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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, and it does disclose the return contents, which is real behavioral information since there is no output schema. It does not state that this is a read-only, side-effect-free operation, nor anything about response size or latency, so it is not fully self-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 short sentences with zero filler, and the actionable instruction ('call this first') is front-loaded right after the payload summary. Every clause adds distinct information about the return 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 zero-parameter, annotation-free tool with no output schema, the description effectively substitutes for a return-value spec by itemizing what comes back. The only soft spot is that it does not clarify how the 'provenance banner' here relates to the dedicated dataset_provenance sibling, leaving slight overlap 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?

The tool takes zero parameters (empty object schema), so there is no parameter semantics to explain; the baseline for a parameterless tool is 4. The description correctly implies no filtering arguments are needed by calling it the first, argument-free orientation step.

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 enumerates the exact payload (columns, numeric flags, row count, provenance banner) of a specific resource, the Trading Card Value Checker dataset, so an agent knows precisely what it returns. The verb is implicit rather than stated (it never says 'returns' or 'lists'), and sibling tools like dataset_provenance and dataset_stats are not contrasted by name.

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?

'Call this first to learn the schema' is explicit ordering guidance that tells the agent when this tool should be invoked relative to the rest of the dataset_* family. It stops short of naming which sibling to use for follow-up questions or stating when this tool is unnecessary.

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 behavioral burden. It does disclose a non-obvious trait the schema does not: results follow the order of the supplied values, and the match is an OR over values. It says nothing about return shape, per-row contents, or any limits on how many rows come back.

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 scope (which rows) and ends with the use case. No filler, though the sentence is grammatically dense enough that it reads a little awkwardly.

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 read tool with no output schema, the description covers purpose and parameter meaning adequately. It stops short of the behavioral context the missing annotations would otherwise supply, such as what a returned row looks like or how large the result can be.

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, and it largely does: "column" is identified as the column being matched and "values" as the set matched against it, with any-of matching and order-preservation semantics spelled out. The 2-item minimum and 10-item cap from the schema are not restated, but the core meaning of both parameters is conveyed.

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 resource and filter: rows of the Trading Card Value Checker dataset where a given column matches any supplied value, returned in input order. That is a precise, verifiable behavior, though it is phrased as a noun fragment rather than a verb+resource and it never contrasts itself with 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 trailing clause "for 'X vs Y' questions" gives a genuine situational cue, so usage is not entirely absent. However, it names no alternatives and gives no exclusion criteria, so an agent must infer for itself whether dataset_row, dataset_search, or dataset_top would be the better pick for a given request.

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 burden. It reveals the returned metadata fields, which is useful behavioral context, but is silent on permissions, whether the dataset is fixed, or any freshness/rate considerations. For a zero-parameter read-only metadata tool the risk is low, but disclosure is thin.

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 tight sentences, front-loaded with the payload contents and closing with the actionable reason to call it. Every clause 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?

With no output schema, the description usefully enumerates the fields an agent will receive (source, computed date, licence, citation), which compensates for the missing return-value documentation. It is nearly complete for a trivial zero-arg tool, lacking only freshness or format details.

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 takes zero parameters, so per the rubric the baseline is 4. Nothing in the description is needed to explain arguments, and it correctly introduces no parameter concepts.

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 content the tool returns (source, computed date, licence, citation) and scopes it to the Trading Card Value Checker dataset, so an agent can distinguish this metadata lookup from the data-oriented siblings like dataset_row and dataset_stats. It is phrased as a noun list rather than a verb+resource, but the purpose is unambiguous.

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 closing sentence 'Read this to attribute a figure correctly' gives one clear use case. However, it names no alternative and offers no explicit when-not guidance, and none of the sibling tools are referenced, so the routing signal is only implied.

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 entire burden, and it does disclose two real behaviors: matching is exact and case-insensitive, and the result is potentially plural rows. It says nothing about matched-row limits, behavior when the column name is unknown, or error/empty-result handling, which are the traits an agent most needs before calling.

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, and the distinguishing constraint (exact, case-insensitive) is placed at the end where it is easy to catch. It leads with the dataset rather than the action, which slightly weakens front-loading.

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?

There is no output schema and no annotations, so the description is the only source of shape information, yet it does not say how many rows come back, what a row looks like, or what happens on an unknown column. For a two-required-parameter lookup tool this leaves meaningful gaps.

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, and it only does so partially: it clarifies that 'value' is matched exactly and case-insensitively, but gives no guidance on 'column' (e.g., that names come from dataset_columns) or on valid value formats beyond minLength 1.

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 specific resource (rows of the Trading Card Value Checker dataset) and a precise selection rule (column equals value exactly, case-insensitive), so an agent can tell it apart from a fuzzy/relevance-oriented sibling like dataset_search. It stops short of naming that sibling explicitly, so the differentiation is implicit rather than stated.

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 when-to-use or when-not-to-use guidance and no alternatives are mentioned. The exact-match and case-insensitive wording hints at the lookup use case, but an agent gets no help deciding between this and dataset_search or dataset_compare.

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 does disclose two useful traits: matching is case-insensitive and results are truncated at 50 rows. It omits the default value of the optional limit, whether full rows or matched cells are returned, ordering, and any read-only/permission framing.

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 with no filler; scope, match semantics, and cap all arrive in one pass. The 'up to 50' clause mildly restates the schema's maximum but also conveys the practical result cap, so it 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 search tool with no output schema and no annotations, the description covers matching and the cap but leaves the return shape, default limit, and ordering unspecified. Adequate to call it, not 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 coverage is 50%: query is documented but limit has no description, only a max of 50. The description compensates partially by clarifying that the query matches any cell case-insensitively and that output is capped at 50, which is meaningful beyond 'text to look for in any cell', but the limit's default behavior remains unexplained.

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?

It names a concrete verb and resource ('Rows of the Trading Card Value Checker dataset whose cells contain the query') and specifies the matching semantics and result cap, so an agent knows exactly what the tool returns. It does not explicitly distinguish itself from near-siblings like dataset_top or dataset_row, which keeps it shy of a 5.

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 statement of when to use this versus dataset_top, dataset_row, dataset_compare, or dataset_stats, and no prerequisites or exclusions. Usage is only inferable from the described behavior.

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 present, the description carries the full burden and does disclose non-obvious behavior: grouping commas and currency symbols are normalized, and non-numeric rows are silently excluded but accounted for in a count. It stops short of stating read-only nature, handling of nulls/empty columns, or error 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?

One tight sentence that front-loads the substantive content (the list of statistics) and appends the important caveats about formatting and excluded rows. No filler, though the parenthetical is slightly dense.

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, the description effectively doubles as the return-value spec by enumerating the six statistics and noting that excluded rows are counted. Missing only edge-case behavior (empty/non-numeric-only columns) to be 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% for the single 'column' parameter, so the description must compensate. It adds the key semantic constraint that the column must be numeric, but does not explain how to discover valid column names (e.g., via dataset_columns) or whether names are case-sensitive.

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 resource (a numeric column of the Trading Card Value Checker dataset) and enumerates the specific statistics returned (count, min, max, mean, median, sum), so an agent knows precisely what it computes. It is clearly distinct from siblings like dataset_row or dataset_search, though it never explicitly contrasts itself with 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?

Usage is only implied: the constraint that the column must be numeric tells the agent when this tool is applicable, but there is no explicit when-to-use/when-not guidance and no mention of alternatives such as dataset_top or dataset_compare for non-aggregate needs.

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 but discloses essentially no behavioral traits. It does not say the operation is read-only, that results are capped at 50 rows, how ties are broken, or what happens if the column is non-numeric despite the 'numeric column' requirement. Only the ordering default is conveyed.

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 operation and the ranking basis; the bracketed '(or lowest)' and the quoted example are compact and earn their space. The double-dash construction is slightly awkward but costs nothing in clarity.

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?

There is no output schema, so the description should indicate what is returned — rows with all columns? just the ranked column? — and it does not. Combined with an entirely undocumented limit parameter and no annotation coverage, an agent has to guess at the result shape and result-size behavior.

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 33% — only ascending has a description, and limit has none. The description adds meaning for column ('numeric') and restates the ascending default, but says nothing about the limit parameter or its maximum of 50, leaving that gap unaddressed in both structured and prose form.

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 on a named resource: returning the highest (or lowest) rows of the Trading Card Value Checker dataset ranked by a numeric column, illustrated with 'which is the most/least X'. That distinguishes it well from dataset_row and dataset_search, though it never names a sibling to sharpen the boundary against dataset_stats or dataset_compare.

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 is only implied through the 'which is the most/least X' framing, which suggests this is the right tool for extremal lookup questions. There is no explicit when-to-use statement, no exclusion ('for aggregate summaries use dataset_stats'), and no prerequisite information.

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