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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavior disclosure. It clearly enumerates the returned information (columns, numeric status, row count, provenance banner), implying a read-only operation, but it does not mention permissions, errors, or output formatting. The core behavior is disclosed, though not richly.

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 filler. It front-loads the output contents and then gives a clear usage directive, making every word earn 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 schema-discovery tool, the description covers the essential outputs and the intended first-step usage. It does not fully specify the output structure, but that is acceptable given the tool's simplicity and the absence of an output schema.

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 baseline of 4 applies because the description does not need to add meaning beyond an empty 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 clearly identifies what the tool provides: columns, numeric flags, row count, and provenance banner for the Eobify dataset. It does not use an explicit verb like 'Returns' or 'Lists', but the noun-phrase style is unambiguous and distinct from sibling tools such as dataset_provenance 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 instruction 'Call this first to learn the schema' gives explicit sequencing guidance, telling the agent this is the initial discovery step. It does not explicitly name alternatives or when not to use it, but for a zero-parameter schema tool the guidance is sufficient.

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 does disclose important behavior: rows are selected by 'any of' matching and returned in the provided value order. However, it does not explain exact-match semantics, side-by-side formatting, empty-result behavior, or whether output is limited in any way.

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, focused sentence conveys the core operation and purpose with no redundant content. The key selection and ordering behavior appears early, and the use-case hint is appended cleanly.

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 with no output schema and no annotations, the description covers the core invocation need: what rows are returned, how they are filtered, and in what order. Minor gaps remain around exact matching behavior and the visual 'side by side' presentation, but these are not blocking for correct 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 schema has 0% description coverage, so the description must add parameter meaning. It does: 'column' is the field used for filtering, and 'values' are the matching values whose order also determines output order. This is sufficient for the two simple parameters, though it omits details like allowed column names or case sensitivity.

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 (rows of the Eobify dataset), the selection mechanism (column matching any of the given values), and the output ordering (order given). It also conveys the intended use case ('X vs Y' questions), but it does not explicitly differentiate 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 phrase 'for "X vs Y" questions' implies when the tool is appropriate, but the description never names alternative tools or states when not to use it. Given several sibling tools exist, more explicit routing would strengthen this dimension.

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 discloses the returned content (source, computed date, licence, citation) and 'Read this' implies a read-only operation, but it never explicitly states that there are no side effects or what the actual citation output looks like.

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, front-loaded with the substantive field list and immediately followed by the practical use case. No filler, no redundancy, and no repetition of the tool name.

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, zero-output-schema metadata tool, the description covers what data is returned and when to use it. It does not detail the exact citation format or return envelope, but that is a minor gap given the tool's simplicity.

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 and schema description coverage is 100%, so there are no parameters to document. The description does not need to compensate for any parameter documentation 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 exposes: the source, computed date, licence, and citation for the Eobify dataset. It clearly acts as a provenance/metadata accessor and is distinct from sibling tools that deal with rows, columns, stats, or 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 gives a concrete trigger: read this when attributing a figure correctly. It implies the use context and makes clear the tool is for citation/provenance rather than data interrogation, though it does not explicitly name alternative tools 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 behavioral disclosure burden. It does add meaningful traits: exact matching and case-insensitivity, and it implies a read-only lookup by returning rows. Still, it does not specify whether all matching rows are returned or just the first, and it says nothing about result shape or ordering, leaving notable gaps for a read 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 focused sentence with no filler. It puts the dataset and matching condition up front and earns its place by conveying the exact-match, case-insensitive behavior.

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 two-parameter read-only lookup with no output schema, the description covers the query semantics but omits the return behavior, such as whether multiple rows may be returned despite the singular-sounding title, and how the tool relates to sibling tools like dataset_search. These gaps prevent a higher score.

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 all parameter meaning must come from the description. The description maps the two required parameters by explaining that a 'column' is compared to a 'value', and it adds the case-insensitive matching detail. This is sufficient for basic invocation, though it could say more about value formatting or 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 defines the tool's result: rows from the Eobify dataset where a column equals a value exactly. It clearly identifies the resource and the match semantics, and the exact/case-insensitive language separates it from a fuzzy search like dataset_search. However, it lacks an imperative verb (e.g., 'retrieves'), so it does not fully meet the 5-level bar.

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' in the title and 'equals a value exactly' imply this tool is for exact-match lookups rather than partial or fuzzy search, giving indirect usage guidance. But the description provides no explicit when-to-use/when-not-to-use statements or named alternatives like dataset_search, so the guidance remains mostly 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 burden of behavioral disclosure. It adds meaningful details beyond the schema: matching is case-insensitive, it searches across cells, and results are capped at 50 rows. It does not mention ordering, default limit behavior, or what happens with no matches, but the disclosed behavior is clear and useful.

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 core operation and includes the two most important behavioral constraints: case-insensitive cell matching and the 50-row cap. There is no filler or repetition of schema information.

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 limit. It could be more complete by noting the default limit and result shape, but an agent has enough information to call the tool correctly in the common case.

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 50%, since limit has no schema description. The tool description compensates by explaining that the result size is capped at 50, which maps directly to the limit parameter, and by adding case-insensitivity semantics for query. It does not explain the default value when limit is omitted, but the main parameter behaviors are 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 states that the tool returns rows of the Eobify dataset matching a query in any cell, with case-insensitive matching and a limit of 50. This is specific enough to distinguish it from sibling tools like dataset_stats or dataset_columns, though it lacks an explicit verb and does not directly name any sibling it is not.

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 primary use case: find rows containing arbitrary text. However, it provides no explicit guidance on when to prefer this over sibling tools such as dataset_row or dataset_top, and it does not mention exclusions or alternative tools even though the sibling list makes selection decisions relevant.

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 well by disclosing that grouping commas and currency are handled and non-numeric rows are excluded and counted. This gives the agent useful expectations about data preprocessing. It does not describe the return format, but the listed statistics strongly imply the output fields.

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 focused sentence that front-loads the result statistics and includes relevant edge-case handling. Every part earns its place, and there is no redundant or vague wording.

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 single-parameter tool with no output schema and no annotations, the description is fairly complete: it names the input, the computed metrics, and important data-cleaning behavior. It could be more complete by describing error handling for missing columns or all-non-numeric columns, but the core calling context is sufficient.

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' refers to a numeric column of the Eobify dataset, but it does not provide details such as accepted column name formats or how to discover valid columns. The description adds helpful context but not full parameter documentation.

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Eobify dataset. This is specific and distinguishes it from sibling tools like dataset_row, dataset_search, or 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?

The description makes the intended use clear: summarizing a numeric column. It does not explicitly name alternatives or say when not to use it, but the numeric-column scope and the listed statistics imply a distinct use case among the siblings. Some explicit guidance about using dataset_columns to find valid column names could improve it.

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 mentions highest/lowest ordering but does not disclose read-only nature, default limit behavior, tie handling, or what the returned rows look like. This is minimal behavioral context for a query tool with no annotation support.

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 concise sentence and is easy to scan. It front-loads the core operation and avoids filler, though the em-dash aside is slightly stylistic rather than informative.

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 tool, the description is minimally viable: it identifies the dataset, the operation, and the key column type requirement. However, with no annotations and no output schema, it would be stronger if it mentioned return behavior, limit defaults, or tie-breaking rules.

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 only the 'ascending' parameter documented. The description adds useful meaning by specifying that 'column' must be numeric and clarifying highest/lowest semantics, but it adds nothing about the 'limit' parameter beyond its name and schema constraints.

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 ranks rows by a numeric column and answers 'which is the most/least X', which makes the operation easy to understand. It does not explicitly name or distinguish itself from sibling tools, but the top-N ranking intent is distinct from row lookup, search, stats, and compare 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 phrase 'which is the most/least X' implies when to use the tool: when ranking rows by a numeric value. However, it provides no explicit guidance about when not to use it or which sibling tool might be more appropriate for related tasks.

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