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

With no annotations, the description carries the full burden, and it usefully discloses the payload contents (columns, numeric flags, row count, provenance banner). However, it never states that this is a read-only/inspection operation, nor anything about caching, cost, or limits. Adequate but incomplete 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 short sentences, with the return payload and the call-first instruction front-loaded and no filler. The opening fragment ("The columns, which of them are numeric...") is slightly elliptical but still 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?

With no output schema and no parameters, the description must describe the return value, and it does so by enumerating the returned fields. An agent has enough to call it and interpret the result; only the exact response shape/format is 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 tool takes zero parameters, so there is nothing to disambiguate and the baseline is 4. The description correctly adds no parameter commentary, as none 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?

It names the specific resources returned (columns, numeric flags, row count, provenance banner) for a named dataset, so an agent knows exactly what it gets. It is largely distinguishable from dataset_stats/dataset_top, though the included provenance banner overlaps with the sibling dataset_provenance tool, which blunts the differentiation slightly.

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 explicit ordering/priority cue that routes the agent ahead of the other dataset_* tools. It does not state when NOT to use it or name a direct alternative, so it stops short of full 5-level guidance.

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 real trait — output order follows the order of the given values — which is non-obvious and valuable. But it omits that this is a read-only lookup, what happens with an unknown column or fewer than 2 / more than 10 values, and how many rows 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 front-loads the resource and scope, then appends the usage hint after a dash. Every clause earns its place, but the sentence is grammatically incomplete (no verb), which slightly hurts immediate parseability.

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 zero schema description coverage for two required parameters — the description should be doing much more work. It never says what a returned row contains, how many rows come back, or how errors are surfaced, leaving an agent to guess at the response shape.

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

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

Schema description coverage is 0%, so the description must compensate, and it does explain the column/values relationship and that result order mirrors input order. It still leaves the matching semantics opaque (exact vs partial string match, case sensitivity) and never mentions the 2–10 item constraint enforced by the 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 fragment states the resource (rows of the Wärmepumpe Kosten Europa dataset) and the filter semantics (column matches any of the given values, order preserved), so an agent can infer it fetches rows for comparison. However, it is grammatically verbless and never distinguishes itself from siblings like dataset_row, dataset_search, or dataset_top, which also return rows.

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 usage context (side-by-side comparison of multiple entities), which is genuinely useful routing information. But no alternative tool is named, no when-not-to-use is given, and it does not say how this differs from dataset_search for the same comparison need.

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. It discloses the exact payload (source, date, licence, citation) for a tool with no output schema, which is the key behavioural fact. It adds nothing about staleness of the computed date or licence caveats, so it is not exhaustive.

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, zero filler, with the returned fields listed up front and the actionable guidance second. Nothing could be removed without losing information.

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 convey what comes back, and it does list the four returned items. Adequate for a simple metadata lookup, though an agent gets no hint about response shape or whether licensing text is a full deed or a short label.

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 nothing for the description to disambiguate. Baseline for a parameter-less tool applies.

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

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

The description names the exact resource (the Wärmepumpe Kosten Europa dataset) and enumerates the four things it returns: source, computation date, licence, citation. That clearly separates it from siblings like dataset_columns or dataset_stats. It stops short of a crisp verb, reading as a noun phrase rather than 'Return the...', which keeps it out of the top band.

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 condition for invoking the tool. It does not name alternatives or state when not to use it, but for a zero-parameter metadata lookup the context 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 provided, the description carries the full disclosure burden; it does reveal one important behavioral trait, that matching is exact but case-insensitive, which an agent could not infer from the schema. It says nothing about return shape, behavior on zero matches, invalid columns, or result limits.

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 matching semantics front-loaded and no filler. The only slightly awkward element is the hard-coded dataset name inside a generically named tool, but the sentence itself 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?

Complexity is low (two required string params, no nested objects, no output schema), so the description need not explain return values. Still, with no annotations and 0% schema coverage it leaves open how to obtain valid columns and what happens on no match, leaving the definition merely adequate.

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 roles of the two parameters (column = the field compared, value = the literal matched case-insensitively), which adds real meaning over bare minLength-1 strings. It still does not indicate how to discover valid column names or whether column is validated against the dataset 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 concrete operation (retrieve rows of a named dataset where a column exactly equals a value) and specifies the matching rule (exact, case-insensitive), which separates it from a fuzzy sibling like dataset_search. It is not a tautology of the name/title. However, it never explicitly differentiates itself from the other dataset_* siblings beyond the matching 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 phrase 'exactly (case-insensitive)' implies the tool is for precise lookups rather than ranked/fuzzy retrieval, which hints at when to prefer it over dataset_search. But there is no explicit statement of when to use this versus alternatives, and no mention of the prerequisite of knowing a valid column name (dataset_columns). 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 full load; it does disclose useful behavior beyond the schema: matching is case-insensitive, it scans all cells, and results are capped at 50. However, it says nothing about ordering, what happens when more than 50 rows match, or the shape of returned rows.

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 scope (dataset), predicate, and cap all appear immediately.

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 annotations and no output schema, this covers the essentials of what is searched, but leaves real gaps: result ordering, tie-breaking when more than 50 rows match, and the returned row structure are 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?

Schema coverage is 50% (only query is documented in the schema). The description adds meaning for query ('case-insensitive', 'any cell') but the limit parameter's effect is only alluded to via 'up to 50', which largely restates the schema's maximum of 50.

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 verb/action (return rows), the resource (the Wärmepumpe Kosten Europa dataset), and the match predicate (cells containing the query, case-insensitive). It is far more specific than the title, but it does not explicitly contrast itself with siblings like 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?

It conveys the implied scenario (you have a text fragment to locate) but never states when to choose this over dataset_row, dataset_top, or dataset_compare, nor any when-not condition. No routing guidance is given.

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 real work: it discloses that grouping commas and currency symbols are parsed, and that non-numeric rows are excluded and counted. What is not covered is error behavior for a non-numeric column or the response shape.

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 computed statistics front-loaded and edge-case handling tucked into a parenthetical. Every clause adds information and nothing is repeated from structured fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description usefully enumerates the exact return values and the exclusion/counting behavior, so an agent knows what it will get back. The remaining gap is parameter format and the fact that the valid column list must come from dataset_columns.

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% and the single 'column' parameter has no description, so the description must compensate. It does so partially by implying the value must be a numeric column of the named dataset, but gives no format details (header name form, case sensitivity, source of valid names).

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 resource ('a numeric column of the Wärmepumpe Kosten Europa dataset') and enumerates exactly what is computed (count, min, max, mean, median, sum), which separates it from siblings like dataset_top or dataset_row. It never names a sibling explicitly, so it stops short of 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 explicit when-to-use guidance, no indication of when to prefer this over dataset_top, dataset_compare, or dataset_row, and no prerequisites mentioned. Usage is only inferable from the fact that it computes 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?

No annotations are provided, so the description carries the full behavioral burden. It conveys that ranking is by a numeric column and the highest/lowest can be chosen, but says nothing about read-only semantics, tie handling, whether the limit silently caps results, default sort order, or what the returned rows contain.

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 ranking action stated first and the use-case gloss appended. It is tight and wastes no words, though the em-dash gloss is slightly informal rather than adding operational detail.

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 read-only ranking tool with no output schema, no annotations, and low schema coverage, the description is only partly sufficient. It never says what a 'row' return looks like, what happens when fewer rows exist than the limit, or that limit defaults/exists at all.

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%, so the description must compensate. It usefully constrains 'column' to a numeric column — a restriction the schema does not encode — and its 'highest (or lowest)' phrasing maps to the ascending flag. It supplies nothing about the 'limit' parameter, including the 1–50 range, leaving one of three parameters unexplained.

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

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

States a specific operation (return the highest/lowest rows) on a named resource (the Wärmepumpe Kosten Europa dataset) ranked by a numeric column. An agent can distinguish it from dataset_row/dataset_stats/dataset_search by the ranking behavior. It does not explicitly name a sibling it is not, so it falls short of 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 quoted 'which is the most/least X' framing gives an implied use case for selecting this tool over a plain row lookup. However, there is no explicit when-to-use/when-not guidance and no alternative sibling named, leaving the agent to infer when top-ranking beats stats or search.

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 well: it states nothing is bought, ordered or paid, no quote is guaranteed, and the service is free, plus it discloses the return content (recipient details, consent wording, confirmation steps). The only gap is that it never states the tool is a read-only, side-effect-free lookup in its own right.

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 the imperative "Read first." and then three compact sentences that each convey distinct information (what it describes, what does not happen, what is returned). Mildly meta in phrasing but no real waste.

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 must explain the return content, and it does: recipient identity, consent wording, and confirmation steps. It is complete enough to call the tool safely, though it could more explicitly characterize the return format.

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 further for the description to disambiguate.

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 purpose: this tool plainly states what submit_enquiry does, including who receives the details, the consent wording, and the confirmation flow. It distinguishes the enquiry mechanism from a purchase or guaranteed quote. It is clear, though it frames itself as an explainer of a sibling (submit_enquiry) rather than directly naming its own resource.

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" gives an explicit sequencing cue relative to submit_enquiry, telling the agent to consult this before submitting. Context is clear, but no explicit exclusions or comparison against enquiry_fields (a plausible alternative for field-level questions) are given.

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 and no output schema, the description carries the full disclosure burden, and it does describe the shape of the returned records (field key, label, type, required flag, help text, allowed options). It is a read-only introspection call, so the main behavioral question is what comes back, which is answered. Minor gaps: no statement about ordering, stability, or whether options list presence is conditional beyond "where there are any".

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: the first front-loads the returned content, the second front-loads the action. No filler, no repetition of the tool name, 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?

For a zero-parameter, no-output-schema metadata tool, an agent needs to know what it returns and what to do with the result. Both are stated explicitly, and the handoff to submit_enquiry closes the workflow loop.

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 nothing to disambiguate and the baseline is 4. The description correctly implies no input is needed by describing only outputs and downstream usage.

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 resource (the fields of the Wärmepumpe Kosten Europa enquiry) and enumerates exactly what is returned: key, label, type, required, help text, options. An agent can tell it is a metadata/introspection tool rather than a data fetcher. It does not distinguish itself from the sibling enquiry_describe, so sibling differentiation is left to 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?

"Pass answers to submit_enquiry keyed by field key" gives a concrete workflow context: call this to learn the keys, then submit. That is a clear usage link to an alternative sibling. There is no explicit "use this instead of X when..." or exclusion statement, keeping it below 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 delivers: it discloses the two-phase token flow, that submission is not a purchase or quote, that the person must click an emailed link before any provider sees the enquiry, and the precise consent semantics. This is unusually rich behavioral context for a mutation 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?

Front-loads the critical 'NOT a purchase' framing and orders the steps logically, so it reads well. It is somewhat dense and repeats the consent string already present in the schema, which slightly dilutes conciseness.

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 happens after step 2 (email with a verification link). For a nested-object, two-phase 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 the baseline is 3, but the description goes further by tying each parameter to the workflow: answers are keyed by field key from enquiry_fields, consent must be true, and confirmation is the token returned from step 1. That flow relationship adds meaning beyond the per-field schema text.

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) and resource (enquiry) and immediately disambiguates from what it is not (NOT a purchase, NOT a guaranteed quote). It also names the sibling it depends on (enquiry_fields) for the field keys, so an agent can distinguish it from enquiry_describe/enquiry_fields without opening schemas.

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?

Lays out the exact two-step invocation: step 1 with answers+consent, step 2 only 'if the person agrees' with the same answers plus the confirmation token. It also names the prerequisite source (enquiry_fields) and the required user-facing actions, leaving little to inference.

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