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

With no annotations and no output schema, the description carries the full burden, and it does disclose the four things returned (columns, numeric indicators, row count, provenance banner). It stays silent on read-only/permission expectations, response format, and sizing behavior for a dataset tool, so it is adequate but not rich.

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 no filler, and the highest-value instruction ('Call this first') is placed after the payload list where it is still easy to find. The opening is a verbless noun phrase, a minor structural rough edge.

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, read-only introspection tool with no output schema and no annotations, the description conveys enough about what comes back to call it correctly. It could add a one-line note that the result is a static read of the bundled dataset, but nothing essential is missing.

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 baseline rule this scores 4; there is nothing for the description to compensate for on the input side.

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 concrete payload (column list, numeric flags, row count, provenance banner) and the resource it belongs to (the Fair Odds Calculator dataset), which is more specific than a bare name restatement. It does not explicitly differentiate itself from overlapping siblings such as dataset_stats or dataset_provenance, which also surface counts/provenance, so it stops short 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?

'Call this first to learn the schema' gives an ordering cue that implies this is the entry point before other dataset_* calls. However, no alternative is named and there is no when-not guidance, so the routing value 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.

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

With no annotations, the description carries the full burden. It does disclose one real behavioral trait — results come back 'in the order given' — which is not derivable from the schema. It is silent on read-only safety, what happens to values that match no rows (omitted vs. error), and whether full rows or just matched columns are returned.

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 with no filler, and the dataset scope plus filtering rule come first. The em-dash clause placement and fragment form make it slightly less crisp than a clean verb-first statement, but nothing is padded.

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?

No output schema, no annotations, and 0% schema coverage leave the description doing all the work, yet it does not state the return shape, ordering guarantee for unmatched values, or error behavior. For a 2-parameter retrieval tool with an identical-looking sibling set, this is under-specified.

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 the any-of (OR) semantics of 'values' and the order-sensitivity of the result, which adds meaning beyond the bare types. It still omits the 2-item minimum, the 10-item cap, and that all values must belong to a single named column.

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 conveys a filter-and-retrieve operation ('rows ... whose column is any of the given values') and adds an ordering nuance, but it is a noun-phrase fragment with no verb and never states the actual comparison behavior the title advertises. An agent can infer it selects rows by column match, but the wording is implied 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?

'for "X vs Y" questions' is a genuine usage trigger that hints at the right scenario, which is more than most siblings offer. However, it names no alternatives (dataset_search or dataset_row are obvious overlapping options) and gives no when-not-to-use condition, so routing is left partly to 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 supplied, so the description carries the burden; it does so by disclosing the four categories of information the call returns (source, computed date, licence, citation). It doesn't explicitly state that the operation is non-mutating, but it enumerates output content that would otherwise be unknown, which is meaningful added context for a zero-annotation 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?

Two short sentences with no filler: the first enumerates the returned content and the second states the use case. Front-loaded with the payload the caller cares about.

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 and no annotations, the description must describe the return; it does so by listing the four fields an agent will receive. It omits the exact shape/format of the citation and licence values, but an agent has enough to call it correctly and interpret results.

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 there are no parameter semantics to explain; the baseline for a parameterless tool applies. Nothing in the description misrepresents what can be passed.

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?

Names the exact content returned — source, computation date, licence, citation — for a named dataset, and is immediately distinguishable from the sibling tools (dataset_columns, dataset_row, dataset_stats) that return data rather than metadata. An agent can tell what this tool is for without opening anything else.

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?

"Read this to attribute a figure correctly" gives a clear condition for invoking it: whenever a figure needs citation or attribution. It does not state explicit exclusions or a contrasting sibling, but the usage context is concrete and unambiguous.

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 it does disclose one meaningful behavioral trait: matching is exact and case-insensitive. However, it says nothing about ordering, limits, pagination, or what happens for an unknown column, which are real gaps for a query tool with zero annotation coverage.

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 tight sentence that front-loads the resource and ends with the most discriminating detail (exact, case-insensitive). No filler, though it is so brief that it leaves obvious questions unanswered.

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 should at least hint at the return contract. Saying 'the rows' (plural) against a singular tool name 'dataset_row' leaves ambiguity about whether all matches or a single row are returned.

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 two required parameters, so the description must compensate. It does map both parameters semantically ('a column equals a value'), but adds no format detail such as whether 'column' is a header name and how case/normalization applies to the column versus the value.

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 gives a specific verb-plus-resource ('The rows of the Fair Odds Calculator dataset') and pins down the filter semantics ('where a column equals a value exactly (case-insensitive)'). It is clear what the tool does, though it never names or distinguishes itself from the sibling dataset_search, which likely covers the non-exact case.

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 explicit when-to-use or when-not-to-use guidance, and no alternatives are named. The word 'exactly' faintly implies a contrast with a fuzzier search tool like dataset_search, but that inference is left entirely to 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, the description carries the full behavioral burden. It usefully discloses case-insensitive matching and a 50-row result cap, but is silent on result ordering, default limit behavior, and whether this is a pure read with no side effects — though 'up to 50' partially duplicates the schema maximum.

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 tight sentence with the resource, matching semantics, and cap front-loaded; no filler. It reads as a sentence fragment, but nothing is wasted.

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 2-parameter search tool with no annotations and no output schema, the description covers matching semantics and result count but omits ordering, default limit, and what a returned row looks like. Adequate minimum, with clear 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 coverage is 50%: query is documented, limit is not. The description adds real meaning for query by specifying 'any cell' plus case-insensitivity, and clarifies limit's upper bound, but provides no default value or guidance on choosing a limit, leaving the undocumented parameter only half compensated.

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?

States a specific verb+resource (rows of the Fair Odds Calculator dataset) and the matching scope (cells containing the query). It clearly distinguishes itself from a stats/compare tool, but never names or contrasts with siblings like dataset_row or dataset_top, so an agent must infer that this is the free-text entry point.

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 when-to-use guidance, prerequisites, or exclusions are given. The description implies a free-text substring search, but nothing tells the agent when to prefer this over dataset_row or dataset_top, or what to do when no rows match.

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 load, and it does disclose real data-handling semantics: grouping commas and currency are normalized, and non-numeric rows are excluded and counted. It still omits what happens if the column is entirely non-numeric, whether errors are raised, and the exact shape of the result.

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 front-loaded sentence that leads with the returned measures and tucks the caveats into a parenthetical. Dense but no wasted sentences; the parenthetical stacks three separate caveats together, slightly blurring the 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?

Absent an output schema, the description helpfully enumerates the six returned fields, but it leaves the input contract (column addressing) and error behavior for a required, undocumented parameter unstated. Adequate for a simple one-parameter tool but with clear 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?

There is one parameter with 0% schema description coverage, and the description does add the constraint that the column must be numeric. However it does not clarify how the column is identified (name vs. index, case sensitivity, whether unknown columns error), leaving the sole parameter only partly specified.

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?

States a specific operation (summary statistics) on a specific resource (a numeric column of the Fair Odds Calculator dataset) and enumerates the returned measures, which separates it from siblings like dataset_top or dataset_compare. No sibling is named explicitly, so it stops short 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?

The description never says when to reach for this tool versus dataset_top, dataset_compare, or dataset_search, nor any prerequisites. Usage is only implied by the word 'statistics'.

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, and it falls short. It conveys that rows are returned ranked, but says nothing about default limit behavior, tie handling, what happens when the column is non-numeric, ordering guarantees, or error conditions — all material for a ranking/preview 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?

A single tight sentence with an embedded example, well front-loaded with the core ranking action. No filler, though the brevity leaves the behavioral gaps noted elsewhere.

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 3-parameter tool with no annotations, no output schema, and only 33% schema coverage, the description is too thin. It should clarify the limit default and return shape (top N rows) to be complete enough for an agent to call correctly.

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 low (33% — only 'ascending' is documented), so the description must compensate. It adds real meaning for 'column' by requiring it to be numeric ('by a numeric column'), and 'highest (or lowest)' maps to the ascending flag. However, it says nothing about 'limit' beyond the schema's max of 50, leaving a 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?

States a specific verb (rank/return highest or lowest) and resource (rows of the Fair Odds Calculator dataset) scoped to a numeric column, and the 'which is the most/least X' framing makes the intent concrete. It is clear what it does, though it doesn't explicitly differentiate itself from siblings like dataset_search or dataset_row beyond the implicit ranking semantics.

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' phrasing implies the use case (extremal value questions such as top/bottom performers), which is reasonable implied guidance. But there is no explicit when-to-use vs alternatives — nothing says when to prefer this over dataset_search, dataset_row, or dataset_stats.

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