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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. It discloses the exact contents of the result set—columns, numeric flags, row count, and provenance banner—and implies a read-only schema-inspection operation. It does not address side effects or access requirements, but for a metadata tool this is sufficient.

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 sentences with no redundant words. The first enumerates the output components; the second provides invocation context. Every word 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?

Given the absence of an output schema, the description tells the agent what information will be returned and when to call. It might have included an example or clarified the output format, but the core context needed to invoke correctly is 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 input schema is empty, so there are no parameters to document. The description adds context about what the output contains, satisfying the baseline expected for a zero-parameter 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 specifies exactly what the tool provides: column names, which are numeric, row count, and the provenance banner. The phrase 'Call this first to learn the schema' establishes it as the schema-discovery tool, distinguishing it from siblings like 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 directive 'Call this first' gives clear ordering guidance for when this tool should be invoked relative to other dataset tools. It does not explicitly name alternatives or describe when not to use it, but the timing guidance is strong.

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 does disclose that output order follows the order of the given values, which is a meaningful trait. However, it does not clarify whether the result is a side-by-side comparison view (as the title implies) or just a filtered list, nor does it mention empty-result behavior or case sensitivity.

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 core behavior ('rows ... whose column is any of the given values') and ends with a useful use-case tag. There is no filler or redundancy; every clause 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?

The description explains the filtering and ordering but leaves important gaps: no output schema or annotations, no mention of whether the return includes full rows or a side-by-side layout, and no explicit contrast with sibling search tools. For a comparison-focused tool, an agent would benefit from knowing what the output looks like.

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 semantic source for parameters. It effectively explains 'column' as the filter field and 'values' as the values to match, and that the order of 'values' determines the output row order. This compensates well for the missing schema descriptions.

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 behavior: retrieving rows whose column matches any of the given values, preserving the provided order. This is distinct from the vague title 'Compare rows side by side' and gives an agent a concrete picture of the operation. It doesn't explicitly name sibling tools, so differentiation relies on the described filtering behavior.

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 a usage context, but the description never explicitly says when to prefer this tool over siblings like dataset_search or dataset_row. An agent might struggle to distinguish this from a generic search tool that filters rows by criteria.

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. It makes clear this is a read-only metadata lookup by saying 'Read this' and specifying what information will be retrieved. It does not mention auth requirements or side effects, but for a zero-parameter provenance tool those are unlikely to be concerns.

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 concise sentences that front-load the key content: what the datum is and how the agent should use it. Every word contributes useful information, with no fluff 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, the description fully captures what the agent needs to know: the contents of the provenance record and when to retrieve it. No output schema exists, but the description summarizes the key fields sufficiently for correct selection and use.

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 an empty input schema, so there is no parameter documentation burden on the description. The description appropriately focuses on the returned provenance information rather than 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 description clearly identifies the resource (EOR Compass dataset), the specific information returned (source, computed date, licence, citation), and the intended purpose (attributing a figure). This distinguishes it from sibling tools like dataset_search or dataset_stats, which focus on data access and analysis.

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 tells the agent when to use this tool: when a figure needs correct attribution. It does not explicitly mention alternatives or when not to use it, but the absence of overlapping siblings makes this less critical. The 'Read this to attribute a figure correctly' phrasing gives practical 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?

No annotations exist, so the description carries the full burden. It does disclose the two behaviors that matter most for correct invocation: exact matching ('exactly') and the case-insensitive modifier. However, it does not say whether multiple matching rows are returned, what the result format is, whether there is a row limit, or what happens on no match.

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; it front-loads the dataset identity and packs in the matching semantics (exact, case-insensitive). It is efficient, though slightly terse rather than actively disorganized.

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 conveys the dataset and match semantics adequately. Gaps remain: the agent must guess valid column names and has no context on how row lookup relates to the sibling comparison/provenance/stats tools, so invocation is partially guesswork.

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 clarify that 'column' is the dataset field to match and 'value' is the value to compare against, but it provides no format guidance, examples, valid column names, or value conventions, leaving both required parameters partially underspecified.

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 what the tool returns: rows of the EOR Compass dataset filtered by exact column-value equality, with case-insensitivity called out. The verb is implied ('returns') rather than explicit, and the filter semantics distinguish it from siblings like dataset_columns or dataset_provenance, though no sibling is named.

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 alternatives such as dataset_columns, dataset_compare, dataset_provenance, dataset_stats, or dataset_top. No exclusions, conditions, or alternative-selection hints are provided.

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 behavioral disclosure. It does state two important traits: the match is case-insensitive and the result is capped at 50 rows. It does not mention ordering, default limit, or behavior when more than 50 rows match, but the core semantics are disclosed.

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 containing only relevant information, with the core behavior front-loaded. 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?

The description explains what is returned, the case-insensitive nature, and the row cap, which covers the essential behavior for a simple two-parameter search. It lacks details on result ordering, default limit, and output format, and there is no annotation to fill that gap, but the tool is simple enough that this is adequate.

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 only 50% (query documented, limit not). The description adds value by explaining that results are capped at 50 and that the query is matched case-insensitively against any cell, which clarifies both parameters. It stops short of documenting default values or exact matching semantics.

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 rows of the EOR Compass dataset matching a case-insensitive query with a 50-row cap. It names the resource and behavior, but does not explicitly distinguish itself from sibling tools like dataset_row or dataset_top, so it misses the top score.

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 explicit guidance is given on when to choose dataset_search over siblings such as dataset_row or dataset_top, and no exclusions are stated. The usage is only implied: an agent can infer it should be used to find rows containing arbitrary text, but there is no direct routing information.

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 properly discloses key behavioral details: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond the title and helps set expectations about data cleaning behavior. It does not cover edge cases like empty columns or invalid column names, but the disclosed quirks are significant and valuable.

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 front-loads the core statistics and then adds important caveats. Every phrase contributes meaning, and there is no filler or redundant repetition of the tool name or title. The structure is efficient 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 one-parameter tool with no output schema, the description provides enough context to understand what the tool returns, especially since it explicitly lists the computed values. It also covers the relevant preprocessing behavior. Minor gaps such as error handling for non-numeric or nonexistent columns prevent a perfect score, but the description is largely 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 schema only defines 'column' as a string with minLength 1, and schema description coverage is 0%, so the description must carry meaning. The description does add that the column must be numeric and belong to the EOR Compass dataset, which is useful. However, it does not provide examples, valid column name formats, or point to dataset_columns for discovering available columns.

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 a concrete operation: computing summary statistics for a numeric column of the EOR Compass dataset. It enumerates the exact statistics (count, min, max, mean, median, sum), making the tool's purpose unmistakable. This also differentiates it from siblings like dataset_row, dataset_search, 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 the tool should be used when summary statistics are needed for a column, and the name 'dataset_stats' reinforces this. However, it does not explicitly mention when not to use it, such as relying on dataset_columns for schema information or dataset_top for ranked values. The usage context is clear but no exclusions or alternatives are stated.

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 disclosure burden. It conveys that the tool performs a read-style ranking operation and requires a numeric column, but it omits important behavioral details such as the default limit, whether rows are returned in full, and how ties or invalid columns are handled.

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, tight sentence with no filler. The quoted 'which is the most/least X' phrase usefully frames the tool's purpose 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?

This tool has three parameters, no annotations, and no output schema, so the description needs to provide more context. It fails to explain the default limit behavior, what the returned rows look like, or what happens when the requested column is not numeric.

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 only documents 'ascending'; 'column' and 'limit' have no descriptions. The description adds meaning for 'column' by specifying it must be numeric and serves as the sort key, and 'highest/lowest' clarifies the 'ascending' semantics. However, 'limit' is entirely absent from the description, leaving a coverage 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?

Title and description clearly state the operation: returning the highest or lowest rows of the EOR Compass dataset by a numeric column. The phrase 'which is the most/least X' gives concrete intent and distinguishes it from sibling tools like dataset_search, dataset_row, and 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 implies when to use the tool: when a user asks for the most or least of something by a numeric column. However, it never explicitly states when not to use it or mentions alternatives such as dataset_stats or dataset_search.

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