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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 of behavioral disclosure. It accurately describes a read-only metadata query returning schema information and a provenance banner, with no side effects implied. It lacks an explicit 'does not modify data' statement, but the informational nature is clear from the description.

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 that front-loads the returned items before adding the usage directive. Every word contributes meaning, with no filler or repetition.

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 metadata tool with no output schema, the description fully covers what an agent needs: the exact return contents and the recommended invocation order relative to sibling tools. There is no missing operational detail.

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 does not need to compensate for missing parameter documentation. It adds value by explaining what the output will contain, which is above the baseline for a parameterless 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 enumerates the four concrete pieces of information returned: columns, which are numeric, row count, and provenance banner. It names the specific resource (Dividvo dataset) and distinctively positions itself as the schema-learning tool, differentiating it from the sibling dataset_* 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 instruction 'Call this first to learn the schema' explicitly indicates when to use this tool and implies it should precede other dataset operations. It does not name alternatives or exclusions, but the 'first' directive provides sufficient usage context for an agent to select it appropriately.

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 key behavior—filtering by 'any of the given values' and returning rows 'in the order given'—but it does not mention exact-match semantics, behavior on no matches, or the output row shape, especially since no output schema exists.

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 states the result, the filtering rule, the ordering behavior, and the intended use case. 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?

For a simple two-parameter read tool, the description provides enough to invoke it: what rows are returned, how they are filtered, and how they are ordered. Remaining gaps such as no-match behavior and output shape are minor, and valid columns can be discovered via the sibling dataset_columns 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?

Schema coverage is 0%, so the description must add meaning. It links both parameters: 'column' is the field to filter on and 'values' are the values to match, and it adds the crucial detail that the values array order controls row order. It does not specify valid column names or value formatting, but the core semantics are covered.

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 Dividvo dataset where the given column matches any of the supplied values, preserving the supplied value order. The title 'Compare rows side by side' and the 'for X vs Y questions' tag distinguish it from sibling tools like 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 intended use case ('for X vs Y questions') and implies this tool is for comparing specific column values rather than searching or aggregating. It does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

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 signals a read-only action ('Read this') and lists the exact information returned, including licence and citation. Minor behavioral details like output format or error behavior are not covered, but they are low risk 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 a single sentence that names all key deliverables and then gives a practical usage instruction. It is compact, front-loaded, and contains no 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, parameterless provenance tool, the description is complete: it tells the agent what content will be returned, why the tool exists, and when to invoke it. No output schema is needed to understand the basic outcome.

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 the schema is already complete, so there is nothing for the description to add about parameter semantics. This meets the baseline for parameterless tools.

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 as the source of provenance metadata for the Dividvo dataset, listing exactly what it returns: source, computed date, licence, and citation. This distinguishes it from sibling tools that explore, search, or compare dataset 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 states when to use it: 'Read this to attribute a figure correctly.' It doesn't mention alternatives or exclusions, but the purpose is clear enough that an agent can distinguish it from the sibling tools.

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 usefully discloses case-insensitive exact matching and that rows are returned, but it does not clarify the singular/plural ambiguity between the title ('a row') and description ('rows'), nor what happens for zero or multiple 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?

The description is a single compact sentence that conveys the essential behavior without filler. It is appropriately sized and front-loads the resource and matching semantics.

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 tool, the description covers the core behavior, but it leaves some operational ambiguity around whether one row or multiple rows are returned and how exactness interacts with case-insensitivity in practice. No output schema or annotations exist to fill these gaps.

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 no parameter descriptions, so the description must add meaning. It relates 'column' and 'value' to the exact-match comparison and case-insensitivity, but it does not explain valid column references, value formatting, or matching edge cases.

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 'Look a row up by an exact key' names a specific action and resource, and the description clarifies that it returns dataset rows where a column exactly equals a value, case-insensitively. This clearly distinguishes it from broader search-like siblings such as 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 usage by emphasizing 'equals a value exactly (case-insensitive)', which hints at when to choose this tool over fuzzy search. However, it does not explicitly mention alternatives or state when not to use it, leaving the routing partly to inference.

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 case-insensitive matching, cell-level searching, and the 50-row cap. However, it does not mention outcome for no matches, result shape, or whether limit controls default vs maximum.

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 conveys the core behavior, search scope, case sensitivity, and result cap. Every word adds value 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 read-only search tool with two parameters and no output schema, the description plus schema is largely sufficient to invoke it correctly. The main omission is the return shape, but the description does convey that rows are returned, which keeps the tool usable.

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 explicitly, but limit has no description beyond its numeric bounds. The description adds the case-insensitive search semantics and 'up to 50' behavior, partially compensating for the 50% schema coverage, but it does not clarify the optional limit parameter's default or exact effect.

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 the resource (Dividvo dataset rows) and the matching behavior (cells containing the query), which distinguishes it from sibling tools like dataset_columns or dataset_stats. It is clear but does not explicitly name or contrast itself with a 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 use this search tool versus alternatives such as dataset_row, dataset_stats, or dataset_top. The description only explains what the tool does, not the intended use case or exclusions.

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 burden of behavioral disclosure. It goes beyond a generic statement by explaining that grouping commas and currency symbols are handled, and that non-numeric rows are excluded and counted. This meaningfully describes edge-case 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?

A single front-loaded sentence lists the output statistics first and then supplies the important data-cleaning caveats in a parenthetical. Every part is useful and there is no 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 one-parameter tool with no output schema, the description covers what is computed, the dataset scope, and how messy numeric values are handled. It could add an example or explicit output shape, but the essentials are present.

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, and schema coverage is 0%. The description adds that the column must be numeric and belong to the Dividvo dataset, and that formatted numeric values are tolerated. It does not specify exact naming, case sensitivity, or how to find valid column names.

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 a specific resource (a numeric column of the Dividvo dataset) and exactly what the tool computes: count, min, max, mean, median, and sum. This clearly distinguishes it from siblings like dataset_columns 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?

The intended use is implied: call this when you need summary statistics for a numeric column. However, it does not explicitly state when not to use it or identify alternatives, such as using dataset_top for row-level exploration or dataset_compare for comparisons.

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 the behavioral burden. It does disclose the core behavior: returning top/bottom rows by a numeric column, with an ascending option implied by 'highest (or lowest)'. However, it does not mention default limit behavior, what happens when limit is omitted, tie-breaking, or handling of non-numeric data, which are relevant for a ranking 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 front-loaded sentence with no filler. It efficiently communicates the primary function and the ranking question it answers, though the 'Dividvo dataset' phrasing and the em-dash aside are slightly cryptic.

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 no annotations and no output schema, the description is thin. It does not explain the limit behavior, nor does it provide guidance on choosing between dataset_top and closely related siblings like dataset_search, dataset_stats, or dataset_row. The core idea is present, but an agent is left without enough context to invoke it optimally in all cases.

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%, with column and limit lacking descriptions. The description adds meaning for column (numeric ranking column, 'most/least X') and partially for ascending, but it does not explain the limit parameter at all—its default, whether it is required, or what a missing limit yields. This leaves a significant parameter-semantics 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 title and description clearly state that this tool returns the highest or lowest rows of a dataset ordered by a numeric column, which is a specific verb+resource combination. It conveys the core ranking purpose and even gives an example question ('which is the most/least X'), though it does not explicitly distinguish itself from sibling tools like dataset_row or 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 the tool is for finding extreme values ('the highest or lowest rows' and 'which is the most/least X'), but it does not explicitly say when to prefer it over siblings such as dataset_search, dataset_stats, or dataset_row. There are no exclusions or alternative-suggestion statements, leaving the decision to inference.

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