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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 return contents in detail (columns, which are numeric, row count, provenance banner). It omits any statement of safety profile, permissions, or that it is a pure read, though the introspective nature makes those largely self-evident.

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 that lead with the payload contents and close with the call-first instruction. Slightly awkward phrasing in 'the provenance banner of the RMMCompare dataset,' but no wasted sentences.

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 and no annotations, so the description must stand in for the return shape, which it does by enumerating the four returned data points. Adequate for a zero-parameter introspection tool, though it could note the output is a single object versus a list.

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 are no argument semantics that could be clarified. The description correctly adds no misleading parameter information.

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 (RMMCompare dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and provenance banner. It positions itself as the schema-discovery entry point, which implicitly separates it from dataset_stats/dataset_search, but it never names a sibling or draws an explicit 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?

'Call this first to learn the schema' gives clear ordering guidance and a concrete reason to invoke it before the other dataset_* tools. It stops short of naming alternative tools or stating when this one is the wrong choice (e.g., when dataset_stats is already 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?

With no annotations, the description bears the full burden, and it does disclose one real behavioral trait beyond the schema: values are matched in the order given. However, it says nothing about the return shape, result-size limits, or that this is a safe read operation, leaving meaningful behavioral 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?

A single front-loaded sentence with no filler, and the core filtering/ordering semantics arrive first. Slightly awkward phrasing ('whose column is any of the given values') is the only mild cost.

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 params are both loosely explained and the operation is identifiable, but with no annotations and no output schema the description should say more about what is returned. The unexplained 'RMMCompare dataset' reference also leaves context the agent cannot resolve from the description alone.

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

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

Schema coverage is 0%, so the description must carry parameter meaning. It clarifies that 'column' is the dataset column to match and that 'values' are matched as a set with order preserved, which adds value beyond the bare schema, but it omits the 2–10 item constraint and whether matching is exact or 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 names a concrete retrieval operation (rows of the RMMCompare dataset filtered by matching a column against given values) and its ordering guarantee, which is more specific than the title 'Compare rows side by side'. It partially differentiates from siblings via the 'X vs Y questions' phrasing, but doesn't explicitly contrast with dataset_row or dataset_search, so an agent must infer the boundary.

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

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

'for "X vs Y" questions' implies the intended scenario, giving a usable but thin usage cue. No alternatives are named and there is no statement of when not to use it versus dataset_row, dataset_search, or dataset_top.

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

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

With no annotations and no output schema, the description carries the full burden for behavioral disclosure, and it does list the fields the response will contain (source, computed date, licence, citation). It implies a static, read-only lookup with no side effects, though it doesn't say so explicitly or describe the response format beyond the field list.

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 with no filler, and the payload description is front-loaded before the usage cue. Every phrase contributes either content or routing information.

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

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

For a zero-parameter, read-only metadata tool with no output schema and no annotations, the description supplies enough for an agent to call it correctly and know what comes back. The only small gap is that it doesn't state the response format (e.g. how the citation is structured) or confirm there are no side effects.

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 baseline there is no parameter semantics to document. The description adds the mild but relevant detail that the returned provenance is scoped specifically to the RMMCompare dataset, which is the closest thing to a parameter-like constraint here.

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 RMMCompare dataset) and enumerates the exact contents returned: source, computed date, licence, and citation. That is far more informative than a tautology, and it is clearly distinct from siblings like dataset_stats or dataset_columns, though it doesn't explicitly contrast itself with any of them.

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

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

'Read this to attribute a figure correctly' implies the usage context (attribution/citation), which is meaningful guidance beyond the literal purpose. However, it names no alternatives and sets no explicit when-not condition, so an agent must infer that this is not the tool for retrieving data values themselves.

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 does add one useful behavioral detail the schema lacks — matching is case-insensitive — but says nothing about return shape (how many rows, ordering, truncation) or whether a missing match errors or returns empty.

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 no padding, and the matching semantics are front-loaded. The only structural weakness is that it is a verbless fragment rather than a statement of what the tool 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 lookup tool with no output schema and no annotations, the agent still does not know the result cardinality (plural 'rows' hints many, but no limit or ordering), the error behavior on no match, or how it differs from the several sibling query tools.

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 two required string params, so the description must compensate. It does clarify the semantics of the column/value pair (exact, case-insensitive equality), which is real added meaning, but it does not say whether `column` must be a physical column name or how invalid names behave.

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

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

Names the concrete resource (rows of the RMMCompare dataset) and the filter operation (column equals value exactly), so the operation is identifiable. However, it is a noun fragment with no verb, and it never distinguishes itself from siblings like dataset_search or dataset_top, which plausibly also return rows.

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

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

No when-to-use guidance at all. The sibling dataset_search is an obvious alternative for non-exact or substring lookups, yet the description never states when exact matching is the right choice versus searching.

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

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

With no annotations, the description carries the full burden. It does disclose real behavioral traits: matching is case-insensitive across any cell, and results are capped at 50 rows. It does not state ordering, behavior on zero matches, or any permission/rate considerations, so coverage 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?

A single front-loaded sentence with no filler; the resource and the result cap are stated in the first pass. Nothing to trim.

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 read tool with no output schema or annotations, the description covers matching semantics and result limits. It omits result ordering and return shape, which an agent would benefit from knowing when choosing between this and dataset_top.

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%: the query parameter already documents 'text to look for in any cell', and limit is only bounded by min/max in the schema. The description usefully restates that the match is case-insensitive and that the cap is 'up to 50', but adds little beyond the schema for the query itself.

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 (RMMCompare dataset rows) plus the matching semantics: any cell, case-insensitive. Strong and unambiguous, though it does not explicitly contrast itself with siblings like dataset_row 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?

Usage is implied by the verb 'search' and the containment semantics, but there is no explicit when-to-use guidance or routing to/away from alternatives such as dataset_row (single row) or dataset_top (ranked rows). An agent can infer intent but must guess at boundaries.

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 behavioral burden. It does disclose useful data-handling behavior (grouping commas and currency parsed, non-numeric rows excluded and counted), but says nothing about permissions, error behavior for a non-numeric or nonexistent column, or whether aggregation is scoped to the whole dataset.

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 efficient sentence that front-loads the returned statistics and parenthetically carries the qualification rules. 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?

For a one-parameter read tool with no output schema, enumerating the exact statistics returned and the row-exclusion behavior makes it nearly callable as-is. Only error/target-scope behavior is missing.

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

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

Schema coverage is 0% with one required parameter, so the description must compensate. It adds one meaningful constraint (the column must be numeric) but gives no hint about column naming conventions or accepted identifiers.

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 action (compute count/min/max/mean/median/sum) on a specific resource (a numeric column of the RMMCompare dataset). The enumerated statistics make the tool's function unambiguous, distinguishing it implicitly from siblings like dataset_compare or dataset_row, though no sibling is named.

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 guidance on when to use this versus dataset_compare, dataset_top, or dataset_columns. Usage must be inferred purely from the fact that it summarizes a 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 behavioral burden, and it discloses very little. It conveys the sort direction semantics but says nothing about the return shape, tie-breaking, default limit, or the cost of scanning the dataset.

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 operation and the motivating question front-loaded; no filler and 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?

For a 3-parameter tool with no annotations and no output schema, the description is only minimally complete: it does not indicate what a returned row looks like or how the default top-N behaves, which the agent must infer from the JSON Schema alone.

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

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

Schema coverage is only 33% (only 'ascending' is documented), so the description must compensate. It partially does so by requiring a 'numeric' column and implying the highest/lowest direction, but the 'limit' parameter and ordering/tie behavior are left entirely to the schema bounds.

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

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

States a specific verb (rank/return highest or lowest rows), the resource (RMMCompare dataset), and the driving input (a numeric column), with the plain-language gloss 'which is the most/least X'. This distinguishes it from aggregate siblings like dataset_stats, though it doesn't name any 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?

Usage is only implied through the quoted question 'which is the most/least X', which suggests a top-N ranking intent. There is no explicit when-to-use/when-not guidance and no mention of alternatives such as dataset_stats or dataset_search 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 burden and does well: it discloses that nothing is bought, ordered or paid, no quote is guaranteed, and the process is free, plus what gets returned (recipients, consent wording, confirmation). It stops short of pacing, auth, or error behavior, but for a read-only informational tool this is solid disclosure.

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 action-oriented instruction 'Read first' is front-loaded, and the three sentences are purpose-, safety-, and return-focused. The second sentence is slightly long but each clause contributes distinct information.

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

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

Given no parameters, no output schema, and no annotations, the description supplies enough context: what it explains, its safety profile, and what content it yields. An agent can call it correctly without further information.

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

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

The tool has zero parameters and 100% schema coverage, so the baseline of 4 applies; there is no parameter meaning to add or omit.

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 establishes this as the explanatory companion to submit_enquiry, stating it 'states plainly what submit_enquiry does.' A specific function is conveyed (describe the enquiry flow) and it is distinguishable from the submit_enquiry sibling, though the framing describes the sibling's behavior more than its own verb+resource.

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

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

'Read first' clearly instructs the agent to call this before the enquiry flow, and the naming of submit_enquiry gives a tacit alternative. However, no explicit when-not conditions or a direct comparison of when to use this versus enquiry_fields is provided.

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

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

No annotations exist, so the description carries the full burden. It discloses the return contents (including the important detail that options are supplied only where they exist), which is genuinely useful given there is no output schema, but it says nothing about side effects, permissions/auth, or whether the field set varies per caller or enquiry state.

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 sentences, front-loaded with what the tool returns and followed by the one actionable follow-up detail. Every clause earns its place, though the first sentence is a packed list that reads more like a spec than prose.

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 tool with no output schema, the description sufficiently describes the returned structure and the hand-off to submit_enquiry. The only real gap is not stating whether all fields are always returned or whether some are conditional on earlier answers.

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 input schema is empty (0 parameters), so there is nothing for the description to disambiguate and the baseline of 4 applies. The description instead documents the output field structure, which is a sensible substitution for a no-arg tool.

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 is concrete about the resource (the RMMCompare enquiry's fields) and enumerates exactly what each field record contains: key, label, type, required flag, help text, and allowed options. It stops short of explicitly distinguishing itself from the sibling enquiry_describe, so an agent must infer the split. Still, the purpose is unambiguous.

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 ties the tool to submit_enquiry by telling the agent to key answers by field key, which implies this is the discovery step preceding submission. However, it never states when to call this versus enquiry_describe, nor any prerequisite ordering. Usage is implied rather than specified.

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 meets it: it discloses validation behavior, the returned artifacts (summary, consent line, confirmation token), the fact that an email with a click-through link is sent, and that no provider sees the enquiry until that click. The precise consent wording is spelled out rather than assumed.

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 critical disambiguation, then cleanly structured as Step 1/Step 2. The quoted consent line appears alongside the schema description's copy of it, a minor redundancy, but the sentence is doing real work since the agent must display it verbatim.

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, and the description compensates fully by enumerating what each step returns. For a two-phase, consent-gated write with nested answer objects, an agent has everything needed to execute 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 real value beyond it: answers are keyed by field key from enquiry_fields (pointing at the sibling that supplies the keys), consent is tied to a specific agreement, and confirmation is defined as the token returned in 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 RMMCompare') and immediately draws the boundary that matters most: 'NOT a purchase, NOT a guaranteed quote.' Among the enquiry_* siblings, only this one performs the actual submission, and the two-step framing makes that unmistakable.

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 routes the caller: Step 1 with answers+consent, then Step 2 only after the person agrees and with the confirmation token. It also describes the human-in-the-loop prerequisite (show summary and consent line, wait for agreement) rather than leaving sequencing to inference.

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