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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 does state what the tool returns and implies a read-only discovery operation, but it does not explain the exact form of the output or what 'provenance banner' means, leaving some ambiguity.

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 and front-loaded with the most important returned information. It uses no filler, though the phrase 'provenance banner' could be clearer and slightly harms the otherwise concise structure.

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 low complexity with no parameters, but there is no output schema, so the description is the only source of return-value information. It lists the main outputs but leaves 'provenance banner' undefined and does not describe the output format, which is a meaningful gap for an exploratory 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 tool has zero parameters and the schema is empty, so there are no parameter semantics to document. The 0-parameter baseline of 4 applies here, and the description does not need to compensate for any schema gaps.

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 tool's purpose: returning columns, their numeric status, row count, and provenance banner, with the explicit directive to call it first to learn the schema. It is distinguishable from most siblings, though its mention of a provenance banner slightly overlaps with the dataset_provenance tool.

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 says 'Call this first to learn the schema,' giving clear positional guidance for when to use it. It does not mention exclusions or alternatives, so it stops short of a full when-to-use/when-not-to-use explanation.

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 the behavioral burden. It does disclose two key behaviors: matching rows if the column equals any supplied value, and preserving the given value order in the output. However, it doesn't mention return shape, pagination, or non-mutating guarantees, so transparency is partial.

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 packs in the operation, matching semantics, ordering behavior, and use case. 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 simple filtered-read tool with two parameters and no output schema, the description gives enough to invoke it correctly: dataset, column, values, ordering, and purpose. It leaves out explicit return-object details, but the title and 'rows' wording make the outcome reasonably clear.

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 explain the parameters. It does: 'column' is the column to match against and 'values' are the allowed values whose order determines the result order. That adds the relationship semantics missing from the schema, though it could be more explicit about value type 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 names a concrete operation: selecting rows of the Shortcodo dataset by matching a column against a set of values, in the supplied order. The 'X vs Y' framing and the title make its comparison intent clear and distinguish it from generic search or row-level siblings, though it doesn't explicitly name 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 'for X vs Y questions' note gives a useful when-to-use signal, but the description does not state when not to use it or how it compares with siblings like dataset_search or dataset_row. Usage is 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 provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool returns provenance information (source, date, licence, citation) and implies a read-only nature by saying 'Read this.' It does not mention any side effects or error conditions, but for a simple metadata retrieval tool this is acceptable. The description adds value beyond the empty schema by enumerating the return 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 two short sentences, with the content list front-loaded and the usage instruction following. Every word earns its place—no filler, no redundancy. It is concise and well-structured.

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 tool has no parameters, no output schema, and is a simple provenance lookup, the description adequately covers what the tool does, what it returns, and when to use it. It could optionally mention the exact format of the citation or the licence, but that is likely available in the tool's actual output. Overall, an agent can confidently invoke this tool without further information.

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 parameters and schema description coverage is 100%, so the description need not explain parameters. Per the rubric, 0 parameters gives a baseline of 4. The description does not mention any parameters, which is appropriate since there are none.

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 verb ('Read') and resource ('provenance for the Shortcodo dataset'), and lists the exact contents (source, date, licence, citation). It is clearly distinct from sibling tools like dataset_stats or dataset_search, which focus on other aspects. The title also reinforces the purpose.

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 clear context: 'Read this to attribute a figure correctly.' This tells the agent when to use the tool (when attribution is needed). It does not explicitly mention alternatives or when not to use it, but the use case is unambiguous. Since there are no other provenance-related siblings, this 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 full burden of behavior disclosure. It does add meaningful behavior: the equality match is exact and case-insensitive. However, it does not disclose whether zero, one, or multiple rows are returned, what the response shape is, or any error/edge-case behavior, which are notable gaps for a lookup 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 short sentence with no filler; the title adds a clear verb phrase. All content is relevant to selecting and invoking the tool, and the exact-match/case-insensitivity detail is front and center. It is appropriately sized for the tool's simplicity.

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, the description is mostly adequate, but it leaves ambiguity: the title says 'a row' while the description says 'rows,' and there is no output schema or mention of return shape. It also does not clarify how this tool differs from the sibling dataset_search. These gaps make it minimally complete rather than fully self-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 effectively maps the two parameters by explaining that 'column' is the field to test and 'value' is the exact, case-insensitive value to match. This adds real semantic meaning beyond the bare schema, though it stops short of giving examples or allowed value formats.

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 state that the tool looks up rows by an exact column-value match, with the qualification that matching is case-insensitive. It identifies the resource (rows of the Shortcodo dataset) and the operation, and the word 'exactly' hints at a distinction from fuzzy search, though it does not explicitly name a sibling tool.

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 context is implied: use this when you need an exact, case-insensitive match on a column rather than a search. However, there is no explicit when-to-use guidance, no alternatives are named, and no exclusion criteria are stated, leaving the agent to infer the right situation from the title 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?

With no annotations, the description carries the full burden. It mentions case-insensitivity and a row cap, but omits key behavioral details: whether results are ordered, what happens if no rows match, whether the search is substring or token-based, and whether it scans all columns. These gaps are material for an agent deciding on invocation.

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 behavior. It is appropriately brief, though it sacrifices detail for brevity. It earns a 4 for being tight and readable, but it lacks the structure to include important qualifiers.

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 search tool with no output schema and no annotations, the description is incomplete. It does not specify result ordering, empty-result behavior, pagination, or error handling. It also fails to indicate whether the search is substring or whole-word, which could change the agent's expectations. Given the tool's simplicity, more contextual detail is needed.

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% (only query has a description). The description repeats the query purpose ('contain the query') but adds nothing about the limit parameter beyond an implicit 'up to 50', which is already in the schema as a maximum. It does not explain how limit controls the result size or what its default is, so the description fails to compensate for the missing schema description.

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's function: returning rows of a dataset that contain a given query, case-insensitively, capped at 50. It is specific and distinct from sibling tools like dataset_row (single row) or dataset_stats (statistics).

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 alternatives. It does not mention that this is the tool for full-text searching, nor does it exclude other dataset tools for different operations. The description is purely functional with no usage 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 and meaningfully discloses edge-case behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. It does not detail behavior for missing/empty columns or the exact return structure, but the key calculation 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 front-loads the statistic list and packs the data-cleaning caveat into a parenthetical. Every phrase adds value 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?

For a one-parameter statistics tool, the description covers the input meaning and the result content via the listed statistics, while the sibling list helps an agent locate its niche. Without an output schema, a small additional note on return shape and all-non-numeric-column behavior would make it 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 compensate. It clarifies that the single 'column' parameter should be a numeric column and that formatted numbers are normalized, but it does not specify whether the column is identified by name, label, or index, or what qualifies as numeric.

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 (Shortcodo dataset) and an explicit list of computed statistics (count, min, max, mean, median, sum), which clearly differentiates it from sibling tools like dataset_row or dataset_search. The lack of an imperative verb is mitigated by the concrete statistic list.

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: when a numeric summary of a column is needed. However, there is no explicit guidance about when not to use this tool or which sibling tool to prefer instead, such as dataset_top for ranking or dataset_search for filtering.

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 clearly conveys a read-only ranking operation, but it does not disclose behavior around ties, non-numeric columns, default limit, or result ordering beyond the schema's ascending note. Core behavior is transparent, but edge-case behavior is not.

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 'most/least X' paraphrase adds useful semantic context without bloating the definition.

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 plus schema cover the core call requirements. However, there are no annotations, no output schema, and no usage exclusions, leaving minor gaps around expected return shape and when to prefer sibling tools.

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. It adds the numeric-column constraint and implies ordering via 'highest/lowest', but it does not explain the limit parameter or its default, and the column parameter has no schema description. Compensation is partial at best.

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 verb-resource relationship: it ranks rows of the dataset by a numeric column and returns the highest or lowest. The 'most/least X' framing clearly distinguishes it from sibling tools like dataset_row, dataset_search, 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 'which is the most/least X' phrase implies a clear use case, but the description gives no explicit when-to-use or when-not-to-use guidance and does not mention alternatives among the sibling tools. The usage context is implied rather than stated.

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