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

No annotations are provided, so the description must carry full behavioral disclosure. It describes the return content and when to call it, but says nothing about read-only safety, permissions, side effects, or rate limits. For a zero-annotation tool, this is a significant 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 with no wasted words. The returned content is front-loaded, and the usage instruction follows immediately. It is appropriately sized for a simple introspection 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?

With no output schema and no annotations, the description does explain the main return fields. It is nearly complete for a low-complexity tool, though it could specify read-only behavior or return format more explicitly.

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 parameters, so there is no parameter semantics to explain. Per the baseline for no parameters, a 4 is appropriate.

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 the exact content returned: columns, numeric flags, row count, and provenance banner. It also distinguishes itself from siblings via 'Call this first to learn the schema,' though the provenance banner somewhat overlaps with dataset_provenance, leaving minor ambiguity.

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 explicit timing guidance: 'Call this first to learn the schema.' This tells the agent when to use it relative to other dataset tools. It lacks explicit when-not or named alternatives, so it falls short of a 5.

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

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

With no annotations and no output schema, the description carries the full disclosure burden. It discloses one real behavioral trait — result ordering follows the order of the supplied values — but says nothing about what a returned row contains, whether both sides of the comparison are guaranteed present, or what happens when a value matches nothing.

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?

It is a single short sentence with no padding, which is good. But it is a sentence fragment with no verb, so the key action is front-loaded only as an implied predicate, costing clarity at no saving in length.

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?

Two required parameters at 0% schema coverage, no annotations, no output schema — the description should be doing far more work. One sentence covering the dataset, the filter, and an ordering hint is not enough for an agent to invoke this confidently over its many row-returning siblings.

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 add real meaning: 'column' is the field to match on and 'values' is an ordered list whose sequence drives output order, which goes beyond the bare schema types. It does not clarify the minItems/maxItems bounds or whether values must be distinct, leaving gaps for a 0%-coverage 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 text names the dataset and the returned entity (rows whose column matches given values), which lets an agent infer this is a filtered row retrieval. However, it is a verbless noun phrase, and it never says what the tool 'compares' despite the name and title 'Compare rows side by side', so the distinctive comparison behavior is only implied by the trailing 'X vs Y' clause.

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 closing clause 'for "X vs Y" questions' gives an implied usage trigger, which is more than nothing. But no alternative is named — an agent cannot tell from this text when to prefer dataset_compare over dataset_row or dataset_search, both of which also return rows.

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 must carry the behavioral burden, and it does disclose the returned content and implies a read-only lookup via 'Read this'. It does not state the return format, whether the source/date values are cached or live, or any access constraints. Adequate but not rich for a metadata call.

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, front-loaded with the returned fields and followed by the action it enables. No filler and no repetition 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?

There is no output schema, so the description usefully enumerates the four values returned (source, date, licence, citation), which compensates well for the missing schema. It is essentially complete for a zero-parameter metadata tool; only the output format is left unstated.

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

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

The tool takes zero parameters, so the baseline is 4; there is nothing to disambiguate. The description also names the fixed dataset scope, which is the only 'input' an agent needs to reason about.

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 exactly what the tool returns — source, computation date, licence, and citation for the named 'Outsourced IT Quotes' dataset — which is a specific resource with concrete payload fields. It is clearly distinct from siblings like dataset_stats or dataset_row by content, though it does not explicitly name an alternative.

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 'Read this to attribute a figure correctly' gives one concrete usage context (attribution/citation), which is better than nothing. However, it names no alternatives and offers no when-not guidance — e.g. whether to call it once per session or whether dataset_columns covers any of the same metadata.

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 that matching is exact and case-insensitive and that multiple rows ('rows') may come back, but says nothing about behavior on no match, whether the column must be an existing column name, string coercion, result size, or permissions. Substantial gaps remain for an un-annotated 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 dataset and operation and wastes no words. The dataset name ('Outsourced IT Quotes') is genuinely informative rather than 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?

The tool has no annotations, no output schema, and two undocumented parameters, so the description is the only source of context — and it is one sentence. It omits return shape, empty-result behavior, and input validity rules that an agent needs to invoke this 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 description coverage is 0%, so the description must compensate. It does implicitly map the two parameters — column is a dataset column, value is the matched literal — and adds the case-insensitive, exact-equality semantics. However it does not say where valid column names come from or that both values are bare strings, so compensation is only partial.

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 concrete retrieval scope: rows of a specific dataset where one column equals a value. That is a clear verb+resource+filter and an agent can picture the operation. It does not, however, distinguish itself from the likely-adjacent sibling 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?

There is no when-to-use guidance and no mention of alternatives such as dataset_search or dataset_columns. The agent is left to infer that an exact match is intended versus a fuzzy/substring search offered by a sibling. No prerequisites or constraints on the column/value inputs are given.

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

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

With no annotations, the description carries the full behavioral burden. It usefully discloses case-insensitive matching, that every cell is searched, and a 50-row cap, but says nothing about result ordering, truncation behavior when there are more matches, or what a returned row looks like.

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, correctly ordered: resource, matching semantics, cap.

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 annotations and no output schema, the description adequately explains matching and result size but leaves the shape and ordering of returned rows unstated, which an agent must guess before using the results.

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 in the schema, while limit has only numeric bounds. The description covers the effective cap ("up to 50") but adds nothing beyond that—no default limit, no ordering effect, no note that limit is optional.

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: rows of a named dataset matching the query, with matching semantics (case-insensitive, any cell) and a result cap. It implicitly distinguishes itself from dataset_row (id lookup) and dataset_columns, but never names or contrasts a 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?

The description says what the tool returns but never states when to reach for it versus dataset_row, dataset_top, or dataset_compare. There are no prerequisites, no exclusions, and no stated conditions that select this tool over its siblings.

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, and it does meaningful work: it discloses that grouping commas and currency symbols are normalized, and that non-numeric rows are both excluded and counted (a real data-loss behavior the caller should know about). It does not state permissions or error behavior for a fully non-numeric column, so it is strong but 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?

A single front-loaded sentence that leads with the returned metrics and then appends the two normalization caveats. Dense but every clause carries information; 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 usefully enumerates the six returned statistics, and it covers data-cleaning behavior. The remaining gap is column-name provenance/validation and failure behavior on a non-numeric column.

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, and it only adds the constraint that the column be numeric and belong to this dataset. It does not explain accepted column naming/format or where valid names come from (likely dataset_columns).

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 computation set (count, min, max, mean, median, sum) over a defined resource (a numeric column of the Outsourced IT Quotes dataset). It is clear and scoped, but it never references a sibling tool the way an ideal routing definition would (e.g. dataset_compare or dataset_top).

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

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

The phrase 'of a numeric column' implicitly tells the agent this tool only applies to numeric columns, and the parenthetical about non-numeric rows hints at the expected data shape. There is, however, no explicit when-to-use, when-not-to-use, or alternative-tool guidance relative to siblings like dataset_top or dataset_compare.

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, and it largely abstains. It does not confirm this is a read-only operation with no side effects, does not describe the return shape, and does not mention the hard 50-row ceiling that materially limits results. The only behavioral cue offered is the implicit directionality of 'highest (or lowest)'.

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

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

A single compact sentence with the operative concept ('highest (or lowest) rows ... by a numeric column') front-loaded, followed by a clarifying example. No filler or repetition, though the em-dash example is slightly redundant with the leading clause.

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 33% parameter coverage, the description leaves too much unstated for a tool an agent must call correctly: what a returned row looks like, how ties are broken, what happens with a non-numeric column, and what limit defaults to are all 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 only 33% — just the 'ascending' flag is documented in the schema. The description usefully adds that the target column must be numeric, which the schema's bare 'string' type does not convey, and clarifies the highest/lowest semantics. It says nothing about 'limit', its default, or the maximum of 50, leaving one parameter entirely undocumented.

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 (return the highest or lowest rows) on a specific resource (the Outsourced IT Quotes dataset) via a specific mechanism (a numeric column), and the quoted example 'which is the most/least X' makes the intent unambiguous. It does not, however, name or contrast itself with siblings like dataset_row or dataset_search, so an agent must infer the distinction.

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 the class of question this tool answers, which is useful framing for selecting it over dataset_search or dataset_stats. But there is no explicit when-to-use/when-not guidance and no mention of an alternative tool for related tasks such as comparing two columns or aggregating statistics.

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

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

With no annotations, the description carries the full behavioral burden. It discloses what the tool returns (who receives details, consent wording, confirmation flow) and asserts the enquiry is free with no purchase or guaranteed quote, but those statements describe the subject matter rather than the tool's own side-effect profile, and it never confirms explicitly that the call itself is read-only and non-mutating.

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 dense but purposeful sentences with the ordering cue 'Read first' front-loaded, then the substance. Minimal waste, though the phrasing is slightly circuitous in places ('States plainly what submit_enquiry does').

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, read-only descriptive tool with no output schema, the description is largely complete: it tells the agent what domain it concerns, what content is returned, and that no purchase occurs. Only explicit confirmation of the tool's own non-mutating nature 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 schema provides no parameter semantics to explain; baseline for a parameterless tool is 4. The description correctly implies no inputs are needed.

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

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

The description states a clear purpose: this tool explains what submit_enquiry does on Outsourced IT Quotes, including what an enquiry is and what it returns. It distinguishes itself from the sibling submit_enquiry by being the explanatory counterpart, though the naming ('enquiry_describe') could be confused with enquiry_fields and the description does not explicitly differentiate the two.

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

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

'Read first' gives an implicit ordering cue suggesting it should be read before submit_enquiry, which is a genuine usage hint. However, it never explicitly states when to use this versus enquiry_fields, nor any exclusion conditions, leaving usage largely to inference.

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 it does disclose the payload contents (key, label, type, required, help text, options). It does not state that this is a read-only/discovery operation, whether results are static per enquiry, or anything about permissions or rate limits.

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

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

Two sentences, both load-bearing: the first defines the returned payload, the second defines the downstream usage. Nothing is repeated from the title, and the resource is front-loaded.

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-param discovery tool with no output schema and no annotations, the description does the essential work by specifying what the return entries contain and how they feed submit_enquiry. A brief note on the overall response shape (e.g., a list of field objects) would make it fully self-sufficient.

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

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

The tool takes zero parameters, so per the rubric the baseline is 4. The description correctly implies a parameterless lookup and adds no misleading parameter guidance.

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 exactly which resource it covers (every field of the Outsourced IT Quotes enquiry) and enumerates what each entry contains: key, label, type, required flag, help text, and allowed options. It also distinguishes itself from the sibling submit_enquiry by naming it as the consumer of these keys, so an agent can place the tool immediately.

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 second sentence establishes the workflow: use this to learn the field keys, then pass answers to submit_enquiry keyed by field key. That's clear context for when to call it, but there is no explicit 'call this before submit_enquiry' instruction or statement of when it is unnecessary.

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 discloses that step 1 validates and returns a summary/consent line/token, that nothing is submitted until step 2, and that providers cannot see the enquiry until the person clicks an emailed link. It does not cover error handling, idempotency, or repeat-submission behavior, leaving minor gaps for a mutation tool.

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

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

Front-loads purpose and the two-step structure before details, which is effective. It is slightly long and repeats the full consent sentence that already appears verbatim in the schema's consent property, which costs a little efficiency.

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 explain returns, and it does: step 1 yields a summary, consent line and confirmation token; step 2 triggers a confirmation email. For a two-step submission tool with a nested answers object, an agent has everything needed to call it 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 genuine meaning by tying the parameters into a flow: answers keyed by field key sourced from enquiry_fields, consent gated on the quoted agreement text, and confirmation being the token returned from step 1.

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 to Outsourced IT Quotes') and immediately disambiguates with 'NOT a purchase, NOT a guaranteed quote.' An agent can distinguish this from the enquiry_describe/enquiry_fields helper siblings without opening any 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=true, Step 2 only if the person agrees, with the confirmation token. The condition selecting each call is spelled out, and enquiry_fields is named as the source of the field keys.

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