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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 the concrete return contents and frames the operation as inspecting, not mutating, the Sudslane dataset; that is sufficient for a zero-parameter metadata query, though it does not explicitly say '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?

Two short sentences carry all essential information, with the output contents front-loaded and the usage guidance ('call first') at the end. No filler or repetition exists.

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 no output schema and no annotations, the description compensates by listing the main returned elements and giving an ordering cue. It is complete for a simple schema-discovery tool, though it could briefly mention that it takes no arguments or allude to which sibling to use after schema discovery.

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 there is nothing for the description to document. The baseline of 4 applies because no parameter semantics are needed; the schema already reflects an empty input object.

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 enumerates exactly what the tool yields — columns, numeric-column flags, row count, and provenance banner — so an agent can tell this is a schema-inspection tool. It is clear but lacks an explicit verb such as 'returns' and does not explicitly contrast itself with sibling tools like dataset_stats or dataset_provenance.

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" is an explicit when-to-use instruction and positions it ahead of the sibling dataset tools. It does not go further to say when not to use it or name a specific alternative, so it misses the top criterion.

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 does disclose useful behavior: rows are filtered by column-value membership and ordered according to the given values. However, it does not describe output format, pagination/limits, behavior on no matches or duplicates, or explicitly confirm read-only status, leaving partial 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, efficient sentence that front-loads the result and filtering criterion before adding the use case. There is no filler, repetition, or unnecessary detail.

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 provides the essential facts needed to invoke it correctly: target dataset, filtering column, allowed values, output ordering, and the comparison use case. It omits response-shape and edge-case details, but those are less critical given the low complexity.

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, and it does: 'column' is explained as the field to match on, and 'values' as the accepted values, with 'any' conveying OR semantics and 'in the order given' adding ordering meaning. It does not discuss constraints like minItems/maxItems, but those are already explicit in the input 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 identifies a concrete resource—rows of the Sudslane dataset—and specifies filtering semantics: rows whose column matches any of the given values. It clearly distinguishes itself from generic row or search tools through the phrase 'in the order given' and 'for X vs Y questions', but it lacks an explicit verb like 'returns' or 'fetches' and does not directly contrast with 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?

The phrase 'for “X vs Y” questions' gives a clear intended context: compare rows across specific column values. This tells the agent approximately when to use the tool, but it does not name sibling alternatives or explicitly 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly enumerates what the tool provides: source, date computed, licence, and citation. It also implies a read-only metadata operation. While it does not explicitly state side-effect-free behavior or auth requirements, nothing suggests mutation or complexity beyond a 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?

The description is one tightly written sentence that front-loads the key content fields and closes with a practical usage instruction. Every word earns its place, and the title reinforces the purpose without bloating the description.

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 metadata lookup, the description is complete. It names the dataset, enumerates the returned provenance elements, and tells the agent when to use it. There are no hidden inputs or complex behaviors left unexplained.

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 an empty input schema, so the baseline is 4. There is no parameter information needed, and the description correctly focuses on the tool's output rather than inputs. The description adds no param semantics because none 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 tool's purpose: it returns provenance metadata for the Sudslane dataset, specifically the source, computation date, licence, and citation. This clearly differentiates it from sibling tools like dataset_row or dataset_stats, which handle data access and analysis rather than attribution metadata.

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 cue: 'Read this to attribute a figure correctly.' This tells an agent when to invoke the tool. It does not explicitly name alternatives or exclusions, but the use case is distinct enough from the data-oriented sibling tools that 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?

No annotations are provided, so the description must carry the behavioral disclosure burden. It does mention case-insensitive matching, which is useful, but it omits details such as whether multiple rows are returned, what happens when no row matches, or any pagination/limits.

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 no filler. The matching behavior and case-insensitivity are both included efficiently, though the sentence is slightly awkward as a noun phrase rather than an active statement.

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 with no annotations and no output schema, the description is mostly adequate: it names the dataset, the matching rule, and case-insensitivity. However, it leaves out usage routing and edge-case behavior, so it is only minimally 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 needs to explain the parameters. It does map 'column' and 'value' conceptually by saying 'where a column equals a value,' providing minimal but real semantic meaning beyond the bare 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 title clearly states 'Look a row up by an exact key' and the description explains that rows are returned when a column equals a value exactly, with case-insensitivity. This identifies the tool's function well, though it does not explicitly contrast with sibling tools like 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?

There is no explicit guidance about when to use this tool versus alternatives such as dataset_search or dataset_compare. The exact-match phrasing implies a use case, but the description does not state conditions, exclusions, or recommended alternatives.

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 usefully discloses case-insensitive matching and the 50-row cap, but does not mention ordering, pagination, or default limit behavior. For a read-only search tool, the disclosed traits are meaningful but not exhaustive.

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 with no filler. It efficiently conveys the matching rule, case sensitivity, and result 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 adequately conveys core behavior and constraints. It could be more explicit about return format and default limit, but an agent can correctly select and invoke the tool based on the given information.

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 query as 'text to look for in any cell', and the description adds case-insensitivity. The limit parameter has no schema description, but the description's 'up to 50' indirectly aligns with its maximum. With 50% schema coverage, the description partially compensates but does not fully define limit's default behavior.

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 operation: return rows from the Sudslane dataset whose cells contain the query, case-insensitively, up to 50. This clearly distinguishes it from sibling tools like dataset_stats or dataset_top by focusing on content-based 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 clear context for when to use the tool: when you need rows matching a substring query across cells. However, it does not explicitly mention sibling tools or conditions where an alternative would be preferable.

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 does well by stating that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It does not explicitly state that the operation is read-only, but that is strongly implied by the nature of the 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?

The description is a single, efficiently written sentence that front-loads the computed metrics and then adds the important formatting and exclusion behavior. Every phrase contributes useful information with no redundancy.

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 statistics tool with no output schema, the description is complete: it defines the input, lists the output metrics, and clarifies how special values are handled. An agent has enough information to select and invoke the 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 schema only says the parameter 'column' is a non-empty string, giving no semantic meaning. The description compensates by specifying that the column must be numeric and belong to the Sudslane dataset, which is essential guidance for choosing a valid value.

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 operation (computing summary statistics) and the specific resource (the Sudslane dataset), and the list of metrics makes its function concrete. This clearly distinguishes it from siblings like dataset_search or dataset_row, which serve different purposes.

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 use case is implied: use this tool when you need count, min, max, mean, median, or sum for a numeric column. However, it does not explicitly mention when to prefer this over alternatives like dataset_top or dataset_search, nor does it provide any exclusions or prerequisites.

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 this burden; it does convey the core behavior (highest/lowest rows, numeric column). It does not mention behavior like non-numeric column handling or whether results are limited beyond the schema's limit property, but the operation is clearly a read-only ranking and the title reinforces that.

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 communicates the operation, dataset, and use case with no filler. The conversational quote earns its place by clarifying the intended question type.

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 low-complexity tool, the schema covers required column, limit range, and default direction, while the description adds numeric semantics and the most/least framing. It is adequate but not complete: with no output schema or annotations, it leaves return-value shape and edge cases (non-numeric column, limit effect) to 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 only 33%, so the description must compensate; it adds the important 'numeric column' constraint and relates ascending to highest/lowest. It does not explain the limit parameter or its effect beyond the schema's min/max values, leaving a partial gap.

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 that the tool returns the highest or lowest rows of the Sudslane dataset by a numeric column, which is a clear verb+resource+scope. The quoted 'most/least X' reinforces the ranking purpose, but it does not explicitly contrast with siblings such as 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 quoted 'which is the most/least X' implies a ranking use case and gives the agent a way to map questions to this tool. However, there is no explicit statement of when to choose dataset_top over sibling tools or 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.