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

With no annotations provided, the description carries the full burden and does disclose the returned elements: columns, numeric flags, row count, and provenance banner. It does not explicitly state that the operation is read-only or discuss freshness, but for a zero-parameter schema query the behavioral surface is otherwise well covered.

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 filler; every clause adds either a return element or usage guidance. It is compact and front-loads the main content before the 'call first' instruction.

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 tool with no output schema, the description is nearly complete: it lists all returned components and tells the agent when to call it. It could more precisely define the structure of the column/numeric mapping or clarify overlap with dataset_stats and dataset_provenance, but overall it provides adequate context 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?

There are zero parameters and schema coverage is 100%, so there is no parameter documentation burden. The description appropriately mentions no parameters, matching 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?

The description names the resource (Attestvio dataset columns) and enumerates the exact payload: columns, numeric flags, row count, and provenance banner. It lacks an imperative verb, but the instruction 'Call this first to learn the schema' makes its role as a schema-discovery tool clear and distinguishes it from sibling analysis 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 explicitly tells the agent to call this tool first to learn the schema, establishing clear entry-point usage. It does not name alternatives or state when not to use it, so the guidance is useful but not exhaustive.

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 transparently discloses that rows are filtered by column membership and ordered by the given values. However, it does not describe the return format, matching semantics like case sensitivity, or any other behavioral caveats.

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 that conveys the resource, filtering behavior, ordering behavior, and intended use case without any filler or repetition.

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, the description adequately covers what inputs mean, what rows are returned, and in what order. Minor gaps exist around the exact structure of returned rows and edge-case behavior, but an agent can invoke the tool correctly based on this description.

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 for the bare parameter names. It explains the relationship between 'column' and 'values' by stating that rows are selected when the column equals any value, and that value order determines row order. This adds meaningful semantics beyond the schema even though it does not restate constraints like minItems/maxItems.

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 selection operation: rows of the Attestvio dataset whose column matches any of the given values, in the given order. The title and 'for X vs Y questions' phrase help distinguish it from generic row or search tools, though it lacks an explicit verb.

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' provides a clear intended use case that differentiates it from more general dataset queries. It does not explicitly name alternatives or exclusions, but the comparison-oriented context is informative enough.

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 burden of behavioral disclosure. It does disclose the returned content (source, date, licence, citation), but it does not explicitly state that the tool is read-only or has no side effects. For a simple metadata retrieval tool, this is a minor but present gap.

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 wasted words. The content is front-loaded (what data it provides) followed by the practical usage instruction. Every part 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?

The tool is simple with no parameters and no output schema. The description lists the key fields that the output will contain (source, date, licence, citation), which is sufficient for an agent to understand the return value. A slight addition about the output format would make it fully complete, but it is not necessary for this simple case.

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 description cannot be expected to document parameters. The baseline for zero-parameter tools is 4, and the description stays at that level without adding unnecessary parameter-related content.

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 provides provenance metadata: source, date computed, licence, and citation. It also gives a specific use case ('Read this to attribute a figure correctly'), which distinguishes it from sibling tools that handle columns, rows, stats, and 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 provides a clear context for when to use the tool: to attribute a figure correctly. It does not explicitly name alternatives or exclusions, but none of the sibling tools serve the same provenance purpose, so 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, the description carries the full behavioral burden. It discloses case-insensitive exact matching and that multiple rows ('The rows') may be returned, but it does not mention read-only status, result limits, output shape, or error behavior.

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 sentence with no filler, and the exact-match constraint is front and center before the case-insensitive nuance. It earns its place by adding semantics beyond the tool name.

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 two-parameter lookup the core semantics are present, and the description states rows are returned. However, with no output schema or annotations it still omits match cardinality, result limits, and whether any column is allowed, leaving some ambiguity 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 description coverage is 0%, but the description maps both parameters: column is the field to compare and value is the exact comparison value, including the case-insensitive matching rule. It does not define valid column names or value formats.

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 operation: retrieve rows of the Attestvio dataset filtered by exact column-value equality, with case-insensitivity noted. This distinguishes it from a general search, though it relies on the sibling name dataset_search rather than naming it.

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 word 'exactly' implies this is the right tool for exact-match lookups rather than fuzzy/partial search, so usage context is only implied. There is no explicit statement of when not to use it or a mention of dataset_search for non-exact matching.

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 behavioral disclosure burden. It does a good job by stating that matching is case-insensitive, searches across all cells, and returns up to 50 rows. It does not detail default limit behavior or output ordering, but the core behavior is transparent enough for selection.

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 sentence contains all essential behavioral information: what is searched, how matching works, and the row cap. It is front-loaded and wastes no 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 straightforward search tool with no output schema and no annotations, the description is mostly complete: it identifies the return type (rows), the search scope, case sensitivity, and the limit. It could be more complete by mentioning the default limit and whether results are ordered, but the current description provides enough for an agent to decide whether to invoke 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?

The schema documents the query parameter but not the limit parameter, so description coverage is 50%. The description adds meaning by clarifying case-insensitive cell matching and the 50-row cap, but it does not explicitly explain how the 'limit' parameter controls the result count or what the default is. It partially compensates for the schema gap without fully covering it.

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 does: it returns dataset rows whose cells contain a case-insensitive query, capped at 50. It is specific about the resource (Attestvio dataset) and the matching behavior, though it does not explicitly differentiate itself from sibling tools 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 intended usage is implied: use this tool when you need to find rows by arbitrary text appearing in any cell. However, there is no explicit guidance about when not to use it or which sibling tool might be more appropriate for other scenarios.

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 takes on the burden of behavioral disclosure and does so effectively by noting that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. This reveals parsing and data-cleaning behavior that could materially affect results, though null-handling and return format are not specified.

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 lists the output statistics first, then adds relevant caveats in a parenthetical. Every clause contributes necessary information, and 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?

For a one-parameter tool without an output schema, the description covers input semantics and key behavioral edge cases such as currency handling and non-numeric rows. It does not describe the exact return structure, but enumerating the computed statistics gives the agent enough information to infer the expected 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?

Schema coverage is 0%, so the description must carry the meaning of the column parameter. It clarifies that the parameter refers to a numeric column of the Attestvio dataset, which is sufficient context for a single, simple string parameter despite not detailing column-name matching 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 explicitly lists the exact statistics produced (count, min, max, mean, median, sum) on a numeric column of a specific dataset, making the tool's function unambiguous. This is clearly distinguished from sibling tools like dataset_row or dataset_top through the resource and operation described.

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 tool is for obtaining summary statistics of a numeric column in the Attestvio dataset, but it does not explicitly state when to use it versus sibling tools or what conditions would make an alternative preferable. No exclusions or alternative routing are provided, so usage context is inferred 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 burden of behavioral disclosure. It explains that rows are ordered by a numeric column and can return either highest or lowest, which covers core behavior. It omits whether the result is a single row or list, default limit behavior, and whether the operation is read-only.

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 short, front-loaded with the core action, and uses the parenthetical 'most/least X' to clarify the use case. Every word earns its place, though it could add a bit more behavioral detail without becoming verbose.

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 output schema and no annotations, the description conveys the essential ranking concept but leaves ambiguity around return format, default limit, and boundary conditions. Given the simple domain, it is minimally adequate but not fully complete for an agent invoking it without additional context.

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 description adds that 'column' should be numeric, which is useful, but it says nothing about the 'limit' parameter, its default, or how it interacts with ranking. The description only partially compensates for the missing schema documentation.

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 'Rank rows by a numeric column' and description 'highest (or lowest) rows ... by a numeric column' state a clear verb and resource. It does not explicitly differentiate from siblings like dataset_search or dataset_stats, but the ranking behavior is distinct enough for an agent to grasp the core action.

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 'which is the most/least X' implies the intended use case for ranking questions, providing some context. However, there is no explicit when-to-use guidance or mention of alternative sibling tools such as dataset_search or dataset_stats.

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