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

No annotations exist, so the description carries the full burden. It discloses the return contents (columns, numeric flags, row count, provenance banner), which is helpful, but never states that this is a read-only, side-effect-free introspection call or describes the response shape beyond a field list. Adequate but with 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 tight sentences, with the returned contents front-loaded and the guiding imperative ("Call this first") closing it. Every clause earns its place with no filler.

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

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

With no output schema, the description enumerates the key return fields (columns, numeric flags, row count, provenance banner) so the agent knows what to expect. For a zero-parameter introspection tool this is nearly complete, though it omits any note on response size or structure.

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

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

The tool takes zero parameters, so the baseline is 4. The description correctly implies a parameterless, whole-dataset introspection call with nothing further 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?

States a specific resource and enumerates exactly what is returned: columns, numeric flags, row count, and provenance banner. It is clearly a schema-introspection tool rather than a data-fetching one, though it does not explicitly differentiate itself from overlapping siblings like dataset_stats or dataset_provenance.

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 a clear sequencing instruction that tells the agent when to reach for this tool ahead of the others. It stops short of naming when-not to use it or pointing to specific alternatives, but the ordering guidance is explicit and actionable.

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 supplied, so the description carries the disclosure burden alone. It does add real behavioral context beyond the schema: results preserve the order in which values were given, and the tool is scoped to one specific dataset. It says nothing about return shape, whether the match is exact/case-sensitive, or the 2–10 value limit's consequences.

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 ordering guarantee and the usage hint both earn their place. The em-dash construction is slightly dense 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?

With no output schema and no annotations, the definition leaves important gaps: the shape of returned rows, match semantics (exact vs. substring, case), and what happens at the 10-value cap are all unstated. For a read tool with zero structured field coverage, more disclosure was needed.

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

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

Schema description coverage is 0%, so the description must compensate for both parameters. It partially does: it explains that 'column' selects the matching column and 'values' are matched with any-of semantics and their order matters. It does not say what column names are valid (a sibling, dataset_columns, presumably lists them) or explain the min/maxItems boundary beyond what the schema already enforces.

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

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

The description names the resource (rows of the Miniature Paints Compare dataset), the retrieval condition (column matches any of the given values), and the ordering guarantee, so an agent can tell what comes back. It does not explicitly differentiate itself from siblings like dataset_search or dataset_top, but the scoping to a named dataset and the ordered multi-value match make the intent clear. Not a tautology of the name – it clarifies that 'compare' is implemented as an ordered row fetch.

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' gives implied usage context, which is genuinely helpful framing. However, no alternatives are named: an agent is not told when to prefer dataset_search, dataset_row, or dataset_top over this tool, nor any preconditions (e.g. valid column names). Guidance is inferred 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?

No annotations are supplied, so the description carries the full burden. It discloses what the tool returns (source, date, licence, citation) and implicitly signals a read-only metadata lookup, but never states that it is non-mutating, takes no arguments, or has no side effects. Useful but incomplete for a zero-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, zero filler, and the payload enumeration is front-loaded before the call-to-action. 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?

With no output schema, the description must describe return values, and it does list the four fields returned. For a trivial zero-parameter lookup this is nearly complete; only the read-only nature of the call is 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 nothing for the description to disambiguate; the 4 baseline applies. The description correctly does not invent or imply any 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 names the specific resource (the Miniature Paints Compare dataset's provenance) and enumerates the payload contents: source, computed date, licence and citation. That is well beyond a restatement of the name. It does not explicitly contrast itself with siblings like dataset_columns or dataset_stats, so it lands at 4 rather than 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?

"Read this to attribute a figure correctly" gives a real use condition, but there is no statement of when NOT to use it, no mention of prerequisites, and no named alternative among the nine sibling tools. Usage is implied rather than spelled out.

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 that matching is case-insensitive, which is genuinely useful, but says nothing about row limits, whether multiple matches are returned ('rows' is plural), behavior on zero matches, or whether invalid column names error out.

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 resource and the match condition front-loaded and no filler. The slightly convoluted 'rows of the ... dataset where ...' phrasing is the only minor deduction.

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

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

For a simple two-parameter lookup with no output schema or annotations, the description covers the core operation but leaves gaps an agent would care about: available column names, whether results are bounded, and what an empty result means. Adequate but not complete.

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

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

Schema description coverage is 0%, so the description must compensate. It does map the two parameters semantically (column to compare, value to compare against) and adds the case-insensitive exact-match semantics the schema lacks. It still omits how valid column names are discovered, which is notable given the dataset_columns sibling.

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 resource (rows of the Miniature Paints Compare dataset) and the retrieval condition (a column equals a value exactly, case-insensitive). It implicitly distinguishes itself from fuzzy sibling tools like dataset_search via the 'exactly' qualifier, but never names an alternative outright.

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 title ('Look a row up by an exact key') and the phrase 'equals a value exactly' imply when to use it: when the agent already knows the exact key value. However, no explicit when-to-use/when-not guidance or named alternatives (e.g. dataset_search for partial matches) appear in the description itself.

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 supplied, so the description carries the full burden. It discloses that matching is case-insensitive and substring-based across any cell, which is genuinely useful behavior. It does not confirm return shape, ordering, default limit when 'limit' is omitted, or whether results are truncated silently at 50.

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 that front-loads the resource and matching condition, then the cap. No filler, no redundant restatement of the tool name.

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 account for returns; it says 'Rows of the ... dataset' but never sketches the row structure, which an agent must infer from sibling tools like dataset_columns. It is minimally sufficient for a 2-parameter, read-only lookup tool but leaves the response shape 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 only 50% (the 'query' param is documented, 'limit' is not), so the description must compensate. It adds matching semantics ('case-insensitive', 'contain') and the 'up to 50' ceiling that clarifies the limit parameter, but it never states the default limit or that limit selects how many rows are returned.

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 ('Rows ... whose cells contain the query') and resource ('the Miniature Paints Compare dataset'), with the matching scope (case-insensitive, substring in any cell) spelled out. This distinguishes it from sibling tools like dataset_row, dataset_top, and dataset_compare without naming them 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?

Usage is implied by the matching semantics: use it when you have a text query and want matching rows. It never names alternatives such as dataset_columns (for discovering fields) or dataset_row (for a single record), so no explicit when/when-not routing 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?

With no annotations, the description carries the full burden and does useful work: it discloses data-handling rules (grouping commas and currency are normalized, non-numeric rows are excluded and counted) rather than just implying them. It still omits error behavior for an invalid column name and does not explicitly state that the operation is a read-only computation.

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 dense, front-loaded sentence that lists the outputs first and appends the caveats afterward. Every clause earns its place with no filler.

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

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

There is no output schema or annotation coverage, so the description must supply everything. It usefully enumerates the returned metrics and the data-cleaning semantics, but leaves out how to identify the column and how errors are surfaced, which matters for a tool with a single untyped string parameter.

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

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

Schema description coverage is 0% for the single required 'column' parameter, so the description must compensate. It only says the column is numeric; it never explains whether the value is a header name, index, or case-sensitive label, leaving the agent to guess the correct identifier format.

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 metrics computed (count, min, max, mean, median, sum) against a specific resource (the Miniature Paints Compare dataset column), so the agent knows precisely what it gets back. It does not explicitly contrast itself with siblings like dataset_columns or dataset_row, but the metric list is specific enough to distinguish 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?

Usage is implied rather than stated: by saying stats apply to a 'numeric column', it signals the precondition that the column must contain numbers. There is no explicit when-to-use versus dataset_columns/dataset_top, and no statement about what happens if the agent points it at a non-numeric column.

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 does not disclose the limit ceiling of 50, that the column must be numeric, default ordering, tie handling, or what the returned rows look like — significant gaps for a ranking 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 tight sentence with the resource and ordering direction front-loaded. No filler, though the quoted 'which is the most/least X' phrasing is more decorative than informative.

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 two of three parameters undocumented, the description should explain default ordering, the limit cap, and the shape of the result. It leaves all of that to inference, which is inadequate for correct invocation.

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 33%. The description's 'numeric column' usefully constrains the column parameter, but limit is undocumented in both schema and description, and the ascending direction is already fully described in the schema, so the description adds little net 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 states a specific operation ('highest (or lowest) rows ... by a numeric column') and frames the intent as 'which is the most/least X'. It is clear what the tool does, but it never distinguishes itself from siblings like dataset_stats or dataset_search, which could plausibly serve overlapping 'top values' needs.

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 'which is the most/least X' implies a use case, but there is no explicit statement of when to pick this over dataset_stats or dataset_search, and no exclusions or prerequisites. Usage is only inferable.

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 exist, so the description carries the behavioral burden, and it does add real context: nothing is bought, ordered or paid, no quote is guaranteed, and the process is free. It also previews the returned content (recipients, consent wording, confirmation). It does not explicitly state read-only-ness beyond the 'Read first' hint.

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 key directive ('Read first') front-loaded; every clause carries information about scope or returns. Slightly dense but no wasted 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 convey return content, and it does name the three things it returns (who receives details, consent wording, confirmation method). Adequate for a zero-parameter read tool, though an agent cannot tell exactly how that content is structured.

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 and 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?

States a clear verb and resource: it describes the enquiry flow, specifically what submit_enquiry does and what the person receives (details recipients, consent wording, confirmation path). It is distinguishable from a generic read tool, but it never contrasts itself with the sibling enquiry_fields, which likely also describes enquiry data, so sibling differentiation is missing.

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 opening 'Read first.' cues that this should be consulted before submit_enquiry, which is useful implied sequencing, but there is no explicit when-to-use/when-not statement and no named alternative tool.

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 the entire return shape (key, label, type, required, help text, options) — significant because there is no output schema. It stops short of stating that the call is read-only, whether auth is required, or whether the field set is stable, which for a zero-parameter introspection tool is a minor gap.

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 return contents, the second front-loads the actionable next step. No filler, no restatement of 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 input parameters and no output schema, the description fully compensates by enumerating the returned payload and explaining how it feeds submit_enquiry. Nothing an agent needs in order to call this and act on the result 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?

The tool takes zero parameters, so the baseline is 4. The description's only mention of 'field key' refers to the output entries consumed by submit_enquiry, not an input, so there is no parameter semantics to clarify.

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

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

Names a specific resource (the fields of the Miniature Paints Compare enquiry) and enumerates exactly what each entry contains: key, label, type, required flag, help text, and allowed options. It is clearly distinguishable from enquiry_describe (which describes the enquiry) and submit_enquiry (which consumes the keys returned here).

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 sentence 'Pass answers to submit_enquiry keyed by field key' routes the agent to the follow-up tool and tells it how to use the output. It does not explicitly state the antecedent condition (call this before submitting) nor contrast with enquiry_describe, so the when-to-use is implied rather than spelled out.

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 validate-then-submit mutation flow, the returned artifacts (summary, consent line, token), the double opt-in email-link requirement before any provider sees the enquiry, and the exact consent meaning. This is unusually rich behavioral disclosure for a mutation.

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 scope disclaimer, then clearly segmented Step 1 / Step 2 with no wasted framing. It loses a point only because the consent string is repeated verbatim from the schema and the sentences are dense.

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, yet the description compensates by naming what each step returns and the downstream email/click flow. With a nested answers object and a token-gated second call, all the sequencing an agent needs is present.

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 100%, so the baseline is 3, and the description adds real semantics beyond it: 'answers keyed by field key from enquiry_fields' links to the sibling field-definition tool, and the confirmation token is tied to step-1 output obtained 'after the person has approved the summary'. It stops short of enumerating field types, which the schema handles.

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 Miniature Paints Compare') and immediately disambiguates scope with 'NOT a purchase, NOT a guaranteed quote', which separates it from the dataset_* siblings and from any quote/purchase tool. An agent can identify the tool's purpose and boundaries without opening the schema.

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

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

Gives an explicit two-step protocol: Step 1 with answers+consent to validate, Step 2 only 'if the person agrees' with the token. It names the conditions that select each call and what must be shown to the user between them, leaving no routing inference.

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