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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 what information is returned and that it is an initial schema-learning call, but it does not explicitly state that the operation is read-only or describe any side effects, cost, or response format. The 0-parameter, metadata-read nature makes this gap less severe.

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 filler. The output contents are listed first, and the actionable instruction 'Call this first' is placed in the second sentence. 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?

With zero parameters and no output schema, the description adequately covers the returned information and the intended call order. The only minor gap is that it does not specify the exact representation of numeric flags or provenance banner, but for a simple schema-discovery tool this is not critical.

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?

There are no parameters and schema coverage is effectively complete, so the baseline is 4. The description adds value by enumerating the output fields (columns, numeric indicators, row count, provenance banner), which helps the agent know what to expect.

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 exact deliverable: columns, numeric flags, row count, and provenance banner for the Monthendly dataset. It also positions the tool as the schema-discovery entry point, which distinguishes it from sibling tools like dataset_row 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?

It explicitly says 'Call this first to learn the schema,' giving clear when-to-use guidance. However, it does not mention when not to use it or name alternatives, and the provenance banner overlap with dataset_provenance is not addressed.

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 usefully reveals that matching is any-of and that output order follows the input values order. However, it does not describe the return format, whether all matches are returned, pagination, or exact-match/case-sensitivity 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, front-loaded sentence that conveys the resource, filter, ordering, and intended use case with no filler. Every clause contributes meaning.

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?

Adequate for a two-parameter read-style tool, but with no output schema and no annotations the description should say more about the returned row shape, the 'side by side' presentation from the title, and edge cases such as no matches or duplicate values. It leaves some operational ambiguity.

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 0%, but the description directly maps 'column' to the field to filter on and 'values' to the list of values to match, and it adds the crucial semantic that the row order follows the given value order. This goes beyond the bare schema 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 identifies the resource (rows of the Monthendly dataset), the filtering action (column matches any given value), and the ordering behavior. The 'X vs Y questions' phrase gives a clear use case, but the description lacks an explicit verb like 'returns' and does not differentiate from sibling tools such as 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?

It explicitly scopes usage to 'X vs Y questions', which tells an agent when this comparison tool is appropriate. It does not state when not to use it or name alternatives, but the use-case cue is clear enough for selection.

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 and does a good job: it signals a read-only metadata lookup and enumerates the returned fields. It does not discuss error cases or permissions, but for a zero-parameter provenance lookup that 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?

A single compact sentence that front-loads the content of the tool and ends with the reason to use it. Every word earns its place; no filler or repetition of the title.

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, the description fully covers the returned information and the intended use case. An agent can correctly invoke this tool and know what to expect without needing any additional context.

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 schema coverage is 100%, so there is nothing for the description to add about parameters. The baseline of 4 applies because no parameter documentation is needed at all.

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 provides: source, computed date, licence, and citation for the Monthendly dataset. It is clearly distinct from sibling tools like dataset_row, dataset_stats, or dataset_search, which handle data access rather than provenance metadata.

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 it: 'Read this to attribute a figure correctly.' It does not explicitly list when-not-to-use conditions or alternatives, but the sibling tool names make the boundary obvious, and the guidance is actionable.

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 usefully discloses case-insensitive exact matching, but it does not state whether a single row or all matching rows are returned, how results are ordered or limited, or what happens when no row matches. The singular title ('a row') and plural description ('rows') leave cardinality ambiguous.

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, front-loaded sentence with no filler. 'The rows ... where a column equals a value exactly (case-insensitive)' delivers the core operation and matching behavior in thirteen words.

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 lookup with no output schema and no annotations, the description covers the match predicate but leaves the result shape, multiplicity, and edge-case behavior unspecified. It is adequate for a simple tool but not fully complete: an agent cannot predict whether the call returns one row or many, or what the payload 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 coverage is 0%, so the description must give meaning to 'column' and 'value.' It does: 'where a column equals a value exactly.' This maps both parameters to their lookup roles, but it does not clarify data types, accepted column-name format, or value syntax beyond what the schema's minLength constraints imply.

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 an exact-match row lookup: 'Look a row up by an exact key' and 'rows ... where a column equals a value exactly.' The 'exactly (case-insensitive)' wording distinguishes it from fuzzy or partial search, though it does not name sibling tools explicitly.

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 exact-match/case-insensitive framing implies this is for looking up rows by an exact value rather than searching or stats, but the description gives no explicit when-to-use guidance, exclusions, or references to alternatives such as dataset_search. Usage context must be inferred by the agent.

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 useful traits: case-insensitive matching, substring containment across cells, and a 50-row cap. However, it is silent on result ordering, what happens when more than 50 rows match, and the exact return shape.

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 where every clause carries information: scope, matching predicate, case-sensitivity, and result cap. It is appropriately front-loaded with the key matching behavior and contains zero 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 2-parameter tool, the invocation essentials are covered, and 'Rows of the Monthendly dataset' hints at the return shape. But with no output schema and no annotations, the lack of ordering, truncation, and empty-match behavior leaves the definition minimally adequate rather than 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 coverage is 50% — query is described ('text to look for in any cell') while limit has no description. The description adds the case-insensitive nuance and confirms the query semantics, and 'up to 50' clarifies the effective cap, but it does not fully compensate for the undocumented limit parameter's 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 description precisely identifies the resource ('Rows of the Monthendly dataset') and specifies the matching predicate: cells containing the query, case-insensitive, capped at 50. It is clear and specific, though the verb is carried by the name/title ('Search the dataset') rather than the description itself, and sibling differentiation is implicit rather than explicit.

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 provided on when to use this tool versus its siblings. There is no mention of dataset_row for fetching a specific row, dataset_top for previewing, or dataset_columns for schema access, and no exclusion criteria or alternatives are named. Usage context is only loosely implied by the description.

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 and discloses meaningful behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not clarify how excluded rows are represented in the result or how the statistics are returned, but the disclosed data-cleaning behavior adds real value.

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?

One compact sentence that front-loads the computed statistics and appends behavioral caveats in a parenthetical. Every word 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 (one parameter, no nested objects), but with no output schema and no annotations, the description should say something about the return shape or behavior on empty/non-numeric input. The stats list is complete, yet the result representation 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?

Schema description coverage is 0%, so the description must compensate. It adds that the column must be numeric and belong to the Monthendly dataset, which goes beyond the bare schema ('column': string). It does not specify format expectations such as exact name matching, but for a single parameter this is adequate.

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 operation (compute count, min, max, mean, median, sum) on a specific resource (a numeric column of the Monthendly dataset). The enumerated statistics clearly distinguish it from siblings like dataset_row, dataset_search, and dataset_columns.

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 intended use is implied by the title and the statistic list — an agent can infer this is the tool for descriptive statistics. However, there is no explicit guidance about when not to use it or how it compares to alternatives such as dataset_top or dataset_search.

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 states the sorting intent but does not reveal default limits, tie-breaking behavior, handling of missing or non-numeric values, or any other operational details beyond what can be inferred from 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 concise sentence with the main idea front-loaded. The quoted gloss is slightly redundant, and the typo "Monthendly" adds noise, but overall it 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?

The tool has no output schema, minimal annotation coverage, and a thinly described parameter set, so the description is not fully adequate for correct invocation. An agent still needs to infer the default limit, the exact effect of `ascending`, and the expected return shape.

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 should compensate for the undocumented parameters. It adds useful meaning to `column` by specifying it must be numeric, and hints at `ascending` via "highest or lowest," but provides no guidance on `limit` or how the parameters interact.

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 ranks rows of the Monthendly dataset by a numeric column to find the most or least X. It identifies the resource and the operation, though it does not explicitly differentiate from siblings like dataset_stats 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 "which is the most/least X" implies the tool is for top-N or bottom-N ranking queries, giving some usage context. However, it does not mention when to prefer this tool over sibling tools or provide any exclusion criteria.

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