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

With no annotations, the description carries the burden of behavioral disclosure. It explains what the tool returns and that it is the first call for schema learning, which implies a read-only metadata operation. However, it does not explicitly state that it has no side effects, requires no authentication, or describe any error/edge-case behavior. Basic but not rich behavioral context.

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 short sentences, front-loads the tool's output, and ends with a clear action directive. Every word earns its place, and there is no redundant restating 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 simple, zero-parameter metadata tool with no output schema, the description covers the essential return components and tells the agent when to call it. It could be slightly more precise about the output structure or what the provenance banner contains, but the low complexity and clear 'call this first' guidance make it 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?

The tool has zero parameters and an empty input schema, so there is no parameter ambiguity to resolve. The description reinforces that the call needs no input. A baseline of 4 is appropriate for a zero-parameter tool.

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 the tool provides: columns, numeric flags, row count, and provenance banner for the Kbasevo dataset. It stops short of an explicit verb like 'returns' or 'lists', but 'Call this first to learn the schema' makes the purpose unambiguous and distinguishes it from sibling tools that compare, search, or summarize.

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 to 'Call this first to learn the schema' provides clear usage context and establishes ordering relative to other tools. It does not explicitly say when not to use it or name alternatives, but the sequencing guidance is strong enough for an agent to know this is the schema-discovery entry point.

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 reveals the filtering rule and ordering behavior, which is useful, but it does not describe the output format, how 'side by side' is represented, or edge cases like missing or duplicate 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?

A single sentence with no filler; the core behavior and intended use are front-loaded. Every phrase contributes to understanding.

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 and the description covers the selection logic, but with no output schema and no annotations, an agent may still be unsure about the exact return shape and how the side-by-side comparison is presented. More detail about output or sibling relationships would strengthen 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 coverage is 0%, and the description compensates by defining the roles of both parameters: 'column' is the field to match against, and 'values' are the allowed values whose order affects output order. This adds meaningful semantics 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 description states a concrete behavior: return rows from the Kbasevo dataset where a column matches any of the provided values, in the given order. This clearly distinguishes it from plain row retrieval, though it does not explicitly contrast 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' provides a clear use context for comparison-style queries. It does not explicitly say when not to use it or name alternatives, but the intended scenario is evident.

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; 'Read this' signals a read-only retrieval, and the listed contents (source, computed date, licence, citation) disclose what the agent will get. It does not address output formatting or potential staleness, but that is minor for a zero-parameter metadata 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 one compact sentence that front-loads the exact contents and follows with the purpose. No word is wasted and the title reinforces the message.

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 fully covers what is returned and why an agent would use it. The provenance fields are enumerated and the attribution purpose is stated, so no critical guidance 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?

The tool has zero parameters, so the description need not explain parameters. The baseline of 4 applies.

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 and title clearly identify the tool as the provenance/metadata view for the Kbasevo dataset, naming the specific fields it covers (source, computed date, licence, citation). It is easily distinguished from sibling tools that operate on columns, rows, searches, and stats, though it lacks a direct operative verb such as 'retrieves'.

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?

'Read this to attribute a figure correctly' gives a clear use case for when an agent should call this tool. It does not explicitly enumerate when not to use it or compare it to sibling tools, but no alternative is really applicable for citation/provenance needs.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does disclose the core matching behavior, including case-insensitivity, but does not mention whether this is a read-only operation, how many rows may be returned, what happens on no match, or any error/rate-limit 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 a single sentence with no filler, no repetition of schema metadata, and the most important scoping detail (exact equality, case-insensitive) is front-loaded. Every word 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, but with no annotations and no output schema, the description should provide more context about return behavior and when this lookup tool is preferred over dataset_search. It is adequate for basic invocation but leaves an agent inferring several practical details.

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 by explaining both parameters. It loosely maps 'column' and 'value' to the equality predicate, but it does not explain how column names are specified, what value formats are allowed, or whether column names are case-sensitive. The parameter names themselves are doing most of the work.

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 Kbasevo dataset) and the filtering behavior (column equals value exactly, case-insensitive). It does not explicitly name a verb like 'returns' or 'looks up', and it does not explicitly distinguish itself from sibling tools, though the 'exactly' phrasing hints at contrast with 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 exact-match lookup but gives no explicit guidance about when to use this tool versus alternatives like dataset_search. It does not state any exclusions, prerequisites, or conditions that should route an agent to dataset_row.

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 present, so the description carries the full disclosure burden. It discloses useful behavioral traits: case-insensitive matching, substring containment in any cell, and a 50-row cap. However, it omits other behavioral context such as the default limit when none is passed, result ordering, and behavior on zero matches.

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 14-word sentence with zero filler. Every element — subject, matching semantics, case behavior, and row cap — carries information, and the most important facts are front-loaded.

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 or annotations, the description covers the core semantics well. Gaps remain: whether 50 is the default when limit is omitted, whether results are ordered, and the exact shape of returned rows.

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 50%: query is already described as 'text to look for in any cell', and the description adds case-insensitive matching and the 'up to 50' cap, which clarifies the limit parameter's effect. The limit parameter still lacks a direct description, but its schema constraints (min 1, max 50) combined with the description's cap statement make its role reasonably clear.

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 specifies a concrete operation: retrieving rows of the Kbasevo dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. It is clear about the resource and semantics, and is conceptually distinct from siblings like dataset_stats, dataset_columns, and dataset_provenance, though it does not name them explicitly.

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?

No guidance is given on when to choose dataset_search over its siblings or when not to use it. The name and semantics imply it is the full-text row-filter tool among the siblings, but there is no explicit routing, prerequisite, or exclusion information.

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 and does so well: it reveals that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. It does not describe exact error behavior or output structure, but for a read-only statistics tool these are meaningful additions.

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: the computed statistics are front-loaded, followed by the preprocessing behavior. Every clause earns its place by informing the agent what to expect.

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 aggregation tool with no output schema, the description covers the input domain, the computed fields, and handling of non-numeric rows. The main omissions are the explicit return format and behavior for missing or invalid columns, but these are minor for a tool this simple.

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 only defines `column` as a required string with no description, so the tool description must supply meaning. It clarifies that the column should be numeric and that comma/currency formatting is handled, which helps an agent pass an appropriate value. It stops short of specifying whether the value is a header name or whether matching is case-sensitive.

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 and resource: it computes count, min, max, mean, median, and sum for a numeric column of the Kbasevo dataset. This clearly separates it from sibling tools like dataset_row, dataset_top, and dataset_search, which have different retrieval 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 description implies usage when aggregate statistics for a numeric column are needed, and the listed statistics provide clear context. However, it does not explicitly say when to prefer this tool over alternatives or mention any exclusions, so the agent must infer the usage boundary.

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 explain the ranking direction (highest or lowest) and the numeric-column requirement, but it does not mention what happens with invalid or missing values, the default limit behavior, or the shape of the returned rows.

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 useful paraphrase 'which is the most/least X' is included. It is compact and front-loaded with the core behavior, though the dash construction is slightly awkward.

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 ranking tool, the description covers the essential behavior and invocation intent. But with no output schema and no annotations, it would benefit from stating the return format and any default limit behavior, so the context is adequate but not fully specified.

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 needs to compensate. It adds useful meaning for the required 'column' parameter by specifying that it must be numeric, and it reinforces the meaning of 'ascending' via highest/lowest wording. However, it says nothing about the 'limit' parameter, leaving part of the parameter meaning to inference from the property name and schema 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 that the tool returns the highest or lowest rows of the Kbasevo dataset by a numeric column, and the quoted 'which is the most/least X' question captures the intended use. It is distinct from siblings like dataset_stats or dataset_row, though it does not explicitly name any 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?

The description implies when to use the tool: when asking a 'most/least X' ranking question about a numeric column. However, it gives no explicit guidance about when not to use it or which sibling tool to prefer instead, such as dataset_stats for aggregate summaries or dataset_search for filtered access.

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