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

With no annotations provided, the description carries the full behavioral burden. It does disclose the shape of the return (metadata plus a provenance banner) and that it is a schema-discovery/first-step call, implying a safe read, but it says nothing about permissions, caching, or whether the dataset selection is implicit — modest coverage for an annotation-free 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 sentences, front-loaded with the payload and closing with the ordering advice; nothing is padded. The opening enumeration is slightly list-heavy but every item maps to a real part of the response.

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 sketch the return, and it does enumerate the four returned elements. For a zero-parameter, side-effect-free inspection tool this is nearly sufficient, with only the exact output format left implicit.

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 the baseline is 4. There are no argument semantics to explain, and the description correctly focuses on what the call yields rather than inventing parameter guidance.

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 contents returned (columns, numeric flags, row count, provenance banner) for a named dataset, which is far more specific than a tautology. It is distinguishable from dataset_stats or dataset_provenance by the enumerated payload, though it doesn't explicitly contrast itself with those siblings.

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/context guidance that tells the agent when to invoke it relative to other dataset tools. It stops short of stating when not to use it or naming which sibling to prefer for other 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?

No annotations are provided, so the description carries the full behavioral burden. It conveys that multiple rows are returned in the given value order, but says nothing about result size limits (schema allows up to 10 values), failure behavior for unknown columns, or the shape/size of the returned rows. For a query tool with zero annotation coverage this 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?

A single compact sentence with no filler, and the key ordering semantics are surfaced at the end where they matter for a comparison tool. It is slightly front-heavy with dataset qualification rather than the action.

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 stand alone, and it adequately covers the input side (column + values + order) but leaves the return shape and edge cases undocumented. Minimum viable for a two-parameter read tool whose output an agent can inspect directly.

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 add real meaning: it clarifies that 'values' is matched against 'column' and that output order follows input order, which the schema does not state. It still omits details like whether column names must match dataset_columns output exactly and what the maxItems=10 constraint means in practice.

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 resource (rows of the Insurance by Profession dataset) and the filter semantics (rows whose column is any of the given values, in order), which is more specific than the title's vague 'Compare rows side by side'. However, it is a verbless noun phrase with no explicit action verb, and it does not distinguish itself from sibling filters like dataset_search, dataset_row, 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 trailing clause 'for "X vs Y" questions' implies the intended scenario and ordering behavior, giving some usage signal. It stops short of naming alternatives (dataset_search, dataset_top) or stating when this tool should NOT be used instead of them.

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 it does disclose the returned content (source, date, licence, citation). However it says nothing about whether results are static/cached, auth requirements, or how the citation should be formatted, so the behavioral picture is only partially filled in.

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 tightly written sentences; the returned fields are front-loaded and the usage cue follows. 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?

No output schema exists, but the description enumerates the returned fields, which is enough for a zero-parameter metadata lookup. Minor gap: no note on how the citation is delivered or formatted.

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 the baseline is 4. The description correctly implies no input is required beyond invoking it for the fixed Insurance by Profession dataset.

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 a specific resource (the Insurance by Profession dataset) and enumerates exactly what it returns: source, computation date, licence, citation. An agent can distinguish it from dataset_columns, dataset_stats and dataset_row, though the description never explicitly contrasts itself with those siblings.

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 an implied trigger condition (use it when you need to cite/attribute a value), but no explicit exclusions or named alternatives. Adequate but thin.

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 only discloses case-insensitive exact matching. It says nothing about whether the operation is read-only, how many rows can be returned, any row limits or pagination, or error behavior for an unknown column or value.

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 matching semantics front-loaded and no filler. It is a sentence fragment rather than a full statement of intent, which is minor but slightly reduces 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?

For a simple two-parameter lookup with no output schema and no annotations, the description is adequate but thin: it does not state the return shape beyond 'rows', whether results are capped, or how to supply a valid column name. A brief note on output and limits would close the gap.

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 for both parameters. The sentence 'where a column equals a value exactly (case-insensitive)' usefully explains the column/value pairing and matching rule, but does not clarify that 'column' must be a valid dataset column name or how such names are discovered (e.g., via dataset_columns).

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 the concrete resource (rows of the Insurance by Profession dataset) and the exact retrieval semantics (column equals value, case-insensitive), so an agent knows precisely what it fetches. It stops short of distinguishing itself from siblings like dataset_search or dataset_compare, which likely handle fuzzy or multi-column matching.

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 'equals a value exactly' phrasing hints at using this when the exact key is known, contrasted with a search-oriented sibling, but no when-to-use or when-not-to-use guidance is stated. No prerequisites or alternative tools are named.

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 usefully discloses case-insensitive matching across any cell and a hard 50-row result cap. However, it says nothing about ordering, pagination, or what happens on very broad matches, so behavioral disclosure 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 front-loaded sentence with the matching semantics and result cap; every clause earns its place and nothing is redundant.

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-only search over two parameters with no output schema, the description covers matching semantics and result bounding adequately. It is slightly thin on result ordering and truncation behavior, but nothing critical to correct invocation 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?

Schema coverage is only 50% (the limit parameter has no description), so the description must compensate. It partly does: 'case-insensitive' enriches the query parameter beyond the schema's 'text to look for in any cell', but 'up to 50' merely restates the schema's documented maximum for limit.

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 and resource: returns rows of the Insurance by Profession dataset whose cells match the query. The case-insensitive matching rule and 50-row cap make the operation unambiguous and clearly distinct from siblings like dataset_row, dataset_top, 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?

There is no explicit when-to-use guidance and no mention of alternatives such as dataset_top or dataset_row for other retrieval patterns. The agent must infer usage from the operation itself, with no exclusions or prerequisites 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?

No annotations are provided, so the description carries the full burden, and it delivers real behavioral detail: commas/currency are parsed, non-numeric rows are excluded and counted, and the exact set of returned statistics is enumerated. Missing only secondary traits such as behavior on large datasets or permission requirements (likely irrelevant for a read-only stat 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?

One sentence, front-loaded with the returned statistics and followed by the parsing/exclusion caveats. No filler, though the parenthetical runs on slightly and could be split for faster scanning.

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 usefully enumerates the return values and edge-case handling, which is what an agent needs to interpret results. It falls short only in not pointing to dataset_columns for discovering valid numeric column names, which would close the last gap for a 1-param 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?

Schema description coverage is 0% for the single parameter, so the description must compensate. It adds meaning by constraining the argument to a 'numeric column' of a named dataset, but it does not specify the expected column-name format, case sensitivity, or what happens when a non-numeric column is 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?

The description names a specific operation (compute count/min/max/mean/median/sum) on a specific resource (a numeric column of the Insurance by Profession dataset), so the agent knows exactly what it returns. It does not explicitly differentiate itself from siblings like dataset_top or dataset_compare, but the verb+resource pairing is unambiguous enough.

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 implied by the phrase 'numeric column' and by the stat list, so an agent can infer this is the tool for aggregate summaries. However, there is no explicit when-to-use/when-not guidance and no mention of alternatives (e.g. dataset_top for ranked values), leaving routing 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 carries the full behavioral burden and says almost nothing: not how many rows are returned, not the tie-breaking behavior, not whether the column must be numeric (only implied), and not that the result is bounded. It does convey the descending-first direction, which is a small but real disclosure.

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 essentially no padding; the quoted 'which is the most/least X' tail adds intent rather than repeating the title. Tight and readable.

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 thin parameter coverage, yet the description does not fill the gap: return shape, limit semantics, and default ordering are all unaddressed. For a ranking tool an agent could easily mis-size requests or misread 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?

Schema description coverage is only 33% (only 'ascending' is documented), so the description needs to compensate. It does imply the column must be numeric and describes the ascending/descending choice, but it never mentions the 'limit' parameter or its 1–50 cap, leaving two undocumented parameters.

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 Insurance by Profession dataset by a numeric column), plus the intent 'which is the most/least X'. Clear on its own, but it never distinguishes itself from siblings like dataset_row or dataset_stats, so the boundary depends on the agent's inference.

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 example use case ('which is the most/least X') implies when this tool is appropriate, but there is no explicit when-to-use guidance, no exclusions, and no named alternative among the many dataset_* siblings for e.g. bottom-N, per-group, or aggregate queries.

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 full behavioral burden. It discloses what content is returned (recipients, consent wording, confirmation) and that nothing is bought or guaranteed, but it never explicitly states this is a side-effect-free read operation for the tool itself — the 'nothing is paid' clause describes the enquiry, not this 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?

Short and front-loaded with the actionable 'Read first.' cue, but the 'not a purchase, not a guaranteed quote' framing largely duplicates the title, adding some 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 zero-parameter describe tool with no output schema, the description enumerates the returned content (recipients, consent wording, confirmation) well enough for an agent to know what to expect. The remaining gap is only that it never explicitly frames the tool's own read-only nature.

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 defines zero parameters, so there is nothing to document and the baseline is 4; the description correctly implies no input is needed.

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 this is a read/describe tool that explains what submit_enquiry does and returns the recipients, consent wording, and confirmation flow. It is clear about its subject matter, though it describes the tool somewhat indirectly (it explains another tool rather than saying plainly 'this returns a description of the enquiry process').

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 first' is an implied sequencing cue pointing the agent to consult this before submit_enquiry. However, there is no explicit statement of when to use it versus siblings like enquiry_fields, and no exclusions, so usage 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 burden and does well for a zero-parameter discovery call: it discloses the exact shape of the returned payload, including conditional content ('allowed options where there are any') and the key field's role as the submission identifier. It does not state whether the field set is static or versioned, but nothing is contradicted.

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 with no filler: the first enumerates the returned fields, the second states the downstream usage. The most actionable detail (keying answers by field key) is front-loaded in the second position without burying the payload description.

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 return values, and it does so field by field, including optional options lists. What remains unstated is anything about the value domain of 'type' or whether field sets vary by enquiry variant, but for a simple read-only enumerator this is essentially 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?

The tool takes no parameters, so the baseline is 4. The description goes further by explaining how the returned 'key' values map to the parameter keys expected by submit_enquiry, which is the only parameter-relevant semantics this tool has.

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 (the Insurance by Profession enquiry) and enumerates exactly what it exposes: key, label, type, required flag, help text and allowed options. It is clearly distinguishable from submit_enquiry, which it references, though it never explicitly contrasts itself with the sibling enquiry_describe.

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 states the follow-on action clearly: 'Pass answers to submit_enquiry keyed by field key,' which tells the agent this is the field-discovery step before submission. It gives that positive workflow context but stops short of an explicit when-not or a direct routing statement versus enquiry_describe.

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 does so: validation-then-summary behavior, the confirmation-token round trip, the double-consent requirement, and the crucial downstream fact that an email link must be clicked before any provider sees the data. It also spells out what consent actually means, which is the irreversible-action disclosure an agent needs.

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?

Front-loaded with purpose and the two negations, then the steps in order — a good structure for a longish description. It loses a point because the consent wording is quoted verbatim and duplicated from the consent parameter's schema description, and the paragraph is a single dense block rather than scannable steps.

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, but the description compensates by naming exactly what step 1 returns (summary, consent line, confirmation token) and what step 2 triggers (submission plus a verification email). For a stateful, consent-sensitive tool, nothing an agent needs to call it correctly 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?

Schema coverage is 100%, so baseline is 3, and the description genuinely adds meaning: answers are keyed by field key from enquiry_fields, consent must be true only for that specific wording, and confirmation is the token returned by step 1 and used only on the second call. The one small gap is that it never states what to do if a field key is invalid or an answer is missing.

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?

Opens with a specific verb+resource ('Submits an enquiry to Insurance by Profession') and immediately negates the two most likely misreadings ('NOT a purchase, NOT a guaranteed quote'). It also names the sibling tool enquiry_fields as the source of the answer keys, so an agent can place it against its siblings without opening any schema.

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?

Gives an explicit two-step protocol with the exact gating condition for each call: step 1 with answers+consent=true, step 2 'only if the person agrees' with the confirmation token. It also states what must be shown to the person between steps, which is guidance, not just description.

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