site

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 describes the returned content (columns, numeric flags, row count, provenance banner) but does not explicitly state whether the operation is read-only or has side effects. The 'learn the schema' wording implies a safe introspection call, but this is not made explicit.

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 deliver the complete message: the tool's output and when to call it. There is no redundancy, restatement of the title, or filler; every phrase 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 tool with no output schema or annotations, the description covers the essential facts: what fields are returned and that it should be called first. An explicit read-only note would strengthen it, but nothing critical is missing given the low complexity.

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 schema coverage is trivially 100%. The description adds context about what the tool reveals, which is sufficient given that there are no parameter semantics to explain.

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 specifies the output: the Carbikly dataset's columns, which are numeric, the row count, and the provenance banner. The phrase 'learn the schema' establishes its unique role, though it does not explicitly contrast with sibling tools or use a strong verb like 'returns.'

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 to 'Call this first to learn the schema,' providing clear sequencing guidance for an agent. It does not name alternative tools or state when not to use it, so it lacks full when/when-not differentiation.

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 transparency burden. It discloses that rows are returned in the order of the given values and that matching is by 'any of' the values. It omits details like response format, exact-match semantics, and behavior when no rows match, so it is only partially transparent.

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 and packs the key semantics: target, filter, order, and purpose. It could be improved by adding a verb, but there is no wasted content.

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 without an output schema, the description covers the main query behavior. However, it is a noun phrase rather than a full statement of what the tool returns, and it does not address edge cases or result structure, leaving some 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 description coverage is 0%, so the description is the only source of parameter meaning. It effectively explains 'column' as the column to match and 'values' as the values to filter on, and adds the ordering semantics. It does not elaborate on whether matching is exact or case-insensitive, but the core meaning is 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 identifies the resource (Carbikly dataset rows), the filtering condition (column matching any of the given values), and the ordering behavior. However, it lacks an explicit verb like 'returns' or 'fetches', and it does not explicitly distinguish itself from sibling tools beyond the 'X vs Y' hint.

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 says the tool is 'for X vs Y questions', which provides a clear use case. It does not name alternatives or state when not to use it, so routing among the sibling dataset tools is left 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 provided, the description carries the full burden of behavioral disclosure. It transparently lists what information the tool returns, and the 'Read this' phrasing implies a non-mutating informational operation. It does not discuss side effects, but none are plausible for a provenance retrieval 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 compact sentence that immediately names what the tool returns, followed by a short actionable purpose. Every word earns its place, and the title reinforces the core idea without 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 zero-parameter, metadata-retrieval tool with no output schema, the description is complete: it names the dataset, lists the key provenance fields, and explains why an agent would use it. No critical information needed for correct invocation 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 and the schema already documents this fully with 100% coverage. Description adds meaningful context about what the returned provenance information contains, which is appropriate 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 states that the tool provides provenance metadata for the Carbikly dataset: source, computed date, licence, and citation. This is distinct from the sibling data-manipulation tools, though it does not explicitly name or contrast them.

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' gives a clear, concrete use case for when to invoke the tool. It does not state exclusions or alternatives, but for a metadata/citation tool this is a sufficiently clear context.

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 behavioral disclosure. It usefully discloses that matching is exact but case-insensitive and that results are rows, not necessarily a single row. It does not explain behavior on no match, whether the operation is read-only, or how duplicate matches are returned.

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 with no filler. It front-loads the resource (rows of the Carbikly dataset) and packs the matching rule and case-sensitivity into a compact, scannable statement.

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 core lookup behavior is covered, but there is no output schema and no annotations, so an agent must guess the returned row shape, empty-result behavior, and whether matching applies to any column or only a designated key column. This is adequate for a simple tool 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 0%, so the description must add meaning. It maps both parameters to their roles: `column` is the dataset column to compare and `value` is the value to match, and it adds the important case-insensitive equality semantics. It does not specify valid column names or value formats, but that is reasonable for two plain string parameters.

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 supplies a specific verb and resource ('Look a row up by an exact key'), and the description clarifies that the tool returns rows from the Carbikly dataset where a named column equals a provided value, with case-insensitive exact matching. The exact-match criterion also distinguishes it from sibling tools like dataset_search 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 description makes the matching condition explicit, so an agent can infer this tool is for exact value lookups rather than fuzzy search or aggregation. However, it never names alternatives like dataset_search, nor does it state when not to use this tool, leaving usage guidance 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?

With no annotations, the description carries the burden of behavioral disclosure. It usefully reveals case-insensitive matching and that any cell is searched, but it does not explicitly state that the operation is read-only, describe the return format, or mention ordering and empty-result 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 sentence with no filler. The core matching semantics are front-loaded and every clause adds information about the search scope or result cap.

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 no output schema or annotations, the description covers the query behavior and limit cap adequately. However, it omits the default limit value, exact return shape, and any guidance on sibling-tool tradeoffs, leaving minor but real 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 documents query, and the description adds the case-insensitive matching behavior for it. Limit has no schema description, and 'up to 50' only restates the schema maximum, so the parameter's default and effect if omitted are not clarified.

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 search behavior: rows of the Carbikly dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This is specific in verb and resource, but it does not explicitly distinguish itself from sibling tools such as 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 guidance is given about when to choose dataset_search over its siblings, such as dataset_row for a specific row or dataset_stats for summaries. The use case is implied by the name but not stated as a decision rule.

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 behavioral burden. It usefully discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not mention edge cases such as an empty column or output shape, but it provides meaningful behavioral context beyond the schema.

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, dense sentence that front-loads the computed statistics and then adds the important data-handling behaviors. Every clause earns its place, with no redundant information or padding.

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 single-parameter tool, the description is largely complete: it specifies the resource, the exact statistics returned, and key preprocessing behaviors. It does not detail the output schema, but since no output schema exists, the listed statistics provide a reasonable expectation of the result. Minor gaps like behavior on missing or all-non-numeric columns are not covered, but they are not critical for basic 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 input schema has only a single 'column' string parameter with no description, so schema coverage is 0%. The description compensates by explaining that the column must be numeric and that formatting artifacts like commas and currency are normalized. This gives the agent the essential semantic constraint needed to invoke the tool correctly.

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 specific resource (numeric column of the Carbikly dataset) and enumerates the exact statistics computed (count, min, max, mean, median, sum). This clearly differentiates it from sibling tools like dataset_row, dataset_top, and dataset_search, making the tool's function 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 the tool—when aggregate summary statistics for a numeric column are needed—but it contains no explicit guidance about when not to use it or which sibling tool to prefer (e.g., dataset_top for top values, dataset_row for raw records). The usage context is clear from the title and description, but alternatives are not discussed.

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 the core behavior: ranking rows by a numeric column and returning either the highest or lowest. However, with no annotations available, it does not surface important behavioral details such as default limit behavior, tie handling, or what happens when a non-numeric column is supplied, leaving the description to carry the full transparency burden.

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, front-loaded, and free of filler. The quoted 'which is the most/least X' is somewhat informal and adds little over the first clause, but overall the structure is efficient and easy to parse.

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?

With no output schema and no annotations, a single sentence about highest/lowest rows is only minimally adequate. An agent still lacks clarity on default row count, asc/desc interplay, return shape, and when this tool is preferable to dataset_stats or dataset_search, so the context is incomplete for reliable 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 description coverage is only 33%, so the description needs to compensate. It adds the useful constraint that the column must be numeric, but it does not explain the limit parameter or clarify how ascending relates to 'most/least' beyond what the schema already states. The limit behavior is left to inference from the parameter name.

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 the highest or lowest rows of a dataset by a numeric column, with the use case 'which is the most/least X'. It is distinct in intent from siblings like dataset_search or dataset_stats, though it does not explicitly name or contrast those alternatives.

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?

There is no guidance on when to use this tool versus siblings such as dataset_search, dataset_stats, or dataset_row. The intended use is only implied by the title and phrasing, with no explicit context, prerequisites, or exclusions.

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