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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 it does well by enumerating the exact output contents: columns, numeric columns, row count, and provenance banner. This makes it clear the tool is a read-only metadata/schema inspection call, even though it does not explicitly state 'no side effects' or describe formatting.

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 compact sentences deliver all essential information: what the tool returns and when to call it. There is no filler, and the usage instruction is front-loaded after the output summary.

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 introspection tool with no output schema, the description adequately covers the return values and the intended usage order. It could additionally mention whether the result is a list or a structured object, but this is a minor gap given the simplicity of the tool.

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 per the baseline this dimension deserves a 4. The description adds useful context about the fixed dataset ('Offdayly') and the kind of information returned, which is more than the empty schema alone provides.

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 exposes: columns, numeric flags, row count, and provenance banner for the Offdayly dataset. It stops short of naming sibling tools or explicitly contrasting itself with dataset_provenance and dataset_stats, but the 'learn the schema' framing sets it apart as the schema-discovery entry point.

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 'Call this first to learn the schema' gives clear, actionable guidance on when to use this tool. It does not explicitly discuss when not to use it or name alternatives, but the sequencing advice is strong enough to route an agent correctly.

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 available, the description carries the behavioral disclosure burden. It does disclose the key selection and ordering behavior, but it does not explicitly state that this is a read-only operation, whether matching is exact/case-sensitive, or what the returned rows look like beyond the ordering.

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 core behavior and then adds the targeted use case. Every part contributes meaning, and there is no redundant 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 simple two-parameter read tool with no output schema, the description is largely complete: it names the dataset, defines filtering and ordering behavior, and gives the intended comparison use case. It falls just short of describing the exact output presentation, though the title helps with that.

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 usefully explains that 'column' is the dataset column to match against and that 'values' controls both the match set and the output row order, giving real semantic meaning beyond the raw 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 specific action and resource: it returns rows of the Offdayly dataset where a column matches any of the provided values, in a specified order. This clearly distinguishes it from siblings like dataset_row (a single row), 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 gives a clear usage context: it is meant for 'X vs Y' questions, where you want selected rows side by side in a given order. It does not explicitly name alternatives or state when not to use it, but the intended scenario is clear 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 provided, the description carries the behavioral burden. It signals a read-only action ('Read this') and enumerates the exact fields returned, which is meaningful transparency for a metadata lookup tool. It does not discuss output formatting, but that is a minor gap for such a simple 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 contain all essential information, with the data fields front-loaded and the usage guidance following immediately. 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 zero-parameter metadata lookup with no output schema, the description is complete: it identifies the dataset, lists the returned provenance fields, and explains why an agent should invoke it. No critical information 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 schema already fully covers parameter semantics. The description adds no parameter information, but none is needed; 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 states a specific purpose: returning the source, computed date, licence, and citation for the Offdayly dataset. This distinguishes it from the sibling dataset tools, which focus on columns, rows, search, stats, and 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 instruction 'Read this to attribute a figure correctly' explicitly identifies when the tool should be used. It does not name alternative tools or exclusion conditions, but the zero-parameter provenance scope makes the intended use unambiguous.

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 does disclose that matching is exact but case-insensitive and that multiple rows may be returned. However, it omits important behavior such as whether all matches are returned, what happens on no match, and the output format.

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 with no filler. It front-loads the core lookup behavior and the case-insensitivity caveat. Minor inconsistency: the title says 'a row' while the description says 'rows,' which could cause slight confusion.

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 is mostly usable, but it lacks enough context for an agent to fully rely on it. There is no output schema, no note about single vs. multiple results, and no mention of edge cases or sibling differentiation, making the tool's complete behavior underspecified.

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 has 0% description coverage, so the description must clarify the parameters. It does convey that 'column' is the field to match on and 'value' is the exact value to match, but it adds little beyond paraphrasing the parameter names. More detail about valid column names or value formatting would be helpful.

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 dataset rows where a specified column equals a given value. The 'exactly (case-insensitive)' qualifier adds precision. However, it does not explicitly contrast with dataset_search, which appears to be a close sibling.

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 about when to choose this tool over dataset_search or other siblings. The agent must infer that this is for exact matching, but there is no explicit 'use this for exact, that for fuzzy/search' 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 burden of behavioral disclosure. It usefully discloses case-insensitive matching, cell-level containment, row-level output, and a 50-row cap. It does not mention ordering, pagination, or what happens when more than 50 rows match, but the core behavior is clearly communicated.

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, tightly worded sentence that front-loads the resource and search behavior. Every clause adds meaningful information: row output, cell matching, case-insensitivity, and the 50-row ceiling.

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, the description covers the essential matching semantics, but gaps remain: no result format is described, no ordering or pagination behavior, and no guidance about when to prefer sibling tools. The absence of an output schema and annotations makes these omissions more noticeable.

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 describes the query parameter as 'text to look for in any cell', and the description reinforces this with case-insensitive matching. However, the limit parameter has no schema description and the description only says 'up to 50', without explicitly tying this to the limit parameter or explaining its role.

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 action and resource: returning rows of the Offdayly dataset that match a query in any cell. This clearly differentiates it from siblings like dataset_row, dataset_stats, or dataset_top, since it is the only full-text row search tool.

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 primary use case: find rows containing a given text. However, it does not explicitly state when to avoid this tool or direct the agent to alternatives, such as dataset_row for a known row identifier or dataset_stats for aggregate insights.

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 disclosure burden and does reveal meaningful behavior: commas/currency are parsed and non-numeric rows are excluded and counted. But the meaning of 'counted' is ambiguous (separate count vs included in stats) and there is no mention of empty-column or missing-column behavior or return structure.

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 dense sentence front-loads the computed statistics and appends only necessary preprocessing caveats in parentheses. 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?

For a one-parameter read-only summary tool, the description covers core outputs and data-cleaning behavior. It is slightly incomplete because it doesn't clarify the counting semantics or describe the response shape, but the low complexity makes these gaps non-critical.

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 for the single 'column' parameter. It adds that the column is numeric and that formatted numbers will be parsed, but it doesn't specify exact column-name requirements, case sensitivity, or whether an existing column must be passed. The added semantics are helpful but incomplete.

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?

Description names a specific resource (numeric column of the Offdayly dataset) and enumerates concrete output metrics (count, min, max, mean, median, sum). It lacks an explicit verb like 'computes', but the stat list makes the operation unmistakable and helps distinguish it from siblings like dataset_top or dataset_columns.

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?

Usage context is implied: it is for numeric columns in the Offdayly dataset, and the note about commas/currency suggests it applies to formatted numeric data. However, it never names alternatives or states when NOT to use it (e.g., for non-numeric or top-N analysis), so an agent must infer the decision.

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 does explain the core behavior: retrieving rows sorted by a numeric column in either direction, and clarifies the column must be numeric. However, it does not describe output shape, tie handling, missing-value behavior, or other operational details.

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 a practical usage example. It front-loads the primary behavior, though the phrase "which is the most/least X" somewhat restates the first part of the sentence.

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 with no output schema and no annotations, the description gives the essential semantics but leaves some gaps: no example invocation, no explicit mention of the limit parameter, and no guidance on distinguishing from similar data-access tools beyond the stated use 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?

Schema description coverage is only 33%, so the description must compensate. It usefully clarifies that column is a numeric column and implies the ascending parameter controls whether the highest or lowest rows are returned. The limit parameter is not addressed in the description, though its schema constraints provide some guidance.

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 operation: return the highest or lowest rows of the dataset ranked by a numeric column, with the use-case phrasing "which is the most/least X". This distinguishes it from siblings like dataset_stats, dataset_search, and dataset_row.

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 use context: use this tool when the question is about the most or least values in a numeric column. It does not explicitly name alternatives or exclusion conditions, but the context is sufficiently clear.

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