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

No annotations are provided, so the description carries the burden; it discloses the full return surface (columns, numeric typing, row count, banner) and implicitly signals a safe, read-only introspection call. With zero parameters there is little additional behavior (auth, side effects, rate limits) that needs disclosing.

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 the payload description front-loaded and the actionable directive ("call this first") in the final position. Every clause earns its place; nothing is padded.

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

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

With no output schema, the description must describe return values, and it enumerates them (columns, numeric flags, row count, provenance banner) adequately for the agent to decide to call it. The precise shape/ordering of the banner is not specified, a minor gap for a schema-introspection 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?

The schema has zero parameters, so there is nothing for the description to disambiguate; the baseline of 4 applies. No parameter-level detail 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?

States a specific resource (the Rechner HQ dataset) and enumerates exactly what it returns: column names, numeric flags, row count, and provenance banner. This distinguishes it from dataset_row/dataset_search (content retrieval), though it partially overlaps dataset_stats and dataset_provenance without explicitly drawing 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?

"Call this first to learn the schema" gives explicit ordering guidance, which is genuinely useful for an agent planning a multi-step exploration. It does not, however, name an alternative or state when-not-to-use it, so it stops short of full when/when-not coverage.

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

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

With no annotations, the description carries the full behavioral burden, and it does disclose two important behaviors: filtering by any of the given values and preserving the order given. It does not state read-only nature, permission needs, result limits, or what happens when no rows match.

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 description is a single compact sentence with no wasted words, and it contains only one idea. Its structure is slightly weak because it opens with a noun phrase rather than the action, but it is appropriately sized for the tool.

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 read tool, the description conveys the core selection and ordering semantics. It does not explain parameter constraints or error behavior, though the input schema at least carries the structural validation rules.

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 conceptually explains both parameters by describing the column filter and the ordered list of values, but it omits constraints such as the minimum of 2 and maximum of 10 items and the string-only type.

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

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

The description indicates that the tool returns rows from the Rechner HQ dataset filtered by a column's values, and it hints at comparison use with "X vs Y" questions. However, it is phrased as a noun fragment rather than a clear verb+resource statement, and it does not explicitly distinguish itself from siblings like dataset_search or dataset_row.

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 "for 'X vs Y' questions" gives an implied usage scenario, which is better than no guidance. But it does not name alternatives or state when-not to use this tool, so the agent must infer how it differs from dataset_search or dataset_top.

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 does establish that the tool is non-mutating and informational through the fields it lists. However it says nothing about failure behavior, whether the metadata is cached, or how stale the computed date might be, leaving real gaps for a no-annotation tool.

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

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

Two sentences with no filler: the returned fields lead, and the usage rationale follows. Every clause earns its place and nothing is repeated from the title.

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 return contents, and it does by enumerating source, computed date, licence and citation plus the dataset's identity. Only the exact output shape (e.g., whether citation is a formatted string or structured fields) is unstated, which is a minor 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?

The tool takes zero parameters (empty properties object), so per the rubric the baseline is 4 and there is no parameter semantics to document. Nothing in the description misrepresents the absence of 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?

States a specific resource (the Rechner HQ dataset) and enumerates exactly what it returns: source, computation date, licence and citation. An agent can distinguish it from siblings like dataset_columns, dataset_stats or dataset_row without opening any schema, though it never names those siblings 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?

'Read this to attribute a figure correctly' supplies a concrete, actionable trigger for invoking the tool, which is more than most metadata tools offer. It stops short of stating when not to use it or naming alternatives, so it is clear context without exclusions.

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 usefully discloses exact-match, case-insensitive matching semantics, but says nothing about return shape, result limits, or behavior when the column does not exist.

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 constraint (exact, case-insensitive equality on a named dataset) is front-loaded. It is a fragment rather than a full sentence but loses nothing.

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 cover more than it does: it never states whether the result is a list, how many rows may come back, or what happens on a miss. For a lookup tool this leaves core calling behavior 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 description coverage is 0% — both parameters are bare strings with only minLength. The description partially compensates by explaining that 'column' identifies the dataset column and 'value' the equality target, and adds the case-insensitive matching rule, which the schema does not convey.

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 operation: returning rows of the 'Rechner HQ dataset' where a column equals a value. It is a clear verb+resource statement, but it does not distinguish this exact-key lookup from the sibling dataset_search, so an agent cannot confidently route between 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 explicit when-to-use guidance and no mention of alternatives, even though dataset_search exists as an obvious sibling. The 'exactly (case-insensitive)' phrasing hints at usage but never states when this tool is preferable to a 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 burden. It usefully discloses case-insensitive matching and a result cap ('up to 50'), but says nothing about ordering, pagination, permissions, or whether the operation is read-only — all of which an agent would want for a search 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 resource and matching semantics, with zero filler. Nothing could be trimmed 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?

For a two-parameter search with no annotations and no output schema, the description covers matching semantics and result size but omits return shape details (beyond 'rows'), ordering, and pagination behavior. Adequate but clearly incomplete.

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' has no schema description. The description partially compensates by stating the 50-row ceiling, but that bound is already in the schema (maximum: 50), so it adds little for 'limit' beyond restating the constraint.

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?

Specific verb (return/search rows) and resource (Rechner HQ dataset) with the matching rule stated. It does not, however, differentiate itself from siblings like dataset_row, dataset_top, or dataset_compare, 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?

No when-to-use guidance and no named alternative. The agent gets no cue for choosing this over dataset_row or dataset_top, and no exclusions or preconditions are 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 present, the description carries full disclosure and does meaningful work: it specifies the exact return contents (count, min, max, mean, median, sum), states that grouping commas and currency are handled, and discloses that non-numeric rows are excluded and counted. What remains unstated is the failure mode for an unknown or wholly non-numeric column and any permission requirements, which keeps it short of a 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?

A single sentence with the returned measures front-loaded and the edge-case handling parenthetically appended. Nothing is wasted, though the parenthetical packs several distinct behaviors tightly together.

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, the enumerated measures effectively document the return shape and the caveats cover common data quirks. It still omits error behavior for invalid or non-numeric column names and any access prerequisites, leaving real gaps for a tool whose structured fields carry no information.

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 schema documents nothing about the single 'column' parameter. The description partially compensates by implying the argument is a numeric column of the Rechner HQ dataset, but it never states the expected naming/format, so the gap is only partly filled.

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 summary statistics) on a specific resource (a numeric column of the Rechner HQ dataset) and enumerates the exact measures returned, so an agent knows precisely what it produces. It doesn't explicitly contrast itself with siblings like dataset_top or dataset_compare, but the resource and scope are distinctive enough to differentiate it in practice.

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 stated when-to-use, when-not-to-use, or named alternative. The agent must infer from the purpose alone that this is the tool for numeric summary statistics rather than dataset_top or dataset_columns; no routing guidance is provided.

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 of behavioral disclosure. It only hints at the ordering direction; it says nothing about the result shape, tie handling, default limit behavior, or what happens if the column is non-numeric or missing.

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 core operation front-loaded and no filler; only the redundant parenthetical and double em-dash construction keep it from being maximally clean.

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 annotations, no output schema, and only one documented parameter, the description is minimally viable: an agent can call it, but return format, tie-breaking, and the limit cap are left entirely to inference.

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% (just the ascending flag), so the description must compensate. It usefully constrains column to numeric values and clarifies the directional meaning, but says nothing about the limit parameter or its 50-row ceiling.

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 operation (returning highest/lowest rows) on a specific resource (the Rechner HQ dataset) ranked by a numeric column, and the "which is most/least X" gloss makes the intent immediately graspable. It does not explicitly contrast itself with siblings like dataset_stats or dataset_search, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: the "which is the most/least X" framing signals the kind of question this answers, but there is no explicit when-to-use guidance and no mention of alternatives such as dataset_stats for aggregates or dataset_search for lookups.

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 discloses the substantive outcomes of the enquiry flow (nothing bought/ordered/paid, no guaranteed quote, free) and enumerates returned content, but never explicitly states that this tool is a safe, side-effect-free read and requires no input — the safety profile is only implied by 'Read first.'

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?

Three tight sentences with the action cue ('Read first') front-loaded and no obvious filler. The bulk of the text explains submit_enquiry's behavior, but since that explanation is effectively the tool's payload, it is not wasted words.

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 annotations, no output schema, and zero parameters, the description correctly takes on the burden of explaining what the caller gets back (recipients, consent wording, confirmation steps). It is complete enough for a 0-param informational tool, with only the explicit read-only/no-input framing 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 there is no parameter semantics to document; the empty schema is self-explanatory. The baseline of 4 applies since the description cannot and need not add parameter meaning.

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

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

The description conveys that this is a read/info tool whose output documents what submit_enquiry does (human providers quoting directly, no purchase, no guaranteed quote) and what gets returned (recipients, consent wording, confirmation flow). It distinguishes itself from submit_enquiry by framing itself as the 'read first' companion, though the phrasing spends more time describing the sibling than the verb+resource of this tool itself.

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 explicit sequencing instruction that tells the agent to consult this before invoking submit_enquiry, and the sibling is named directly. It stops short of naming when-not-to-use conditions (e.g. after the human flow is already underway) or other alternatives like enquiry_fields.

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?

There are no annotations, so the description carries full behavioral burden. It discloses the returned field attributes (key, label, type, required, help text, allowed options) but does not state that the operation is read-only or side-effect-free, nor does it mention authorization or output structure.

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 resource description and followed by actionable downstream guidance. Every sentence earns its place with no 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?

With no output schema and no annotations, the description adequately explains what fields are returned and how to use them. It does not cover the relationship to enquiry_describe or explicitly confirm read-only behavior, leaving minor gaps.

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

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

The tool has zero input parameters, so the baseline is 4. The description adds relevant guidance about how the returned field keys should be used with submit_enquiry, but there are no parameters to document.

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 clearly states the resource: every field of the Rechner HQ enquiry, and enumerates the returned attributes. It does not explicitly distinguish this from the sibling enquiry_describe tool, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear downstream usage: 'Pass answers to submit_enquiry keyed by field key,' which tells the agent exactly how the output ties into the next step. It does not state when to use this versus enquiry_describe, so it lacks explicit alternatives or exclusions.

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 does so: it discloses that step 1 only validates and returns a summary/consent line/token, that step 2 commits the enquiry, that an email verification gate stands between submission and provider visibility, and what consent actually means. This is far more than the schema reveals.

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 critical caveat (not a purchase, not a quote) is front-loaded and the two steps read in order. It is a single dense paragraph and the German consent sentence is repeated verbatim from the schema, a minor redundancy, but almost 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?

There is no output schema, so the description must cover return values — and it does, naming the summary, the consent line and the confirmation token returned by step 1. For a stateful, human-in-the-loop write tool, nothing an agent needs to drive both calls 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 the baseline is 3, but the description adds real meaning the schema cannot: it explains that answers must be keyed by field key from enquiry_fields, and that confirmation is only supplied on the second call after human approval. It stops short of describing value formats, but the two-step semantics are the important addition.

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

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

States a specific verb+resource ('Submits an enquiry to Rechner HQ') and immediately negates what it is not ('NOT a purchase, NOT a guaranteed quote'). It also names the sibling dependency enquiry_fields for the answer keys, so an agent can place it among the dataset_* / enquiry_* tools without opening a 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 explicit sequencing: Step 1 with answers + consent=true, show the summary; Step 2 only if the person agrees, with the same answers plus the confirmation token. It also states the gating condition for downstream visibility (the person must click an emailed link before any provider sees it).

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