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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 full burden, and it does disclose the returned fields (columns, numeric flags, row count, provenance banner). However, it never states that the call is read-only/side-effect free, nor whether results are static or cached, which matters for an agent deciding how often to call it.

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, no filler. The payload is front-loaded and the second sentence is a genuinely useful routing instruction rather than padding.

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 sketch the return value, and it does so with a four-part enumeration covering the key fields. It is complete enough for a zero-parameter read tool, missing only notes on read-only safety and result stability.

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 schema has nothing to document and there is no parameter semantics to add; the baseline of 4 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?

Names the concrete resource and enumerates exactly what comes back: columns, which are numeric, row count, and the provenance banner of the Historia Pojazdu VIN dataset. It is clear this is schema introspection, though it does not explicitly distinguish itself from the sibling dataset_provenance, whose domain the 'provenance banner' overlaps.

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 ordering guidance with 'Call this first to learn the schema,' which tells the agent when in a workflow to reach for it. It stops short of naming alternatives or exclusions (e.g., when to prefer dataset_stats or dataset_provenance instead).

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 only discloses one trait: rows are returned in the order the values were given. It says nothing about read-only safety, result size, whether the returned rows are truncated, or any permission/scope 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?

A single front-loaded sentence that leads with what is returned. Untidy dash clause and slightly tangled phrasing, but no wasted content.

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, no-annotation, no-output-schema tool, the description covers filtering and ordering but not the shape of the result (how the two compared rows are returned, which columns come back), leaving a real gap for an agent constructing a 'vs' answer.

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, and it partially does: it clarifies that 'column' is the attribute matched against and 'values' is a set matched with OR semantics, in caller-specified order. It never says whether column must be an exact dataset header name, nor why values is capped at 10.

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 rows of a named dataset whose column matches any of the given values, preserving order. The 'compare' framing is loose (it filters rather than computes a comparison), and it doesn't explicitly distinguish itself from siblings like dataset_row or dataset_search, but the filter semantics are concrete enough to identify it.

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?

'For "X vs Y" questions' gives an implied usage context, which is more than nothing. However, it names no alternative tool and gives no condition for choosing this over dataset_row, 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?

With no annotations and no output schema, the description carries the full disclosure burden, and it does at least reveal the four fields the call returns. It stops short of stating that this is a non-mutating metadata lookup with no side effects, permissions, or cost, which is the kind of behavioural framing a zero-argument, unannotated tool should supply.

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 padding, and the resource being described is front-loaded before the guidance sentence. Every clause carries 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?

There is no output schema, so the description must convey the return shape, and it does so by listing source, date, licence and citation. For a simple, zero-parameter metadata lookup against an unannotated tool, this is close to complete, with only side-effect/permission framing 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?

The tool takes zero parameters and the schema coverage is 100%, so there are no argument semantics to explain. The baseline for a parameterless tool is 4; nothing in the description misrepresents the empty input.

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 Historia Pojazdu VIN dataset) and enumerates the metadata it returns: source, computation date, licence and citation. That distinguishes it clearly from numeric siblings like dataset_stats or dataset_columns, though it is framed as a content list rather than a precise verb+object statement.

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 one concrete condition for calling the tool (citation/attribution), which is genuinely useful. However, it never contrasts itself with the other nine dataset_* siblings or states when this is unnecessary, so the routing guidance stays at the implied level.

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 does disclose a genuine matching trait (exact, case-insensitive) that the schema cannot convey, and that the result is plural 'rows', but it is silent on permissions, result limits, pagination and return 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, front-loaded sentence with no filler; the filter semantics are stated up front and nothing is wasted. It is efficient, though the terseness leaves no room for the missing usage and behavioral details.

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 2-required-param tool with 0% schema coverage, no annotations and no output schema, the description is too thin. It never explains how an agent discovers valid column names or what the returned rows look like, leaving core gaps unaddressed.

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?

Both parameters have 0% schema description coverage, so the description must compensate. It does map 'column' to the field filtered on and 'value' to the compared literal and clarifies the comparison is exact and case-insensitive, but it gives no help on valid column names or value formatting.

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 concrete verb (look up) and resource (rows of the Historia Pojazdu VIN dataset) with a specific filter condition (column equals value, case-insensitive). It implies a distinction from the fuzzy-looking 'dataset_search' sibling via 'exactly', but never names that sibling 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?

There is no explicit when-to-use guidance or prerequisite, and no alternative is named. The word 'exactly' only weakly hints that this is the exact-match counterpart to a search tool; the agent must infer the routing decision on its own.

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 burden, and it does disclose useful behavior: matching is case-insensitive, it scans any cell, and results are capped at 50. However, it says nothing about ordering of results, pagination beyond the cap, or whether the operation is read-only and side-effect free, leaving real gaps for a dataset query 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?

A single sentence that front-loads the scope (which dataset), then the match semantics, then the result bound. No filler, no redundancy, everything 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 two-parameter search tool with no output schema and no annotations, the description covers the essentials: dataset scope, match rule, case sensitivity, and result cap. It could go further by describing the shape of a returned row or the ordering, 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 query parameter is documented in the schema, but limit has no schema description. The description compensates by stating the cap ('up to 50'), which explains the limit's effective max, and by clarifying that query matches any cell case-insensitively. That is meaningful added value beyond the structured fields.

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 (search) and resource (rows of the Historia Pojazdu VIN dataset) and defines the matching rule (cells containing the query, case-insensitive). It is clear on its own, but it does not explicitly position itself against siblings like dataset_row, dataset_top, or dataset_compare, so an agent must infer the distinction from semantics alone.

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

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

There is no statement of when to use this tool versus the nine siblings (e.g., dataset_top for ranked results, dataset_row for a single record, dataset_compare for diffing). Usage is only implied by the substring-match wording; there are no exclusions, prerequisites, or alternative routing 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?

No annotations are provided, so the description carries the full burden, and it does disclose non-obvious data handling: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. That is meaningful behavioral context beyond a restatement of the title. It stops short of describing response shape or behavior for an all-non-numeric column, keeping it out of 5 territory.

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 that leads with the returned metrics before the dataset-scoping parenthetical. Dense but no wasted clauses; the parenthetical on parsing/exclusion earns its place.

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

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

With no output schema, the description correctly enumerates the returned statistics, so the agent knows what comes back. The only gap is edge-case behavior (e.g., a column with zero numeric values) and how the excluded-row count is surfaced.

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% and the single 'column' property has only minLength, so the description must compensate. It partially does by implying the argument is a column of the Historia Pojazdu VIN dataset and must be numeric, but it gives no naming convention, case sensitivity, or example values.

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+resource (compute summary statistics for a numeric column) and enumerates the exact outputs returned (count, min, max, mean, median, sum). It is clearly distinguishable from siblings like dataset_columns or dataset_row, though it never names an alternative to route against.

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 mention of numeric columns and exclusion of non-numeric rows signals this tool is for aggregating numeric fields only. There is no explicit when-to-use guidance, no mention of alternatives for categorical columns, and no 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?

No annotations are provided, so the description carries the full behavioral burden. It conveys only the ranking semantics; nothing is said about read-only nature, the 50-row cap, tie/missing-value behavior, or what is 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 front-loaded sentence with no filler; the extremum semantics come first. Slightly odd quoting, but nothing 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 3-parameter tool with no annotations and no output schema, the description covers the core intent but leaves the limit behavior, ordering default confirmation, and result shape for the agent to infer from the schema alone.

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 33% — only 'ascending' is documented in the schema. The description adds meaning for the sort direction ('highest (or lowest)') and constrains the column to numeric, but the 'limit' parameter and its maximum of 50 are left unexplained beyond the schema's type bounds.

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

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

States a specific verb+resource ('highest/lowest rows ... by a numeric column') and names the dataset, so an agent can distinguish it from dataset_row and dataset_stats. It lacks an explicit contrast with siblings, which keeps it 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 paraphrase 'which is the most/least X' implies the usage context (extremum queries rather than full listing), but there is no when-to-use/when-not guidance and no named alternative such as dataset_stats or dataset_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 behavioral burden and does well: it discloses that nothing is bought, ordered or paid, that no quote is guaranteed, that it is free, and what the response contains (recipients, consent wording, confirmation method). It does not address auth, rate limits, or whether the described text is static, 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?

The operative instruction ('Read first') is front-loaded and the rest packs several distinct facts into two sentences without redundancy. It is slightly dense/run-on in the second sentence, but every clause carries 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 zero-parameter, no-annotation, no-output-schema tool, the description covers purpose, the key caveats, and roughly what is returned (recipients, consent wording, confirmation method). An agent has enough to decide to call it, though the exact return shape is only sketched.

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 per-parameter semantics to explain. The baseline for a parameterless tool applies, and the description appropriately spends its words on return content instead of inventing parameter detail.

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 function (describes what submit_enquiry does) and its scope, explicitly contrasting with the sibling submit_enquiry: 'not a purchase, not a guaranteed quote.' An agent can tell from the first line that this is the informational/read companion to submit_enquiry rather than the submission 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' clearly signals this should be consulted before submit_enquiry, and the title/description reinforce what the alternative (submitting) does and does not do. It stops short of an explicit 'do not call this to submit an enquiry' instruction or named alternatives, so it is clear context rather than full when/when-not routing.

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 reasonably: it is obviously a read-only lookup and it discloses the exact shape of what comes back. It does not mention whether the field set is stable, cached, or requires any authorization, which are minor gaps for a zero-parameter read.

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

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

Two sentences, both earning their place: the first defines the payload, the second routes the result into the next call. The field list is dense but front-loaded and free of filler.

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

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

There is no output schema, so the description must document the return value, and it does so field by field while tying the result to submit_enquiry. The remaining gap is the relationship to the enquiry_describe sibling, which is left unaddressed.

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 spends its words on the return payload instead, and the 'keyed by field key' note is the only semantic detail needed to consume the output.

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 — every field of the Historia Pojazdu VIN enquiry — and enumerates exactly what each entry contains (key, label, type, required flag, help text, allowed options). That is far more than a restatement of the title. It falls short of a 5 only because it never distinguishes itself from the sibling enquiry_describe, which an agent must choose between.

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

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

It gives concrete downstream context: answers obtained elsewhere are passed to submit_enquiry keyed by field key, which tells the agent where this tool sits in the workflow. It does not state when this tool should be called instead of enquiry_describe, so there is no explicit exclusion.

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 validation behavior, the returned summary/consent line/token, the double-submission requirement, the post-submit email link, and that no provider sees the enquiry until the link is clicked. It also reproduces the exact consent text the person must agree to.

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 most important framing ('NOT a purchase, NOT a guaranteed quote') and ordered step 1/step 2. It is long and the full consent sentence is repeated from the schema, which is minor redundancy, but nearly every sentence carries procedural weight.

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 nested-object, multi-call tool with no output schema, the description supplies the missing return-value semantics (summary, consent line, token) and the full cross-call workflow. Nothing an agent needs to invoke it correctly is absent.

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, but the description adds genuine meaning: answers are keyed by field key from enquiry_fields, consent is tied to a specific quoted Polish consent string, and confirmation is explicitly the token returned in step 1. It links parameters to the multi-call flow rather than just restating them.

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 ('submits an enquiry'), immediately disambiguates from a purchase or guaranteed quote, and names the provider/domain (Historia Pojazdu VIN). An agent can distinguish this from dataset_* siblings and enquiry_* helpers 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?

Spells out an explicit two-step protocol: step 1 with answers+consent=true, step 2 with the same answers plus the confirmation token and only after the person agrees. It also points to enquiry_fields for the field keys, giving the agent a clear routing rule.

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