It usefully discloses that rows are selected by matching a column against given values and that ordering follows the provided value order. However, it says nothing about behavior when no rows match, case sensitivity, duplicate values, or the shape of the returned comparison.

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 packs the selection logic, ordering rule, and intended use case without any filler. It is front-loaded with the main behavior and ends with the purpose clause, making it maximally scannable.

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-style tool with no output schema, the description provides the essential invocation facts: what is returned (rows), how they are filtered, and in what order. It omits edge-case behavior and response formatting, but these are less critical given the low complexity and the visual title 'Compare rows side by side'.

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 descriptions for `column` or `values`, and schema description coverage is 0%, so the description must compensate. It does so by clarifying that `column` is the attribute to match and `values` are the accepted values, and it adds the important semantic that values are considered in the given order. This adds real meaning beyond the bare type 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 states a clear functional behavior: it returns rows of the Mustvo dataset whose column matches any of the supplied values, in the supplied order. It also frames the use case as 'X vs Y' questions, which helps distinguish it from more general row/search/stats tools, though it does not explicitly name those siblings.

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 a clear, concrete context for when to use this tool over alternatives like dataset_row or dataset_search. It does not explicitly list when not to use it or name alternative tools, so it stops short of full exclusionary 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. It discloses the type of data returned (source, date, licence, citation) but does not explicitly state that the operation is read-only or non-mutating. While the nature of the tool implies no side effects, it would be safer to state this explicitly. It also doesn't mention any limitations like caching or staleness.

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 concise sentences with no filler. It front-loads the key information (what is returned) and immediately follows with a clear usage instruction. Every sentence 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?

For a zero-parameter metadata retrieval tool with no output schema, the description adequately covers what it returns and when to use it. It could specify the return format more precisely, but for such a simple tool, the description is nearly complete. The sibling tools don't overlap in function, so there's no risk of confusion.

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 the schema reflects that. Per the rubric, a baseline of 4 applies for 0 parameters, and the description correctly doesn't attempt to explain parameters that don't exist.

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 specific resource (Mustvo dataset) and the information provided (source, date, licence, citation). It distinguishes itself from sibling tools that operate on data content rather than metadata, making its 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 explicitly states when to use it ('Read this to attribute a figure correctly'), giving a clear usage context. It doesn't mention alternatives or exclusions, but the purpose is so distinct from siblings that this 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, the description must carry the full behavioral disclosure. It states the matching is exact and case-insensitive, which is useful. However, it does not mention what the output looks like (e.g., entire rows or identifiers), whether multiple matches are returned, what happens on no match, or any error conditions. These gaps leave the agent guessing about the result format.

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 behavior (exact, case-insensitive matching). There is no wasted verbiage, and the key qualifiers are placed early. It is both concise and structured effectively.

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?

This is a simple tool with only two string parameters and no output schema. The description covers the essential matching logic, but it omits details about the return value shape, multiplicity, and failure modes. Given that annotations are absent, an agent might not know whether to expect a single row or a collection. The description is adequate for a basic lookup 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 0%, so the description must explain the parameters. It does so implicitly: 'where a column equals a value' clarifies that 'column' is the dataset column name and 'value' is the value to match. This adds meaning beyond the bare string types in the schema, but it doesn't specify constraints like column must exist or whether the value should be quoted. It's adequate but not thorough.

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 description 'The rows of the Mustvo dataset where a column equals a value exactly (case-insensitive)' clearly state the verb (look up), resource (rows), and the exact matching semantics. This distinguishes it from dataset_search, which implies a broader or fuzzy search, and from dataset_stats or dataset_top which aggregate. The purpose is 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 provides no guidance on when to use this tool versus its siblings like dataset_search. It does not mention scenarios where exact match is preferred or when a different tool would be more appropriate. The 'exact' in the title hints at a use case, but there is no explicit comparison or exclusion.

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 burden of disclosing behavior. It does mention that the search is case-insensitive and that results are limited to 50, which gives some transparency. However, it does not specify what happens when no rows match, how the rows are ordered, or whether the search spans all columns, leaving some behavioral aspects undisclosed.

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 to the point, consisting of a single sentence that effectively captures the core functionality, the case-insensitivity, and the maximum result count. There is no redundant information or verbosity.

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 simplicity of the tool (no output schema, no nested structures), the description is sufficiently complete for an agent to understand what the tool does and what its parameters are. It could be slightly more complete by mentioning the return format or error behavior, but these are not critical for a simple search operation and are typically inferred.

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 a description for query ('text to look for in any cell') but not for limit. The overall description clarifies query's role but only implies limit's purpose via 'up to 50'. Since schema coverage is 50% (only query described), the description adds some value by explaining the limit as a cap, but it does not fully elaborate on how limit interacts with the search (e.g., whether it applies after all matches are found).

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 Mustvo dataset whose cells contain the query, which conveys the search functionality. It implicitly differentiates itself from sibling tools like dataset_row (which fetches a specific row) and dataset_top (which returns top rows without a search). However, the verb 'search' is only implied, not explicitly stated, making it slightly less direct.

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 no guidance on when to use this tool versus alternatives. It does not mention that this should be used for text searching or that it is inappropriate for other tasks like fetching specific rows or getting statistics. The only hint is the search condition and the limit, but there is no explicit 'use this when...' or 'instead of...'.

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 that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is valuable. However, it doesn't mention whether the operation is read-only, error handling, or performance, but for a stats tool these are less critical. It adds context beyond the 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?

One concise sentence that packs all essential information: the operation, the output statistics, and data handling behaviors. No filler, perfectly front-loaded. It 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?

For a simple one-parameter tool with no output schema, the description is fairly complete. It specifies what statistics are returned and how data quirks are handled. It doesn't cover edge cases like missing columns or empty results, but those are minor. Overall, an agent can correctly invoke it without further clarification.

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 has one parameter 'column' with no description, and schema coverage is 0%. The description mentions 'numeric column of the Mustvo dataset', which implies the parameter is a column name and that it must be numeric. However, it doesn't provide examples, format expectations, or clarify case sensitivity or exact naming. It provides some meaning but not fully compensates for the lack of schema descriptions.

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 verb (compute) and resource (numeric column of the Mustvo dataset) and lists the exact statistics returned. It distinguishes from siblings like dataset_row or dataset_search by focusing on aggregation, making it unmistakable what this tool does.

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?

While not explicitly stating when to use this vs alternatives, the specific purpose (summary statistics for a numeric column) strongly implies its use case. It doesn't mention exclusions or alternatives, but the sibling names (columns, compare, row, search, top) are distinct enough that confusion is unlikely. It's clear but could have explicitly stated 'use this when you need column statistics'.

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 explaining behavior. It states that the tool returns the highest or lowest rows by a numeric column and mentions the ascending default via the parameter description. However, it does not explicitly describe side effects, output format, or tie handling, so it is not fully 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, concise sentence with an em-dash use-case illustration. It is front-loaded and contains no filler, making it easy to scan.

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 has no output schema, and the description does not specify what is returned (e.g., full rows, only the column, or a ranked table). This is a notable gap for an agent deciding how to use the result, though the core ranking behavior 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?

The description clarifies that the column must be numeric, adding semantic value beyond the schema. However, the limit parameter is left undescribed in both schema and description, and ascending is only explained in the schema. With only 33% schema description coverage, more compensation was needed.

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 by a numeric column and returns the highest or lowest rows, with the use case 'which is the most/least X'. This distinguishes it from sibling tools like dataset_search 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 gives a natural-language use case ('which is the most/least X') but does not explicitly contrast with sibling tools or explain when to prefer this over dataset_row or dataset_stats. Some guidance is present, but alternative differentiation is missing.

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