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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, and it does disclose the payload shape and the ordering directive. It does not explicitly say the call is side-effect free, that it takes no arguments, or how the provenance banner is formatted. For a zero-parameter read tool the risk is low, so gaps are minor but real.

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

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

Two sentences, no filler, with the return contents listed first and the usage directive second. The opening noun phrase is dense, but every clause earns its place and nothing is repeated.

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 convey the return values, and it does so by naming all four pieces of returned content. It omits structural details such as whether columns come as a list of names or objects with types, leaving a modest gap for a schema-discovery 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?

The schema has zero parameters, so there is nothing for the description to document; baseline 4 applies. The description correctly implies an argument-free call by not mentioning 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?

It enumerates exactly what the tool returns (columns, numeric flags, row count, provenance banner) for a named dataset, which lets an agent distinguish it from dataset_stats or dataset_provenance. However, there is no explicit verb framing the operation as an introspection/read, and the provenance-banner content partially overlaps the dataset_provenance sibling.

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 directive relative to the sibling tools, which is exactly the context an agent needs. It stops short of stating when not to use it or what to call after, but the intended entry-point role is unambiguous.

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, yet it only mentions one behavior: result ordering follows the input order. It says nothing about permissions, whether the read is safe, the maximum result set, or the shape of the returned records.

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 with the resource front-loaded, and no filler. The em-dash qualifier is slightly awkward and the sentence is a touch convoluted, 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% schema coverage, the description would need to do heavy lifting. It adequately conveys the filtering/ordering semantics and implies the return set, but leaves permissions, result shape, and the values-count constraint unaddressed.

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

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

Schema description coverage is 0%, so the description must compensate. It does clarify the semantics of both parameters ('column is any of the given values', 'in the order given'), which is meaningful. However, it omits the 2-to-10 item constraint on 'values' and gives no format examples for the column name.

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

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

States a specific operation (returns rows of the Structured Settlement Compare dataset) and a specific filter (rows whose column is any of the given values), so the core resource and behavior are clear. It does not, however, explicitly differentiate itself from siblings like dataset_search or dataset_row, which 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?

The closing phrase 'for "X vs Y" questions' gives an implied usage context, which is genuinely helpful routing information. But it offers no explicit when-to-use/when-not guidance and names no alternative tool (e.g., dataset_search) for other query shapes.

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, but the tool is a zero-parameter metadata lookup whose behavior is inherently low-risk. It discloses the content of the response but says nothing about whether the result is stable, cached, or authoritative for a specific release, leaving a modest 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, no filler, with the returned field list front-loaded ahead of the usage instruction. Every clause carries information an agent needs.

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 effectively doubles as return-value documentation by enumerating source, date, licence, and citation. It is nearly complete for such a simple tool, though it does not describe the shape or format of those fields.

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 no parameters, so there is nothing for the description to disambiguate; baseline is 4 per the zero-parameter rule. No parameter-level detail is needed or missing.

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 and enumerates what it returns (source, computation date, licence, citation) for a named dataset, which is far more specific than a restatement of the title. It does not explicitly contrast itself with siblings like dataset_stats or dataset_columns, but the metadata-vs-data distinction is obvious from the field list.

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 clear triggering condition for when to call the tool. It stops short of naming alternatives or stating when this tool is unnecessary (e.g. for raw values), so it is clear context without exclusions.

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 case-insensitive and that multiple rows ('The rows') may be returned, which are non-obvious traits. However it omits what happens on no match, error behavior, and whether an exact column name is required.

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 filler, and the exact-match/case-insensitive constraint is front-loaded. It reads as a noun phrase rather than an imperative, 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?

For a two-parameter lookup with no annotations and no output schema, the description covers the core purpose and matching semantics but leaves return shape, no-match behavior, and column-name expectations unstated. Adequate but with clear gaps.

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 both parameters are undocumented in the structured fields. The description loosely maps them by saying 'a column equals a value,' conveying that one input is a column and the other a value with case-insensitive equality, but it gives no format guidance (e.g. valid column names).

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 outcome: returning rows of the 'Structured Settlement Compare' dataset matching an exact, case-insensitive column/value condition. This is clear enough that an agent knows it's an exact-match lookup, but it never names the sibling it contrasts with (e.g. dataset_search for non-exact queries), so differentiation is left implicit.

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

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

There is no explicit when-to-use or when-not-to-use statement. The word 'exactly' hints that this is the precise-match counterpart to dataset_search, but the agent must infer that routing decision rather than being told it.

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 does disclose case-insensitive substring matching and a 50-row cap, but says nothing about result ordering/relevance, whether the search is read-only, or what happens when nothing matches, which matters for a search tool with zero annotation coverage.

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 scope (dataset), match rule (case-insensitive cell containment), and bound (up to 50) are all delivered compactly.

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-only search with no annotations and no output schema, the description covers what is returned at a coarse level (rows) but omits ordering, result shape, and empty-result behavior. Adequate but with visible gaps.

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% (limit is undocumented in the schema). The description adds useful meaning for query by clarifying matching is case-insensitive and applies to any cell, and effectively restates the 50-row limit bound, but it does not explain limit's role versus the stated cap. Baseline 3 is fair.

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 on a concrete resource: returning rows of the named 'Structured Settlement Compare' dataset whose cells match the query. It is distinguishable from siblings like dataset_stats or dataset_columns, though it never explicitly contrasts itself with the close neighbors dataset_row and 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?

There is no guidance on when to choose this over dataset_top, dataset_row, or dataset_compare, and no prerequisites or exclusions are stated. The agent must infer that this is the free-text search tool purely from the name.

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 non-obvious data handling: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. However, it says nothing about what happens if the requested column is non-numeric (error vs. empty result), permission requirements, or whether the excluded-row count is surfaced in the result.

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 returned metrics, followed immediately by the data-handling caveats in parentheses. Every clause carries information and nothing is redundant, though the parenthetical makes it a dense single 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?

With no output schema, the description usefully enumerates the returned statistics, and with no annotations it documents the parsing and row-exclusion behavior. The remaining gap is discoverability of valid column values and error behavior for non-numeric input, which are minor for a one-parameter read 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?

The single 'column' parameter has 0% schema description coverage, so the description must compensate. Saying 'a numeric column' usefully constrains the parameter to numeric fields, but it does not clarify whether the value is a column name or index, casing rules, or where a valid column list can be obtained (e.g., from 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?

The description states a specific verb (compute) and resource (summary statistics) and enumerates the exact outputs: count, min, max, mean, median, sum for a numeric column of a named dataset. An agent can tell what it does at a glance. It does not explicitly contrast itself with siblings like dataset_columns or dataset_top, 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?

Usage is only implied: it applies to a numeric column of the Structured Settlement Compare dataset, which implicitly rules out non-numeric columns. No sibling is named as an alternative and there is no explicit when-to-use or when-not-to-use statement. This is minimum-viable implicit guidance rather than real routing help.

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 implies a read-only ranking query but never states that it is non-destructive, that only a subset of rows (limit ≤ 50) is returned, or what the default ordering/limit behavior is — the defaults are only partly recoverable from the schema.

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 wasted preamble; the core action (highest/lowest rows) comes first. The quoted em-dash phrase is slightly informal but does real work by stating the user question being answered.

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 should do more: it doesn't say what columns come back, how ties are handled, or what the default limit is. It is adequate for a simple ranking tool but leaves real gaps for an agent to guess at.

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 33% (only 'ascending' is documented). The description compensates slightly by constraining 'column' to a numeric column, but says nothing about the 'limit' parameter's cap or the default direction/limit, so the undocumented parameters remain under-explained.

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 concrete operation: returning the highest/lowest rows of a named dataset ranked by a numeric column. The 'which is the most/least X' framing makes the intent immediately clear and is distinguishable from siblings like dataset_stats or dataset_search, though it doesn't explicitly name its alternatives.

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 analytical question this tool answers, giving an agent a usable context. However, it offers no explicit when-not guidance and never points to the sibling tools (dataset_search, dataset_stats) that might be preferable.

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 disclose important traits: the enquiry is free, nothing is bought/ordered/paid, no quote is guaranteed, and consent/recipient details are included. It could go further on this tool's own read-only nature or auth needs, but it provides substantial behavioral context.

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?

Three short sentences with a front-loaded directive ('Read first') and no wasted wording. The no-purchase/no-guarantee clarifications are compact and earn their 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?

There is no output schema, so the description must explain what comes back, and it does list recipients, consent wording, and confirmation. It does not specify the return format, but for a simple informational tool this is close to 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?

The tool has zero parameters, so the baseline is 4. The empty schema is consistent with the description, which focuses on returned informational content rather than 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 states a specific informational purpose: it describes what submit_enquiry does and returns who receives details, consent wording, and confirmation. It also distinguishes itself from the sibling submit_enquiry by framing itself as read-first explanatory content rather than the action 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?

The opening phrase 'Read first' clearly signals when to use this tool: before engaging with the enquiry process. It does not explicitly name alternatives beyond submit_enquiry or state when not to use it, so it stops short of the top score.

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 describe the returned field structure, which is valuable, but it never states that this is a side-effect-free read or whether the field set is static. Adequate but with a clear gap on behavioural guarantees.

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: the first front-loads the returned payload, the second gives the follow-on action. No filler, no repetition of the title, and every clause carries information.

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

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

Since there is no output schema, the description must explain the return value, and it enumerates the seven returned attributes well enough for an agent to plan parsing. Missing only the contrast with enquiry_describe and any note on whether the field list can change.

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 nothing for the description to clarify. The mention of the field key as the linking concept between this call and submit_enquiry is a small bonus but does not apply to any argument of this 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 states precisely what the tool returns for the Structured Settlement Compare enquiry: key, label, type, required flag, help text and allowed options. That is a specific resource with an enumerated payload, and naming submit_enquiry as the consumer helps separate it from sibling tools. It does not explicitly contrast with enquiry_describe, so it stops short of full sibling differentiation.

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 implicitly conveys usage by saying answers should be passed to submit_enquiry keyed by field key, which frames this as the discovery step before submission. However, it never states when to use this versus enquiry_describe, nor any preconditions or exclusions. Usage is inferable but not 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 that step 1 validates without submitting, that a confirmation token is returned, that the real submission happens only on step 2, and that the person must click an email link before any provider sees the enquiry. It also states exactly what consent means and warns that no quote is guaranteed.

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 long, but front-loaded with the critical negative framing and structured as Step 1 / Step 2. The final verbatim consent sentence duplicates the schema wording, which is slightly redundant, but the rest of the content earns its length given the two-call protocol.

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 compensates by describing what step 1 returns (summary, consent line, confirmation token) and what step 2 triggers (submission plus a verification email). For a stateful, consent-sensitive, two-call tool this is complete enough to invoke 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: answers are keyed by field key from enquiry_fields, consent must be true only after agreement, and confirmation is the token returned from step 1 after approval. The sequencing rule for confirmation is real workflow semantics beyond the 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?

States a specific verb and resource ('Submits an enquiry to Structured Settlement Compare') and immediately disambiguates with 'NOT a purchase, NOT a guaranteed quote'. The two-step nature is spelled out, and it is clearly distinguishable from the enquiry_fields/enquiry_describe siblings it references.

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 when-to-use instructions: step 1 with answers and consent=true, then step 2 only if the person agrees and with the confirmation token. It names the alternative source for inputs (enquiry_fields) and states the gating condition (person must approve the summary) before the second call.

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