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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. It usefully enumerates the return contents (which is valuable given there is no output schema), but never states that the call is read-only, side-effect free, or takes no arguments. Adequate but not rich behavioral 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?

Two sentences, zero waste, and the return payload is front-loaded before the 'call this first' directive. 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?

Because there is no output schema, the description must convey the return shape, and it does so explicitly (columns, numeric flags, row count, provenance banner). Minor gap: it does not describe the response format or how many columns to expect.

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 this scores 4. The description correctly implies a no-argument call and adds nothing that would confuse the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource (the Crypto Exchange Compare HQ dataset) and enumerates exactly what it returns: columns, which are numeric, row count, and the provenance banner. It is clearly distinct from dataset_provenance and dataset_stats by content, though it 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?

"Call this first to learn the schema" gives explicit ordering guidance, which is real usage direction for an agent exploring an unfamiliar dataset. It stops short of saying when not to use it or pointing to dataset_stats/dataset_top for related needs, so it is clear context without alternatives.

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

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

With no annotations, the description carries the full behavioral burden. It discloses one meaningful trait beyond the schema: results follow the order of the supplied values. It omits whether the call is read-only, what happens when a value has no matching row, and whether duplicate matches are collapsed.

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 dense sentence with the core behavior front-loaded and no filler. The phrasing around 'whose column is any of the given values' is slightly clumsy but not wasteful.

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 low-complexity, 2-param tool with no annotations and no output schema, the description covers purpose and use case but leaves return shape under-specified (full rows? which columns? ordering guarantees when values are missing). Adequate but with clear gaps an agent would want filled.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It does explain the roles of both params ('column' is matched against 'values'), which is genuinely useful, but it never states that `column` must be an exact column name or that `values` is bounded to 2-10 entries as the schema requires.

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 concrete operation (return the rows of the Crypto Exchange Compare HQ dataset whose `column` matches any of the given `values`, preserving order) and names the intended scenario ('X vs Y' questions). The resource and behavior are unambiguous, but it never distinguishes itself from dataset_search or dataset_row, so an agent must infer 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?

The 'for X vs Y questions' clause implies the comparison scenario, giving some usage signal. However, it offers no explicit when-not-to-use guidance and never names dataset_search or dataset_row as alternatives, leaving the agent to guess which sibling handles a non-comparison lookup.

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 disclosure burden. It signals a read-only operation ("Read this") and lists the returned metadata, but says nothing about freshness guarantees, whether the licence/citation is stable, or any access requirements. For a zero-parameter metadata lookup the risk surface is small, so this is adequate but 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 short sentences with no filler; the list of returned fields comes first and the purpose ('attribute a figure correctly') closes it. The only minor cost is a slightly run-on enumeration of four fields in one sentence.

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 convey the return shape, and it does by naming the four provenance fields. With zero inputs and a simple metadata payload, an agent has enough to call this correctly; a note on citation formatting would be the only plausible addition.

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 there is nothing for the description to disambiguate; the baseline for a zero-parameter schema applies. No param semantics are needed or 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?

The description names a specific resource (the Crypto Exchange Compare HQ dataset's provenance) and enumerates exactly what it returns: source, computed date, licence, and citation. That is clearly distinct from the numeric/aggregation siblings like dataset_stats or dataset_compare, 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?

"Read this to attribute a figure correctly" implies the trigger condition (you need to cite or attribute a number), which is genuine usage guidance. It does not state when not to use it or reference any alternative, so the guidance stays implied rather than explicit.

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 one genuinely useful trait, that matching is exact and case-insensitive, but says nothing about result limits, ordering, pagination, or what happens when no row matches or the column name is invalid.

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: the matching rule and its case-insensitive caveat are both stated. Wording is slightly convoluted ('The rows of the X dataset where...') 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 two-parameter read tool with no output schema and no annotations, an agent still lacks the return shape (row objects? a single row?), result caps, and error behavior when the column does not exist. The statement of the exact-match, case-insensitive rule is the main substance the description contributes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so both parameters ('column' and 'value') are documented only by the description. It correctly conveys that 'value' is compared for equality against the named 'column' and that the comparison is case-insensitive, which partially compensates, but it does not clarify whether 'column' must be an exact schema column name or whether match/name casing matters.

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 'Crypto Exchange Compare HQ' dataset filtered by an exact column/value match. It is clear what the tool does, though it does not distinguish itself from the sibling dataset_search, which an agent would likely consider interchangeable for this kind of lookup.

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 guidance and no mention of alternatives. The phrase 'equals a value exactly' implicitly contrasts with a fuzzy/full-text search such as dataset_search, but the description never names that sibling or states the condition under which an agent should choose one over the other.

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 that matching is case-insensitive and across any cell, but says nothing about read-only nature, behavior on zero matches, or whether results are truncated silently at the cap.

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 scope. Nothing is wasted or buried.

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 should say more about the return shape (rows include which columns? how are they structured?) and truncation behavior. It is minimally adequate but leaves the agent guessing about 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 coverage is 50%: 'query' is documented but 'limit' is not, and the description's 'up to 50' merely restates the schema's maximum of 50. The only real added semantic is that matching is case-insensitive, which the schema omits.

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 (search), resource (rows of the Crypto Exchange Compare HQ dataset), and matching criterion (cells containing the query). It is distinguishable from siblings like dataset_row or dataset_stats, 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?

There is no guidance on when to use this tool versus alternatives such as dataset_row, dataset_compare, or dataset_top, and no exclusions or prerequisites are stated. Usage must be inferred purely from the one-line 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, the description carries the full burden, and it does disclose real data-handling behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. However, it says nothing about permission requirements, failure modes (e.g. a column with zero numeric values), or the shape of the returned 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?

A single efficient sentence with the returned metrics front-loaded. The parenthetical caveat about commas, currency, and excluded rows is dense but 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 one-parameter tool with no output schema and no annotations, the description is nearly self-sufficient: it enumerates the exact statistics returned and the edge-case handling. Only the column-name source and error behavior are left unstated.

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 single 'column' parameter has 0% schema description coverage, so the description must compensate. It does convey that the column must be numeric and that it belongs to the named dataset, but it does not explain valid column naming or point to dataset_columns for discovery.

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 operation (count, min, max, mean, median, sum) on a specific resource (a numeric column of the Crypto Exchange Compare HQ dataset). An agent can distinguish this from siblings like dataset_top, dataset_compare, and dataset_row, though no sibling is named explicitly.

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 does it state prerequisites such as needing a valid column name. Usage is only weakly implied by the word 'stats'.

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 conveys the ranking concept but omits return format, how the limit interacts with the ordering, whether the column must be numeric (only hinted at), and any error or empty-result 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?

A single front-loaded sentence with no wasted words; the scope and intent land immediately. It is perhaps a touch terse given the undocumented parameters, but structurally efficient.

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 and no annotations, so the description is the only guidance. It names the dataset and purpose but leaves the return shape, limit semantics, and valid column requirements unexplained for a 3-parameter query 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 low (33%), so the description must compensate. It usefully constrains 'column' to a numeric column and implies the ascending direction via 'highest (or lowest)', but says nothing about the 'limit' parameter or its 1-50 bound, leaving that to the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — ranking rows of the Crypto Exchange Compare HQ dataset by a numeric column — and clarifies the intent with the 'which is the most/least X' framing. It is clear what the tool does, though it does not explicitly contrast itself with siblings like dataset_stats 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 'which is the most/least X' example implies the question type this tool answers, giving implied usage. However, it never states when to use it versus dataset_stats, dataset_search, or dataset_row, and offers no exclusions or prerequisites.

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 key behavioral traits: nothing is bought/ordered/paid, no quote is guaranteed, and the service is free. It also previews the returned content (recipients, consent wording, confirmation). It stops short of stating read-only-ness or any auth requirement explicitly, so it is not a full 5.

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 imperative 'Read first.' before the substance. The triple 'bought, ordered or paid' is mildly redundant but reinforces the non-commitment point at negligible cost.

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 tool with no output schema and no annotations, the description covers the essentials: what it does, that it has no side effects or cost, and roughly what it returns. Since there is no output schema, it does the work of previewing the return payload, which is the right call.

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. There is nothing for the description to disambiguate, and it correctly says nothing about inputs.

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 verb-and-resource: it returns a plain-language explanation of what submit_enquiry does, plus the recipient list, consent wording and confirmation flow. It implicitly separates itself from submit_enquiry (which performs the action) and enquiry_fields, though it never names those siblings directly.

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 a directional hint that this should be consulted before submit_enquiry, which is useful sequencing guidance. However, it names no alternative tool, no exclusion, and no explicit condition under which this should be skipped, so usage remains 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 the return payload shape and the linkage to submit_enquiry, which is useful behavioral context, but it never states that this is a read-only, zero-parameter operation or anything about auth, caching, or limits. For a trivial no-arg getter this is acceptable, 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 sentences, front-loaded with what is returned and closed with the actionable usage pointer; nothing is padded. The colon-separated list of returned attributes is dense but every item 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?

There is no output schema, so the description must explain return values and it does so explicitly. Combined with the cross-reference to submit_enquiry, an agent has what it needs; only minor gaps (read-only nature, error behavior) remain.

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 does not invent parameter semantics, and instead explains that the field keys it returns are the keys to use when calling submit_enquiry.

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 makes clear this returns the full field schema of the Crypto Exchange Compare HQ enquiry, enumerating exactly what comes back (key, label, type, required, help text, options). It does not use an explicit verb ('List'/'Get'), and it does not directly distinguish itself from the sibling enquiry_describe, but the returned-content list makes the purpose 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 'Pass answers to submit_enquiry keyed by field key' names the sibling this tool feeds into and the condition that selects it, giving clear workflow context. There is no explicit when-not-to-use guidance or mention of enquiry_describe as an alternative, so it stops short of a 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 burden and does so: it discloses the two-phase validation, the returned confirmation token and consent line, the required human approval gate, the downstream email with a click-to-confirm link before any provider sees the enquiry, and the exact consent wording. This is unusually rich disclosure of side effects and auth/consent requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening clause front-loads the critical 'not a purchase' distinction, and every sentence carries load (flow, token, email link, consent text). It is dense and slightly long, but no sentence is expendable; a minor trim of the repeated consent wording could tighten it.

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 the description compensates by describing what step 1 returns (summary, consent line, confirmation token) and what step 2 triggers (email with a confirmation link). For a two-step, nested-object tool this is complete enough to invoke 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 100%, so baseline 3 applies, but the description adds real meaning: 'answers' are keyed by field key from enquiry_fields, consent must be true only after the person agreed, and the confirmation token is the step-1 output replayed in step 2 to complete submission. That binds the three params into the flow better than the schema alone.

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 (submits) plus resource (enquiry) and immediately scopes it with negatives — NOT a purchase, NOT a guaranteed quote — which disambiguates it from any transaction-flavored sibling. An agent can distinguish it from enquiry_describe/enquiry_fields, which only inspect fields and metadata.

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?

Explicitly prescribes the when: Step 1 with answers and consent=true to validate, then Step 2 only if the person agrees. The condition selecting the second call and the show-the-summary requirement are stated, leaving nothing to inference.

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