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

No annotations are supplied, so the description carries the full behavioral burden. It discloses the returned fields (a useful trait, especially the 'provenance banner'), but says nothing about the operation being a zero-argument read, whether results are cached or static, or whether the call is expensive — all cheap things it could have stated.

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 and no filler: the payload is enumerated first, the recommended call ordering second. Every clause 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 no output schema, the description must announce what comes back, and it does list the four returned items. It is slightly incomplete for an introspection tool in that it does not hint at the shape of the column output (e.g., names paired with numeric flags) or that 'numeric' determines later tool 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?

The tool takes zero parameters, so there is no parameter semantics to document and the baseline is 4. The description correctly implies a no-argument introspection call rather than suggesting undocumented options.

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 — for a specific dataset, which is more than a restatement of the name. It stops short of a 5 because it never differentiates itself from dataset_provenance, which by name covers the same 'provenance banner' content.

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 explicit ordering guidance relative to the other dataset_* tools, which is exactly what an agent needs before touching dataset_search/row/stats. It gives no when-not condition and does not name a specific alternative, so it falls short of 5.

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 does disclose one genuinely non-obvious trait: results are returned 'in the order given', which matters for side-by-side comparison. It says nothing about what happens when a value matches no row, the 10-value ceiling, or that this is a read-only operation, leaving meaningful gaps.

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?

It is a single tightly-written sentence with the resource and filter stated up front and the use case appended after an em-dash. No waste, though the relative clause makes it slightly dense on first read.

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 tool with no annotations and no output schema, the definition covers the core contract (rows returned, ordering, use case) but leaves out the value-count limits and unmatched-value behavior. Adequate to invoke, not fully self-contained.

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 supply the semantics, and it does: 'column' is the field to match against and 'values' are the values to match, with input order determining output order. This compensates well for the missing schema docs, though it omits the schema's 2–10 item bounds.

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 concretely what is returned: the rows of the Calibvo dataset whose column matches any of the given values, preserving input order. That is a specific resource plus filtering behavior, and the 'for X vs Y questions' clause hints at the comparative niche. It does not, however, name any sibling to distinguish itself from dataset_search or dataset_top, 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?

It gives one positive usage cue ('for "X vs Y" questions'), which tells the agent when this tool is appropriate. It offers no when-not guidance and never names an alternative such as dataset_search for single-value lookups or dataset_row for one row, so the routing burden stays largely with the reader.

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 behavioral burden, and it does implicitly signal a safe read by describing static metadata rather than a mutation. However, it says nothing about determinism, permissions, or freshness beyond the computation date, and it does not explicitly confirm the operation is side-effect free.

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, both earning their place: the first enumerates the payload, the second gives the use case. The most decision-relevant content (what you get) is front-loaded with no 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?

There is no output schema, so the description must describe the return value, and it does so by listing the four fields an agent can expect. Combined with zero parameters and a low-risk read, this is nearly complete; only the exact format of the citation/licence response 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?

The schema declares zero parameters and zero required parameters, so there is nothing for the description to explain. The baseline for a parameterless tool applies, with no gaps to compensate for.

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 fields returned (source, computation date, licence, citation) for a specific resource, the Calibvo dataset, so an agent knows precisely what this tool surfaces. It is readily distinguishable from siblings like dataset_stats or dataset_columns, though it never states an explicit retrieval verb and relies on the reader to infer that this is a read.

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 concrete trigger condition for when to call the tool, which is more than most metadata tools offer. It stops short of naming an alternative or an exclusion, but for a zero-argument lookup the routing decision is 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 meaningful matching semantics: equality and case-insensitivity. It says nothing about what is returned (one row vs. all matching rows, the title's 'a row' conflicts with the description's 'rows'), result caps, ordering, or behavior on zero matches.

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 matching condition is stated directly. It is arguably too terse rather than too long, 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 simple read tool the bar is low, but with zero annotations, zero schema coverage, and no output schema, the description is the only source of truth and omits return shape, match cardinality, and no-match behavior. An agent cannot predict the result structure before calling it.

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 map both parameters conceptually (column and value) and specifies that the value comparison is exact and case-insensitive. It does not explain what a valid column identifier looks like (name vs. index) or any other constraint beyond the schema's minLength.

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 resource (rows of the Calibvo dataset) and a specific retrieval condition (column equals value exactly), which is enough to tell it is an exact-match lookup. It does not name or contrast with any sibling, so the agent must infer that dataset_search is the non-exact alternative.

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 explicit when-to-use or when-not-to-use guidance is given, and no alternative tool is named. The word 'exactly' hints that this is the precise-match route versus a fuzzy search sibling, but that routing decision is left entirely 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?

With no annotations, the description must carry the behavioral load; it does partially by disclosing case-insensitive whole-cell substring matching and a hard 50-row cap. It says nothing about permissions, ordering of results, or what happens when more than 50 rows match (truncation semantics).

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 with zero filler. Matching semantics, scope, and cap all land before the reader needs anything else.

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 read tool with no output schema and no annotations, the description supplies the essentials: what matches, how matching works, and the result cap. Truncation/ordering behavior is the only notable omission.

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 in the schema, limit is not. The description compensates for limit by stating 'up to 50', which matches the schema's maximum. However, it adds nothing about the minimum or whether limit changes result ordering — thin compensation for the uncovered parameter.

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 on a specific resource: rows of the Calibvo dataset whose cells match a query, case-insensitively, capped at 50. An agent can distinguish it from dataset_stats/dataset_columns/dataset_row, though it never names a sibling to sharpen the boundary.

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, no alternatives named, no prerequisites. The agent must infer that this is the free-text lookup path and that dataset_row/dataset_top serve different access patterns.

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 burden and does add useful edge-case behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It omits error behavior for a missing/invalid column name and the exact shape/wording of the returned values.

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 that front-loads the returned statistics and offloads edge-case handling to a compact parenthetical. No wasted words, though the sentence is dense rather than cleanly 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?

No output schema exists, so the description correctly enumerates the returned statistics and covers data-cleaning behavior. It is nearly complete but does not address failure cases (missing column, empty dataset) that an agent may need.

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 has 0% description coverage on the single 'column' parameter, and the description only implies a column of the Calibvo dataset without specifying format (header name? index? case sensitivity?). It adds minimal meaning beyond the schema's string type.

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 the exact aggregate outputs (count, min, max, mean, median, sum) over a numeric column of the Calibvo dataset, which is specific enough to distinguish it from dataset_row/dataset_columns. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, so it falls 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?

There is no statement of when to use this versus alternatives (e.g. dataset_top, dataset_columns) and no prerequisites such as requiring a numeric column or what happens if the column is non-numeric. Usage must be inferred entirely from the description's content.

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 disclosure burden. It conveys directionality but says nothing about the result shape, ordering ties, the 50-row cap on limit, error behavior for non-numeric columns, or that this is a read-only operation — significant gaps for a tool with zero structured behavioral 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 compact sentence with the core behavior front-loaded and no wasted clauses. The trailing quoted phrase adds an intuitive gloss rather than filler, though it is slightly informal for a spec.

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 read tool with no output schema and no annotations, the description covers the ranking intent but omits the return format (row shape/fields), how limit interacts with the cap, and behavior on non-numeric or missing values. Adequate minimum, 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 only 33% (only 'ascending' is documented). The description partially compensates by explaining the high/low direction and that ranking is by a numeric column, but 'limit' is left entirely unspecified in both schema and description, so the gap is not fully closed.

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 outcome — returning the highest/lowest rows of the dataset ranked by a numeric column — plus an intuitive framing ('which is the most/least X'). It does not explicitly differentiate from siblings like dataset_row or dataset_stats, but the ranking/top-N 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 'which is the most/least X' framing implies the intended question type, but there is no explicit when-to-use/when-not guidance and no mention of alternatives (e.g. dataset_stats for aggregates vs dataset_row for a specific row). Usage is inferable but not stated.

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