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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 behavioral burden. It discloses what the agent will receive and frames the call as a schema-learning step, implying a safe read-only metadata operation. It stops short of explicitly stating that there are no side effects or permissions required, but such concerns are minimal for a parameterless 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?

Two short sentences with no wasted words: the first lists the output contents, and the second gives the usage directive. Nothing is redundant with the title, and the most actionable instruction is 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 parameterless introspection tool, the description covers the core return contents and the intended first-call behavior. The exact return shape is not specified, but the low complexity and the described output make the tool usable even without an output schema.

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 accepts zero parameters, so the description does not need to explain any argument semantics. The baseline 4 applies, and the description adds no unnecessary parameter-related detail.

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 returns—column names, numeric flags, row count, and provenance banner—so the agent knows what to expect. It is positioned as the schema-discovery entry point among siblings, though it lacks a strong imperative verb like 'list' or 'get' and does not explicitly contrast with dataset_provenance despite overlapping banner content.

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 tells the agent to 'Call this first to learn the schema,' providing a clear when-to-use signal. It does not mention when to use alternatives, but for a first-step introspection tool the intended placement is fairly obvious.

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 discloses a key behavioral trait: results are returned in the order of the input values. It also implies a read-only operation. However, it does not specify the output format (e.g., full rows or selected columns) or any error handling, leaving some ambiguity for a tool with no annotation coverage.

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 that front-loads the core action and the ordering guarantee. There is no filler or redundant information, making it highly efficient.

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 (2 required params, no output schema, no annotations), the description provides the essential information to call it correctly: what it returns and the ordering. However, it does not mention any limitations beyond the schema, nor does it explain the expected output format in detail, leaving some room for 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 coverage is 0%, so the description must compensate. It clarifies that 'column' is the field to match and 'values' are the set of accepted values, with the OR semantics ('any of'). It also explains the ordering behavior. However, it does not elaborate on value matching details (e.g., case sensitivity) or the purpose of the min/max constraints, which are only in the 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 the tool returns rows from the Ninebix dataset where the given column matches any of the provided values, preserving the order of the values. It explicitly frames the use case as 'X vs Y' questions, which distinguishes it from siblings like dataset_search (full-text search) and dataset_row (likely a single row lookup).

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: use for comparing specific values side by side ('X vs Y' questions). However, it does not explicitly name alternative tools or state when not to use it, so it lacks explicit exclusions but gives a solid usage scenario.

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 tool's output contents (source, date, licence, citation) and frames it as a read operation with 'Read this.' For a zero-parameter provenance tool, this is reasonably transparent, though it does not specify return formatting or whether values are static.

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 with no filler. The key output fields are listed up front, and the usage instruction is concise and actionable.

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, parameterless provenance tool with no output schema, the description fully lists what the agent will receive and when to use it. The title reinforces the purpose, so nothing essential is missing for correct selection and 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 no parameter documentation is needed. The description does not need to compensate for schema gaps, and the baseline of 4 is appropriate.

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 tool's resource (the Ninebix dataset) and the specific information returned: source, computation date, licence, and citation. It is clearly distinct from sibling tools like dataset_stats or dataset_search, which address data contents rather than 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?

The description explicitly instructs when to use the tool: 'Read this to attribute a figure correctly.' This provides a clear use context, though 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?

The core behavior is described (filter rows by equality), but it does not disclose return format, error handling, or side effects. With no annotations, the description carries the burden, yet it omits details like what happens if the column does not exist or if multiple rows match.

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 that immediately conveys the tool's purpose and key matching semantics without 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 read operation, the description is largely sufficient: it specifies the dataset, matching condition, and case-sensitivity. However, it could mention explicitly that multiple rows may be returned or that the result is a collection, though 'rows' implies this.

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 references both parameters ('column' and 'value') contextually, but schema coverage is 0% and no explicit parameter descriptions are given. It implies 'column' is a column name and 'value' is the match target, but does not clarify types, allowed values, or validation 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 clearly states the tool retrieves rows from the Ninebix dataset where a specified column exactly matches a given value, with case-insensitivity noted. This distinguishes it from sibling tools like dataset_search and 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 implies usage for exact-match lookups but does not explicitly mention when to avoid it or alternative tools. The phrase 'exactly' and 'case-insensitive' hint at differentiation from fuzzy search, but no direct guidance is provided.

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 burden of behavioral disclosure. It reveals the matching rule (case-insensitive) and the result cap (up to 50), which are useful. However, it does not mention whether the operation is read-only, what happens with zero matches, or if there is any default ordering. Some transparency is present, but 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 a single, focused sentence with no redundancy. It front-loads the core action (searching rows) and immediately provides essential constraints (case-insensitivity and limit). Every word adds value.

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 only two parameters and no output schema, the description covers the main behavior but leaves ambiguities: the default limit when the optional 'limit' parameter is omitted, the structure of returned rows, and any error handling. Given the low complexity, this is acceptable 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 50%: only the query parameter has a description in the schema, while limit does not. The tool description hints at the limit's role with 'up to 50', adding some semantics beyond the schema, but it does not explicitly explain the limit parameter or clarify default behavior. This partial compensation justifies a baseline 3.

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: returning rows of the Ninebix dataset that contain a query string, with specific details on case-insensitivity and a maximum result count. This distinguishes it from sibling tools like dataset_stats (statistics) or dataset_columns (schema), making the 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 implies when to use it (searching dataset rows by content) but does not explicitly mention alternatives or exclusion criteria. An agent can infer it is for content-based lookup, but there is no explicit guidance on when to prefer other tools like dataset_top or 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?

With no annotations, the description carries the full burden. It discloses important behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This goes beyond the basic operation, though edge cases like missing columns are not addressed.

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 that front-loads the computed statistics and packs preprocessing notes into a parenthetical; no fluff.

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 with no output schema, the description covers the operation, preprocessing, and numeric constraint. It does not spell out the return format or error behavior, but those are not critical for 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?

Schema coverage is 0% and the only parameter, `column`, has no description in the schema. The tool description adds that it must be a numeric column and implies a column name from the Ninebix dataset, providing enough semantic context for the single parameter.

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 (numeric column of the Ninebix dataset) and the exact operations (count, min, max, mean, median, sum), making its purpose unambiguous and clearly distinct 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?

No explicit when-to-use or alternatives are given; the description implies use for computing summary statistics but doesn't contrast with sibling tools or 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?

No annotations are provided, and the description does not clarify read-only status, side effects, or the exact structure of the returned rows. It only hints at sorting behavior via the ascending parameter, leaving operational details unclear.

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, directly to the point, with no redundant words or filler. It efficiently conveys the core function.

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 lacks an output schema and does not mention whether it returns entire rows or just column values. It gives enough for basic usage but leaves some ambiguity about the result structure.

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 33% (only ascending has a description). The description adds context that 'column' must be numeric, which helps, but does not explain limit semantics or the output format, and the schema remains minimal.

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, either highest or lowest, and even provides a natural language query example. This distinguishes it 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?

The description implies usage when needing top or bottom rows by a metric, but does not explicitly mention when to avoid this tool in favor of siblings. There is no direct comparison to dataset_stats or dataset_row.

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