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

No annotations are supplied, so the description carries the burden. It does disclose the returned surface (column list, numeric flags, row count, provenance banner) and its role as the entry point, and the operation is implicitly a read. However, it says nothing about caching, freshness, auth requirements, or whether the dataset is fixed to Strength Standards Calc only, leaving meaningful behavioral gaps.

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 no filler; the returned-field inventory leads and the usage directive follows. The first sentence is a dense list, but every element is load-bearing since no output schema exists to carry it.

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

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

With no output schema and no annotations, the description does the important work of naming the return fields, which is exactly what an agent needs before calling a zero-parameter introspection tool. It is sufficient for invocation, though it could note whether the dataset name is fixed or whether other datasets are queryable.

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 takes zero parameters, so there is nothing to document and the baseline of 4 applies. The description correctly adds no parameter narrative, which matches the empty input 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 gives a concrete verb-less but explicit resource: it enumerates exactly what is returned — columns, which are numeric, row count, and the provenance banner for a named dataset. That is specific enough to distinguish it from dataset_stats or dataset_top, though the overlap with dataset_provenance (also returning a provenance banner) is not addressed.

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' is an explicit sequencing directive that tells the agent when to reach for this tool relative to the other dataset_* tools. It stops short of naming alternatives or stating when not to use it, so it does not reach the top of the scale.

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

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

With no annotations, the description carries the full behavioral burden. It discloses the core filtering and ordering semantics, but omits read-only status (implied by 'rows'), error behavior, output format beyond 'the rows', and any constraints such as the 2–10 value limit.

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 resource and core operation. It is compact with no obvious filler, though the noun-phrase style is slightly less direct than a full verb-led sentence.

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

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

For a simple two-parameter tool with no annotations or output schema, the description covers the essential return selection and ordering. However, it leaves gaps around acceptable column values, array length constraints, and the read-only nature, so an agent still needs to infer details 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 description coverage is 0%, so the description must compensate. It identifies both parameters by role ('column' matches against 'given values') and adds the meaningful ordering rule ('in the order given'), but provides no format or constraint details (e.g., column name syntax, string types, min/max array length) to fully make up for the empty schema descriptions.

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 dataset ('Strength Standards Calc') and a specific row-selection operation: rows whose column matches any of the given values, in the given order. It distinguishes itself from siblings like dataset_search or dataset_top by its 'X vs Y' comparison framing, though it does not name those alternatives directly.

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

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

It gives an implied usage context ('for "X vs Y" questions') but does not explicitly say when to use this tool instead of siblings like dataset_row or dataset_search. No exclusions or prerequisites 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?

No annotations are provided, so the description carries the full disclosure burden. It does tell the agent what it will receive back (source, date, licence, citation), which is the main behavioral trait of a zero-parameter read tool, but it says nothing about the response shape or format, and there is no output schema to fill that 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 short sentences, the content list front-loaded and the usage cue last. 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?

For a parameterless metadata lookup with no annotations and no output schema, the description covers what the tool is and what it returns, which is essentially the whole contract. Only the return format/pagination-style details are left unstated, and those are minor here.

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 this defaults to a baseline of 4. There is nothing for the description to clarify beyond the empty schema.

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

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

The description names the exact resource (provenance of the Strength Standards Calc dataset) and enumerates the fields returned: source, computation date, licence, citation. It is a noun phrase rather than a verb-led statement, but the scope is unambiguous and cannot be confused with siblings such as dataset_columns or dataset_stats.

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 trigger for calling the tool. It does not name alternatives or state when not to use it, but for a narrowly scoped metadata tool with obvious siblings, the context is sufficient.

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 full burden. It usefully discloses two behavioral traits beyond the schema: matching is case-insensitive and the result is potentially plural ('the rows'). It says nothing about behavior on zero matches, duplicates, return shape, or limits, which for an unannotated tool leaves a notable 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?

A single sentence with no filler, and the dataset scope and matching mode are front-loaded. It is grammatically a fragment rather than a complete statement, but nothing is wasted.

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

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

With no annotations, no output schema, and 0% parameter coverage, this thin one-line description leaves too much unspecified for a lookup tool: return format, empty-result behavior, and how to obtain valid column names are all unaddressed. An agent would need to consult sibling tools to call it reliably.

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 both parameters, so the description must compensate and largely does not. It conveys only the tautological idea that 'column' names a column and 'value' is the thing compared; it never clarifies whether 'column' must be a valid name (e.g. from dataset_columns), whether value strings cover numeric columns, or any formatting constraints.

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 (rows of the Strength Standards Calc dataset) and the precise matching semantics (column equals value exactly, case-insensitive), so an agent can tell it apart from fuzzy siblings like dataset_search. It is a noun phrase rather than a verb+resource sentence and never names the alternative it contrasts with, keeping 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?

Usage is only implied: the 'equals a value exactly (case-insensitive)' phrasing signals an exact-match lookup as opposed to a broader search, which hints at when to prefer this over dataset_search. However, no alternative tool is named and no conditions or prerequisites are stated, leaving the agent to infer the 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 behavioral burden; it usefully discloses case-insensitive matching and a 50-row cap. However it omits result ordering/ranking, the default when limit is not supplied, and any permission or error behavior, so the disclosure is partial.

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 tight sentence with the resource, scope, and two key behavioral facts front-loaded; 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?

No output schema or annotations exist, so the description must stand alone; it explains what rows come back but not their field shape or ordering, and there is no mention of the default limit. Adequate for a simple search tool but leaves gaps an agent would want.

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 50% (query documented, limit not). The description compensates partially by clarifying that matching is case-insensitive (query) and that results cap at 50 (limit), but says nothing about the default limit value or the ordering of returned rows.

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

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

States a specific verb (search) and resource (rows of the Strength Standards Calc dataset), and defines the matching rule (cells containing the query, case-insensitive). It is clearly a text-search over rows, distinct from dataset_row or dataset_stats, though it never names a sibling to sharpen the contrast.

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 only implied by the purpose: an agent can infer this is the tool for keyword lookup, but there is no explicit when-to-use, when-not, or pointer to alternatives such as dataset_top or dataset_columns for other access patterns.

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 useful preprocessing semantics: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It stops short of stating error behavior when the supplied column is non-numeric, or the result shape/pagination, so it is helpful but incomplete.

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 computed statistics, with the preprocessing caveats tucked into a parenthetical. No wasted words and nothing important buried.

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

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

There is no output schema, so the description correctly enumerates the return values and explains how rows are filtered. What remains unstated (error handling for a non-numeric column, dataset scope rigidity) is minor for a single-parameter read-only statistics tool.

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

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

Schema description coverage is 0% for the single 'column' parameter, so the description must compensate. It adds the meaningful constraint that the column must be numeric and belongs to the Strength Standards Calc dataset, but gives no guidance on column-name format or matching (e.g. exact vs case-insensitive).

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 statistics returned (count, min, max, mean, median, sum) and scopes them to a numeric column of a specific named dataset, so an agent knows precisely what the tool produces. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, which keeps it 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?

There is no statement of when to choose this tool over dataset_top, dataset_compare, or dataset_columns. Usage is only implied by the tool's return values; the description never says when it is or is not appropriate.

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 says nothing about return format, default limit or the 50-row cap, read-only safety, ordering ties, or whether the dataset is fixed. For a tool with zero structured coverage of its behavior, this is a substantial 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?

A single compact sentence, front-loaded with the core action and scope. It largely restates the title's "rank rows by a numeric column" rather than adding much, but there is no wasted wording.

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, no annotations, and 3 parameters at 33% coverage, the description would need to explain the return shape, default result count, and limits. It covers neither the limit behavior nor what the returned rows look like, leaving the agent under-informed for 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 description coverage is only 33% (only 'ascending' is documented). The description adds one useful constraint — the column must be numeric — and conveys highest-vs-lowest via "(or lowest)", but 'limit' is undocumented in both the schema and the description, including its default and maximum of 50. It does not compensate for the coverage gap.

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: returning the highest/lowest rows of the Strength Standards Calc dataset ranked by a numeric column. The "which is the most/least X" framing makes the intent immediately clear and lets an agent distinguish it from dataset_stats or dataset_search, 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?

The "which is the most/least X" phrasing implies the use case (extremal ranking queries) but gives no explicit when-to-use vs alternatives, no condition under which dataset_stats or dataset_search would be preferred, and no prerequisites. Usage is inferable rather than stated.

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

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

With no annotations, the description carries the full burden and does well: it explicitly negates purchase, order, payment, and guaranteed quotes, and flags that it is free. It also previews the return contents (recipients, consent wording, confirmation method), though it never explicitly says the tool has no side effects of its own.

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,' then two tight sentences. Dense but every clause earns its place; nothing redundant.

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 signal what comes back, and it does list the return contents. For a zero-parameter explanatory tool this is nearly complete, missing only explicit side-effect/read-only language.

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. There is nothing for the description to add beyond confirming it is a pure documentation read.

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 ('States plainly what submit_enquiry does') and the topic (the ENQUIRY flow with human providers). It distinguishes itself well from enquiry_fields and submit_enquiry by being the explanatory/read-first companion, though it spends more words describing the sibling than 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' implies ordering relative to submit_enquiry, which is useful guidance. However it does not state when to use this vs enquiry_fields, the other sibling in the same enquiry family, so the routing 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?

No annotations are provided, so the description carries the burden, and it does disclose the full shape of what comes back (keys, labels, types, requiredness, help text, allowed options). It omits whether output is paginated or ordered, and any auth/rate considerations, but for a read-only metadata lookup that 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, zero waste. The resource and returned fields come first, and the follow-on instruction for submit_enquiry is placed last, which is the right front-loading.

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 define the return shape, and it does so field by field. It also closes the loop by explaining how the result feeds submit_enquiry, so an agent has everything needed to call it and use the result.

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 appropriately adds no parameter detail, instead explaining that the returned values are keyed by field key for use in submit_enquiry.

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

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

States a specific resource (every field of the Strength Standards Calc enquiry) and enumerates exactly what it returns: key, label, type, required flag, help text and allowed options. It is clearly distinguishable from siblings like enquiry_describe and submit_enquiry, which do different jobs.

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 implies usage by telling the agent to pass answers to submit_enquiry keyed by field key, which hints that this is a pre-submission discovery step. However, it never explicitly says when to call this rather than the sibling enquiry_describe, and there are no stated exclusions or prerequisites.

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

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

With no annotations, the description carries the full burden and discharges it: validation behavior, what is returned at step 1 (summary, consent line, confirmation token), the two-call gating requirement, and the email-confirmation-before-provider-visibility behavior. It also spells out the exact consent text, which an agent must relay accurately.

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 'not a purchase' caveat and the two-step structure are front-loaded, and each sentence carries procedural weight. It is dense for a single paragraph, but the length is justified by the mandated two-call flow and verbatim consent language.

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, yet the description explains the step-1 return values and the step-2 side effect; with no annotations, it also covers the behavioral and consent requirements. An agent has everything needed to run both calls 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 adds cross-tool meaning beyond the schema: 'answers' are keyed by field key drawn from enquiry_fields, and 'confirmation' is tied to the step-1 return, explaining its role in the workflow rather than just its 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?

States a specific verb and resource ('submits an enquiry') and immediately negates the likely misreadings ('NOT a purchase, NOT a guaranteed quote'). It is clearly distinguished from siblings like enquiry_fields and enquiry_describe, which are the read/prep tools referenced 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?

Explicitly prescribes when to call each time: step 1 with answers + consent=true, step 2 only after the person approves, with the confirmation token. It also names enquiry_fields as the source of field keys and states the precondition for consent, leaving no sequencing to inference.

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