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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 full burden of behavioral disclosure. It lists the returned content (columns, numeric flags, row count, provenance banner) and implies a read-only schema lookup, but it does not explicitly state that there are no side effects, permissions required, or error behavior. Adequate but not fully 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?

Two short sentences, with the first front-loading the exact information returned and the second adding a clear usage directive. Every sentence 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 0-parameter, no-output-schema tool, the description covers the key return elements (columns, numeric flags, row count, provenance banner) and the recommended call order. It is reasonably complete, though adding output format details or an explicit read-only label would strengthen it.

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 zero properties, so there are no parameters to describe. The schema already captures the absence of parameters, and the description adds no misleading parameter information. Baseline 4 applies for 0-parameter 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 states the tool returns the dataset's columns, which are numeric, the row count, and the provenance banner. 'Call this first to learn the schema' reinforces its role as the schema-discovery tool. This differentiates it from siblings like dataset_provenance and dataset_stats by focusing on schema/shape rather than rows, stats, or 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 'Call this first to learn the schema' gives explicit timing guidance, positioning this as the initial step. It does not explicitly contrast it with sibling alternatives, but the clear context of being the first schema-related call is sufficient 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 full burden. It discloses useful behavioral traits: matching is on 'any of the given values' and output order follows the input order. However, it does not mention edge cases like no matches, exact-match semantics, or whether the operation is read-only, though the wording implies retrieval.

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 efficient sentence packs the resource, filtering logic, ordering behavior, and usage context with no filler. The most important information appears first.

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 2-parameter read-oriented query tool, the description gives enough to call it correctly: what rows are returned, how parameters map to behavior, and the expected order. It does not describe the output format, but no output schema exists and the title ('Compare rows side by side') partially covers that. Minor gaps remain around empty results and exact matching.

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 does: 'whose column is any of the given values' clarifies that `column` is the field to match and `values` are the accepted cell values, while 'in the order given' explains how the `values` array order affects output. This adds real meaning 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 states a specific behavior: returning rows of the CoilDesk dataset filtered by a column matching any of the given values, preserving the given order. It also explains the intended use ('X vs Y' questions), which distinguishes it from siblings 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 phrase 'for "X vs Y" questions' gives clear context for when to use this tool: comparing specific row values side by side. It does not explicitly name alternatives or exclusions, but the context is specific enough to guide selection among siblings.

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 clearly states that this is a read operation ('Read this') and enumerates the informational fields returned. However, it does not describe any output format, error behavior, or whether the data is static or fetched dynamically, though for a zero-parameter provenance tool this is a minor gap.

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 sentences with no redundancy. The first sentence packs the core content categories, and the second sentence states the practical use case. It is appropriately front-loaded and easy to scan.

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 zero-parameter metadata tool, the description is largely complete: it names the dataset, the fields returned, and the reason to call it. The lack of an output schema is partially mitigated by the explicit list of content categories, though the exact structure of the return value is left unspecified.

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 parameter semantics are not applicable. Per the calibration baseline, a no-parameter tool receives a 4; the description adds contextual value by explaining what the returned provenance information will contain, which is more than is required.

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 (CoilDesk dataset provenance) and the exact content categories: source, computation date, licence, and citation. It also gives the intended use ('to attribute a figure correctly'), which clearly distinguishes it from sibling tools like dataset_stats or 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 explicitly tells the agent when to use this tool: when attribution for a figure is needed. It does not explicitly name sibling alternatives or give when-not-to-use conditions, but the attribution context is clear enough for selection among the listed siblings.

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 and does add the important detail that matching is exact and case-insensitive. But it does not disclose what happens when no rows match or when multiple rows match, and it does not state that the operation is 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?

The description is concise, one sentence long, and contains no filler. The core matching behavior is front-loaded, though the phrasing could be slightly more direct.

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 with no output schema, the description covers the basic matching semantics. However, it lacks guidance on return behavior, such as whether one row or multiple rows are returned, and what happens on no match.

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 missing parameter documentation. It only restates that 'column' and 'value' are used in an equality comparison, without explaining valid column names, value formatting, or edge-case behavior.

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 identify a row-lookup operation on CoilDesk dataset rows using exact equality. The 'exactly (case-insensitive)' wording helps distinguish it from a broader search, but the description does not explicitly differentiate it from the sibling 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 phrase 'exact key' implies this tool is for known-key exact lookups rather than open-ended searching. However, there is no explicit guidance about when to prefer this tool over dataset_search, nor any mention of alternatives 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 provided, the description carries the full transparency burden. It discloses several non-obvious behaviors: case-insensitive matching, substring-like cell containment, and an upper bound of 50 returned rows. It does not explicitly state read-only behavior or the output format, but the language strongly implies a non-mutating query.

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 resource and core behavior, with every phrase earning its place. It is compact without sacrificing the key details needed to understand the tool.

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 with no output schema, the description covers the essential semantics: what is searched, how matching works, and the result cap. Minor omissions such as result ordering and explicit return structure are not critical given the simplicity of the operation.

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 the query parameter but not the limit parameter, leaving 50% coverage. The description compensates by clarifying that queries match case-insensitively across cells and that results are capped at 50, giving meaning to both parameters. It doesn't explain the limit's optionality or default, but the schema specifies the numeric bounds.

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 defines the tool as a search over the CoilDesk dataset, returning rows whose cells contain the query. It specifies the predicate (cell containment), case-insensitivity, and a 50-row cap. It doesn't explicitly contrast with sibling tools, but the sibling names (compare, stats, top) make the differentiation reasonably 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?

No guidance is given about when to use this tool versus the sibling tools. There is no mention of when search is appropriate, when it is not, or which alternative to choose for related operations. The agent is left to infer usage from the title and description alone.

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 behavioral burden. It usefully discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. It stops short of describing output format or edge cases like an all-non-numeric column, but the core behavior is 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 compact sentence that leads with the exact set of statistics and tucks edge-case handling into a parenthetical. Every clause earns its place with no 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?

The tool is simple, and the description enumerates the computed statistics, effectively indicating the expected result set. It also covers important data-cleaning behavior. The only notable omission is an explicit statement of the return shape, but this is easily inferable for a single-column summary 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 schema only defines column as a non-empty string, with 0% description coverage. The tool description adds meaningful semantics by clarifying that the column must be numeric and that non-numeric values are excluded. For a single-parameter tool, this adequately compensates for the schema 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 names a specific operation: computing six named statistics (count, min, max, mean, median, sum) for a numeric column of the CoilDesk dataset. This clearly differentiates it from siblings like dataset_columns, dataset_search, and dataset_top, 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?

Usage is implied: this tool is for getting aggregate numeric summaries of a column. However, it does not explicitly state when to choose this over sibling tools, nor does it provide exclusion criteria or alternative recommendations.

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 state the core behavior: returning highest/lowest rows by a numeric column, with direction controlled by ascending. It does not disclose output shape, default limit, or handling of tied values, but the tool appears to be a simple read-only ranking operation.

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. The core operation and illustrative use case are front-loaded, making it easy to scan and process.

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 three-parameter ranking tool, the description plus schema covers the essentials: numeric column, optional limit, ascending direction, and dataset context. It could be strengthened by stating the return format or default limit, but nothing critical is missing for a 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?

Schema description coverage is low (33%), and the description adds meaning by clarifying that the column must be numeric and that results are ordered most/least. However, the limit parameter is not described beyond its schema constraints, and default behavior still relies on inference.

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?

Title and description clearly state that dataset_top returns the highest or lowest rows of the CoilDesk dataset ranked by a numeric column. This distinguishes it from siblings like dataset_row 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?

The description provides a use case ('which is the most/least X') that implies when to use the tool. However, it gives no explicit guidance about when not to use it or which sibling tool would be more appropriate, so usage guidance is only implicit.

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