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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 communicates the returned content concretely and signals a read-only, exploratory operation. It does not mention side effects or error behavior, but for a schema/columns tool these are low-risk and reasonably inferable.

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 sentences with no filler: the first lists the returned information, the second provides usage positioning. The essential 'call first' guidance is front-loaded and 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 schema-discovery tool with no output schema, the description is complete: it names the dataset, enumerates the returned information, and tells the agent when to call it. Nothing essential is missing 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?

The tool has zero parameters, so there is no parameter semantics to document. The description appropriately focuses on what the caller will learn rather than on inputs, matching the empty 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 clearly states what the tool returns: columns, numeric indicators, row count, and provenance banner. It identifies the specific dataset ('Threewayly') and positions the tool as a schema-discovery call, though it does not explicitly contrast it with siblings like dataset_provenance 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?

Provides clear usage guidance with 'Call this first to learn the schema,' telling the agent when in a sequence to invoke it. It does not explicitly list alternatives or exclusions, but the positional instruction is sufficient for a simple introspection tool.

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 does explain the core logic: row selection by column matching any of the values, and ordering according to the given values. It does not mention edge cases (e.g., no matches) or confirm read-only behavior, but the explanation is sufficient for this simple filter operation.

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, well-structured sentence with no redundant words. The title adds further clarity. It is appropriately concise for a tool of this simplicity.

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 tool's simplicity and the absence of an output schema, the description covers the essential aspects: what data is returned and in what order. It does not describe the output format or error behavior, but for a straightforward filtering operation this is not a significant gap. The sibling list provides additional context for disambiguation.

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 successfully conveys the meaning of both parameters: 'column' is the attribute to filter on, and 'values' are the set of values to match. It does not explicitly enumerate parameter constraints, but the schema already provides those, and the descriptive sentence makes the roles 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 clearly states the tool's function: it returns rows from the Threewayly dataset where a given column matches any of the provided values, preserving the given order. The title 'Compare rows side by side' reinforces the purpose, and the phrase 'for "X vs Y" questions' adds context that distinguishes it from row lookup or 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 gives a clear usage scenario ('for "X vs Y" questions'), which implies when to use this tool rather than a general search or single-row retrieval. However, it does not explicitly name alternatives or state when not to use it, so it falls short of fully explicit 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?

With no annotations provided, the description carries the behavioral burden. It frames the tool as a read operation ('Read this') and enumerates the returned content, which is useful. However, it does not explicitly disclose side effects, response structure, or access constraints, though for a zero-parameter metadata tool these risks are limited.

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, no filler. The most important information—source, date, licence, citation—is front-loaded, and the directive 'Read this to attribute a figure correctly' earns its place by stating the intended use.

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 tool with no output schema, the description covers the essentials: what data it concerns, what fields it returns, and why to call it. The only minor gap is not specifying exact value formats, but that is not necessary for correct invocation here.

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 is empty, so the parameter-semantics baseline is 4. The description appropriately adds meaning by naming the dataset and the output fields, even though there are no parameters to document.

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 resource (the Threewayly dataset) and the exact fields returned: source, computed date, licence, and citation. The title 'Where this data comes from, and how to cite it' plus the attribution use case clearly distinguishes it from the sibling data-manipulation 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?

'Read this to attribute a figure correctly' gives a clear and practical when-to-use condition. It does not explicitly mention alternatives or exclusions, but the sibling tools are different enough in purpose that the intended use 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?

The description discloses matching behavior (exact, case-insensitive equality) and implies a read-only lookup, but it does not mention result cardinality, ordering, error behavior, or handling of missing columns or 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?

One sentence, directly to the point, with no redundant wording.

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, the description gives the core semantics, but it lacks return-shape or edge-case details. The absence of an output schema makes this acceptable but not fully comprehensive.

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 only indirectly defines the parameters: 'column' is the field to match and 'value' is the exact comparison value. There are no per-parameter descriptions, types, or constraints beyond the schema's minLength.

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 returns rows where a specified column matches a value exactly, case-insensitively, and the title reinforces 'exact key'. It is distinguishable from a generic search, though it does not explicitly compare itself to 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?

No explicit guidance is given for when to choose this tool over sibling tools such as dataset_search. The exact-match language is a hint, but it does not state use cases or 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?

With no annotations, the description carries the behavioral burden. It discloses that matching is case-insensitive and returns up to 50 rows, which is useful. However, it does not mention return format, ordering, behavior when no rows match, or whether all columns are searched. This is adequate but not rich.

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 zero fluff. It front-loads the core action and the most important qualifiers (case-insensitive, any cell, limit). Efficient and to the point.

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 search tool with two parameters, the description covers the essential behavior: what it returns, how matching works, and the result cap. It lacks explicit mention of result ordering or pagination, but given the low complexity and absence of an output schema, it is nearly complete. Siblings like dataset_stats or dataset_top require more context, but this search tool is adequately described.

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 covers the 'query' parameter with a description ('text to look for in any cell') and limit has no description. The tool description adds 'case-insensitive' and 'any cell', which extends schema meaning. But it does not elaborate on the limit parameter beyond the implicit 'up to 50'. With 50% schema coverage, it partially compensates but leaves limit semantics to the schema's bounds.

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 a specific verb (search) and resource (dataset rows), and adds the key detail that matching is case-insensitive and applies to any cell. It distinguishes itself from siblings like dataset_row (which fetches a specific row) and dataset_stats (which summarizes).

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 it (to find rows containing a text query) but does not explicitly state alternatives or exclusions. It does not say 'use this instead of dataset_row when...' or mention when not to use it. The purpose is clear enough that an agent can infer usage, but explicit routing is absent.

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 disclosure burden. It usefully reveals that grouping commas and currency are handled and that non-numeric rows are excluded from stats but counted, which is valuable edge-case behavior. It stops short of describing error cases or exact output structure, but those are secondary here.

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 compact sentence front-loads the output statistics and then appends the parsing/non-numeric caveats in parentheses. There is no filler or 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?

Given one simple parameter, no annotations, and no output schema, the description covers purpose, the returned statistics, and important data-handling behavior. It does not specify the response format or edge cases like all rows being non-numeric, but the missing details are unlikely to prevent 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?

The schema only defines 'column' as a non-empty string, while the description adds that it refers to a numeric column and clarifies how values are normalized. With a single required parameter, the mapping from description to the 'column' property is unambiguous.

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 which statistics are produced (count, min, max, mean, median, sum) and the specific resource they apply to (a numeric column of the Threewayly dataset). This clearly distinguishes dataset_stats from sibling tools that return columns, rows, search results, or comparisons.

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 intended use: call this when you need summary statistics for a numeric column. It does not explicitly name sibling tools or state when to prefer dataset_columns, dataset_top, or dataset_search instead, so the guidance is moderate 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?

With no annotations provided, the description carries the behavioral disclosure burden. It does convey the key behaviors: selection of highest/lowest rows by a numeric column. However, it omits defaults (e.g., limit), tie-handling, and details about the returned rows, so significant gaps remain.

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 very brief and front-loaded with the core purpose. No words are wasted, though the 'Threewayly' reference could confuse agents expecting a generic dataset tool. Overall, it is efficient but not overly 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?

Given there is no output schema and no annotations, the description is too thin to fully support correct invocation. An agent cannot determine what happens when limit is omitted, whether full row objects are returned, or how to handle non-numeric columns. More behavioral and parameter context is needed.

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%, and the description adds only partial semantics: it clarifies the column must be numeric and hints at ascending/descending order. The limit parameter is completely unexplained, and the schema gives no description for column or limit. The description does not compensate for the low coverage.

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 and description clearly state the tool ranks rows by a numeric column, returning the highest or lowest values. The 'which is the most/least X' phrasing gives a concrete semantic, and the tool is distinct from siblings 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?

No guidance is provided about when to use this tool versus alternatives such as dataset_stats or dataset_row. There is no mention of preferred use cases, exclusions, or relationships to sibling tools.

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