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

No annotations are provided, so the description must carry the behavioral disclosure burden. It implies a read-only, inspection-oriented operation and lists what will be returned. However, it does not explicitly state that the tool has no side effects, does not modify data, or note any permissions or limitations, leaving some safety behaviors implicit.

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. The first sentence enumerates the exact returned information, and the second provides actionable placement in the workflow. Every sentence contributes value.

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 parameterless metadata-discovery tool with no output schema, the description covers the key return values and tells the agent when to invoke it. It is slightly incomplete in not explaining what a 'provenance banner' is or how the returned columns are ordered, but these are minor gaps for a simple schema-inspection 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 is nothing additional the description needs to explain about inputs. The baseline of 4 applies because no parameter semantics are 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 clearly identifies what the tool provides: columns, numeric flags, row count, and provenance banner for the Cmmsly dataset. It is not a tautology and the 'Call this first to learn the schema' phrase gives it an actionable purpose, though it uses a noun phrase rather than a strong verb like 'returns' or 'gets'.

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 explicit sequencing guidance: call this first to learn the schema. This helps an agent know when to use it during exploration. It does not explicitly name alternative sibling tools or explain when not to use them, but the 'first' instruction is clear enough for an initial discovery tool.

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 does disclose the core behavior: filtering by column values with 'any of' semantics and preserving the given value order. It does not discuss case sensitivity, duplicate handling, or output shape, which are reasonable but unstated details.

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 sentence, zero redundency, with the resource front-loaded. The 'X vs Y' tag is valuable, non-obvious context. It could have added a sentence about output format, but as-is it is compact and easily parsed.

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 select-and-compare tool, this is not grossly insufficient. But with no output schema and no annotations, the agent is left guessing about the response shape and how rows are presented. The relationship to sibling tools is only implied by the name 'compare'.

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 clarify parameters. It establishes that 'column' is the matching attribute and 'values' are the candidate matches, and it relates the values array order to the result order. It does not explain constraints like minItems=2 or exact-match assumptions, but those are partially encoded in the 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 concrete operation: retrieve rows whose column matches any of the given values, in the order supplied. The 'X vs Y questions' phrase gives context for why it exists. It does not explicitly contrast against sibling dataset tools, but the purpose is specific and distinguishable from names like dataset_search or 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 'for "X vs Y" questions' tag gives an implied use case, so an agent can infer when to reach for this tool. However, it does not explicitly say when not to use it or mention alternatives among the sibling tools, leaving the routing decision to the agent's inference.

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 behavioral burden. It clearly signals a read-only informational operation by enumerating metadata fields and instructing the agent to 'read this.' It does not discuss errors or formatting, but for a zero-parameter provenance lookup the behavior is adequately 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?

The description is two short sentences, front-loading the data fields before the usage instruction. Every phrase earns its place and there is no fluff 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 parameterless tool with no output schema, the description tells the agent both what it returns and why it matters. The agent can select it and know it will get attribution metadata without further documentation.

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, which sets a baseline of 4. The description reinforces what the returned content means — source, date, licence, and citation — though there is no parameter-level semantics to clarify.

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 presents — source, computed date, licence, and citation — for the Cmmsly dataset. The title reinforces that it is about provenance and citation, which is clearly distinct from sibling tools for columns, search, stats, and other data operations.

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 gives an explicit purpose: 'Read this to attribute a figure correctly.' This tells an agent when to use it, though it does not name alternatives or provide an explicit when-not-to-use statement. Still, the context of sibling dataset tools makes the intended use clear.

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 mentions case-insensitive matching, which is a useful behavioral trait, but does not disclose whether multiple rows can match, what the output format is, or any access/rate limitations.

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 is easy to parse and front-loads the key behavior. It is concise, though it omits useful context.

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 tool with no output schema and no annotations, the description gives the basic idea but lacks critical details like output shape, potential multiple matches, and error behavior. It is minimally viable but not 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, but it only mentions 'column' and 'value' implicitly without explaining types, format, or that 'value' must be a string. It adds minimal meaning beyond the raw 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 clear verb and resource: look up rows in the Cmmsly dataset by exact (case-insensitive) column match. However, it does not distinguish itself from sibling tools beyond the dataset context, though the title adds 'exact key' specificity.

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 on when to use this tool versus siblings like dataset_columns, dataset_compare, dataset_provenance. The description implies it is for exact-value lookups but does not state exclusions or alternatives.

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 disclosure burden and does reveal case-insensitive matching and a 50-row upper bound. However, it does not explicitly state substring semantics, ordering, default limit behavior, or whether the tool is read-only, leaving some behavioral 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?

A single sentence front-loads the core behavior and every clause adds value: result type, matching scope, case sensitivity, and row cap. There is no redundancy or 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?

Adequate for a simple two-parameter search, but with no output schema and no annotations, it omits the return row structure, the default limit, and any route to sibling tools. An agent knows roughly what it returns but not the full contract.

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 already describes the query parameter and constrains limit with min/max; the description adds 'up to 50' but little else. At 50% schema coverage, the description only partially compensates and does not clarify the default limit or matching nuances beyond what the schema implies.

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: return rows of the Cmmsly dataset whose cells contain the query, with case-insensitivity and a 50-row cap. It is clear on the operation and resource but does not distinguish this from siblings like dataset_top or dataset_row, which could also return rows.

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 on when to use this tool instead of a sibling, nor any exclusions or context for the search. The description implies a general search capability but leaves the agent to infer the appropriate situation.

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 does disclose that grouping commas and currency are handled and that non-numeric rows are excluded and counted—useful behavioral context. However, it does not describe the output structure, error handling, or whether the column must already exist.

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 statistic list and then places the row-handling caveats in parentheses. It is economical with words, though the dataset name 'Cmmsly' appears to be misspelled.

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 tool, the description adequately covers the target column and preprocessing behavior. But with no output schema and no annotations, an agent is left without information about the return format (e.g., dictionary vs list) and potential error conditions, leaving some 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% and the only parameter is 'column' defined as a non-empty string. The description adds meaning by clarifying the column must be numeric, but it does not specify how the column name is passed or how edge cases like whitespace or special characters are handled. Partial compensation 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 explicitly enumerates the computed statistics (count, min, max, mean, median, sum) and identifies the target resource as a numeric column of the Cmmsly dataset. This distinguishes it from siblings like dataset_columns or dataset_top, though it relies on the title 'Summary statistics' to supply the verb.

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 no guidance on when to use dataset_stats versus alternatives such as dataset_search or dataset_top. It implies that the tool is for numeric column summaries, but it does not state selection criteria 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?

No annotations are provided, so the description carries the full burden of behavior disclosure. It only states that the highest or lowest rows are returned, but does not mention output shape, tie handling, missing values, non-numeric column behavior, or that it is a read-only operation. With no annotation safety net, this is a significant 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 a single, focused sentence with no filler. The core idea is front-loaded ('highest or lowest rows'), and the quoted 'which is the most/least X' is a compact, memorable framing that 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 tool with 3 parameters, no annotations, and no output schema, the description is too thin. It does not clarify what the result looks like, how many rows are returned by default, or how the 'limit' parameter interacts with the ranking. An agent calling this tool correctly would need to infer too much.

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% (only 'ascending' has a description). The description adds valuable meaning to the required 'column' parameter by specifying it must be numeric. However, it does not explain the 'limit' parameter's semantics at all, leaving a key aspect of the tool's behavior undocumented.

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 'Rank rows by a numeric column' plus the description 'The highest (or lowest) rows of the Cmmsly dataset by a numeric column' clearly state a specific verb, resource, and scope. The phrase 'which is the most/least X' gives an intuitive framing that distinguishes it from sibling tools 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 description implies when to use the tool: when you need the most/least extreme rows by a numeric column. However, it does not explicitly mention any alternatives or exclusion conditions, so an agent must infer the intended context from the purpose statement alone.

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