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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 does state exactly what information is returned and implies a read-only schema inspection, which is helpful. However, it does not mention return format, potential latency, access requirements, or any edge cases, though the tool is simple enough that these are minor gaps.

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 concise sentences convey the full payload, the target dataset, and the recommended invocation order. The most important guidance ('Call this first') appears at the end of the second sentence, but the description is otherwise tight and free of 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 schema-inspection tool, the description gives sufficient context: what data to expect, which dataset it refers to, and when to call it. It could more clearly separate its provenance-banner output from the sibling dataset_provenance tool, but nothing critical 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 documentation burden. The description correctly implies that no input is needed, and the schema confirms this with an empty properties block.

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 what the tool exposes: columns, numeric flags, row count, and provenance banner for the BurdenRateLedger dataset. It also indicates the tool's role as a schema-learning entry point, which distinguishes it from most siblings, though it lacks an explicit verb like 'list' or 'get'.

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?

'Call this first to learn the schema' is a direct usage instruction that establishes when this tool should be invoked relative to the others. It does not explicitly name alternatives or exclusions, but with zero parameters and a clear schema-introspection role, 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 two important behaviors: rows are selected when the column value is any of the provided values, and the result order follows the order of the given values. With no annotations provided, the description carries the burden of behavioral disclosure, and it does not explain matching semantics (exact match vs. substring), return format, or potential limitations.

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 entire description is one compact sentence that conveys the dataset, matching rule, ordering, and use case without repeating the title. The structure is slightly awkward because it starts with a noun phrase rather than a verb, but it is efficient and free of 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 tool, the description covers the core selection logic, ordering, and intended use case. However, with no output schema and no annotations, important details such as the return shape, whether the output is truly 'side by side,' and exact-match behavior are absent. The description is adequate 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?

The description maps both parameters to their roles: 'column' is the field being matched, and 'values' are the allowed values to match against. It also clarifies that the order of 'values' determines the output order. Since schema description coverage is 0%, this is valuable parameter-level context, though exact-match behavior is not explicitly confirmed.

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 identifies the resource (BurdenRateLedger) and the operation: selecting rows whose column matches any of the given values. It also signals its comparison use case with "for X vs Y questions," which helps distinguish it from siblings like dataset_row or dataset_search. There is no explicit verb like "returns," but the intent is clear.

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 context for when this tool is appropriate. However, it does not mention alternatives, such as using dataset_search for broader queries or dataset_row for single-row lookup, nor does it state when not to use it. The usage guidance is implied 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?

No annotations are provided, so the description carries the behavioral burden. It discloses exactly what information will be returned — source, computed date, licence, and citation — and frames the tool as read-oriented ('Read this'). It does not explicitly state side-effect-freedom, but for a metadata lookup tool the disclosure is fairly complete.

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, efficient sentences with no filler. It front-loads the key content elements and ends with a direct usage instruction, making it easy for an agent to parse quickly.

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 lookup, the description is complete: it names the dataset, lists the provenance fields, and gives the intended use case (attribution). No output schema exists, but the description sufficiently explains what the agent will get.

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 no parameters, and the schema coverage is 100%, so there are no parameter semantics to clarify. The description adds useful context by naming the specific dataset, but this is a baseline-4 situation 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 identifies the tool as providing provenance metadata for the BurdenRateLedger dataset: source, computation date, licence, and citation. It names a specific resource and an actionable verb (attribute), and it is easy to distinguish from sibling data-manipulation tools like dataset_columns 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 explicitly states when to use this tool: when a figure needs to be attributed correctly. It does not explicitly list when not to use it or name alternatives, but the use case is concrete and the sibling tools are clearly different in purpose.

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 disclosure burden. It does add useful behavioral context by specifying exact matching and case-insensitivity. However, it does not state whether all matching rows are returned, what happens when no row matches, whether there are limits, or what the output shape is. The disclosure is partial.

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 dataset context and the matching rule. It earns its place, though it could be slightly more informative without becoming 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?

For a simple lookup tool, the description covers the core query semantics, but completeness gaps remain: no return format, no behavior for missing values, and no comparison against sibling tools. The lack of annotations and output schema raises the responsibility on the description, which it only partially fulfills.

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 for the schema's lack of explanatory text. It does so by clarifying that 'column' is the field to match and 'value' is the exact value to compare, with case-insensitivity as an added qualifier. For a simple two-string-parameter tool, this meaningfully clarifies both parameters, though it omits examples or formatting details.

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 a row-level lookup operation on the BurdenRateLedger dataset with exact, case-insensitive column matching. The title adds a verb ('Look a row up') and resource ('row'), making the intent unambiguous. It is distinguishable from siblings like dataset_columns and dataset_stats, though it does not explicitly name a differentiating 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 this tool: when you need rows where a column equals a value exactly. However, it provides no explicit guidance on when not to use it, no mention of sibling alternatives, and no context such as 'use dataset_top for aggregations' or 'use dataset_compare for comparisons.' The usage context is present but only implicit.

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 full behavioral burden. It does disclose case-insensitive matching, full-cell search scope, and the 50-row cap, which is useful. However, it omits details like the default limit when not provided, result ordering, handling of zero matches, and whether the operation is purely read-only.

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, dense sentence that front-loads the core behavior and packs in matching scope, case sensitivity, and result cap. Every word earns its place with 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 search tool, the description conveys the essential behavior and return shape (rows). Minor gaps remain, such as the default limit, ordering, and empty-result behavior, but nothing critical is missing for a competent agent to invoke this 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 50%: the query parameter is already described in the schema, and the description adds the case-insensitive behavior tying to it. The limit parameter has no schema description, and the description only indirectly addresses it via 'up to 50' — it does not explain defaults or the effect of lower limit values. The description partially compensates but does not fully cover the 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 description states exactly what the tool does: it returns rows of the BurdenRateLedger dataset matching a query, with case-insensitive cell matching and a cap of 50. The resource, matching behavior, and result scope are all specified, distinguishing it from sibling tools like dataset_row, dataset_stats, and 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 description implies when to use the tool (when you need rows containing arbitrary text), but it never explicitly names alternatives or states when-not-to-use it. An agent can infer the use case from the described behavior, but there is no routing guidance relative to 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 provided, the description carries the full responsibility for behavioral disclosure. It usefully notes that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which goes beyond the schema. It does not mention error behavior or what happens if the column is missing, but the disclosed behaviors are meaningful.

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 list of statistics, names the dataset, and appends important caveats in a parenthetical. Every part earns its place with no redundancy or unnecessary verbosity.

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 low-complexity, one-parameter tool with no annotations and no output schema, the description is fairly complete: it identifies the dataset, the column type, the returned statistics, and data-cleaning behavior. It lacks an explicit output shape or error semantics, but these are not critical for such a simple tool and can be inferred.

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 string with minLength 1 and no description, so schema description coverage is 0%. The description compensates by specifying that the column must be numeric and that formatted numbers (commas/currency) are handled. For a single parameter, this adds sufficient 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 explicitly states the operation (computing count, min, max, mean, median, and sum) and the resource (a numeric column of the BurdenRateLedger dataset). This clearly differentiates it from sibling tools like dataset_row or dataset_search, which serve different 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 use case is implied by the title and description: use when summary statistics of a numeric column are needed. However, the description does not explicitly contrast this tool with sibling alternatives or state when not to use it, leaving the guidance implicit 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'highest (or lowest) rows' and does not mention default limits, tie handling, handling of non-numeric or missing values, output format, or performance implications.

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 short and front-loaded with the core purpose, and every phrase contributes to identifying the operation. It is slightly redundant with the title, but the added dataset name and the 'most/least X' framing provide useful specificity.

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 three parameters and no output schema or annotations, the description is minimally adequate: it names the dataset, the ordering basis, and the high/low choice. However, it lacks details about default limit values, output shape, and edge-case behavior, which an agent might need when invoking 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?

Schema description coverage is only 33% (only the ascending parameter has a schema description). The description compensates somewhat by specifying that the column must be numeric and by linking ascending to 'lowest first' versus 'highest first', but it leaves the 'limit' parameter's meaning and default behavior unexplained.

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 'Rank rows by a numeric column' and the description together clearly state that the tool returns the highest or lowest rows of a specific dataset by a numeric column. It names the resource (BurdenRateLedger) and the ordering concept, though it does not explicitly contrast itself with 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 description implies when to use it ('which is the most/least X'), but it gives no explicit guidance on when to choose this tool over sibling tools like dataset_search or dataset_stats. There are no exclusions, alternative references, or use-case boundaries beyond the general 'top/bottom rows' idea.

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