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

No annotations are provided, so the description carries the burden. It describes what information is returned but does not explicitly state that the operation is read-only or has no side effects, though the nature of returning schema metadata strongly implies it.

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 description is brief, direct, and front-loads the key purpose. Every sentence adds value, with no redundant or vague phrasing.

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 description conveys the main output elements (columns, numeric indicators, row count, provenance banner) and the intended first-step usage. It is complete enough for a simple schema-introspection tool, though it does not specify the exact output format.

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 the schema coverage is 100%, so there is nothing missing. The description fully aligns with the empty parameter schema.

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

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

The description clearly states that the tool returns columns, numeric flags, row count, and provenance banner for the Take-Home Compass dataset. It is distinct from sibling tools like dataset_compare or dataset_search by focusing on schema information.

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 advises 'Call this first to learn the schema,' giving clear situational guidance. It does not name alternative tools, but the instruction to call it first is actionable and sufficient for basic usage.

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?

The description states the main behavior: selecting rows whose column value matches any of the provided values, preserving the given order. However, with no annotations, it does not disclose whether the operation is read-only, what the output format is, or how results are paginated or limited. The core behavior is transparent, but edge-case behavior is not covered.

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 description is a single, tightly worded sentence with no redundant information. It effectively conveys the core behavior and the intended use case in a compact form. The structure is clean and easy to parse.

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 description provides enough context for a simple filtering operation and names the target dataset. However, it lacks details about output structure, parameter semantics, and relationship to sibling tools. For a tool with no output schema and no annotations, this leaves some gaps for the caller.

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 description refers to 'column' and 'given values' but does not explicitly map them to the 'column' and 'values' parameters. It implies that 'column' is the dataset column to filter on and 'values' are the accepted values, but it does not explain constraints like minimum two values or the meaning of the order. Since the schema has no parameter descriptions, the description only partially compensates.

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 clearly identifies the tool's purpose: returning rows from the Take-Home Compass dataset where a specified column matches any of the given values, in the provided order. The title 'Compare rows side by side' and the phrase 'for X vs Y questions' add helpful intent context. It could be more explicit about the relationship to sibling tools, but the core 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 description gives minimal guidance on when to use this tool. It mentions 'for X vs Y questions' but does not contrast it with sibling tools like dataset_search, dataset_row, or dataset_top, nor does it explain when this tool should be preferred. Users are left to infer the appropriate usage context from the phrase alone.

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?

The description states exactly what information the tool provides (source, date computed, licence, citation) and implies a read-only, side-effect-free operation. No annotations exist, but nothing in the description contradicts observable behavior.

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 description is brief and well-structured, using two sentences to convey both content and usage. No unnecessary words or redundancy.

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

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

For a parameterless provenance tool, the description is complete: it names the dataset, lists the returned attributes, and explains when to use it. An explicit return schema is absent, but the listed attributes provide enough context for this simple 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 tool has zero parameters and the schema coverage is 100%, so the baseline of 4 applies. Since there are no parameters to describe, the description sufficiently covers the parameter space.

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?

Clearly identifies the resource (Take-Home Compass dataset provenance) and the specific purpose (to attribute a figure correctly). The title and description align well, and the tool is distinguishable from sibling tools focused on columns, search, stats, etc.

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 tells the agent when to use it ('Read this to attribute a figure correctly'), implying use when citation or provenance information is needed. It does not explicitly contrast with sibling tools, but the purpose is specific enough that confusion is unlikely.

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 provided, the description carries the full behavioral disclosure burden. It usefully discloses case-insensitive exact matching, but it does not explain whether multiple rows can be returned, what happens if no row matches, or the output shape, leaving important behavior undocumented.

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 description is a single 18-word sentence that front-loads the resource and the core matching rule. There is no filler, and the title and description complement each other without redundancy.

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

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

For a simple two-parameter lookup tool, the description is reasonably adequate, but with no annotations, no output schema, and no details about return behavior, error handling, or row multiplicity, an agent still lacks some information needed to safely and correctly invoke the tool. It is complete enough to start but not rich.

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%, and the schema only defines 'column' and 'value' as minLength strings. The description adds that these are compared for exact equality, but it does not explain valid column names, expected value formatting, or behavior for empty or invalid values, so it only partially compensates for the missing schema documentation.

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 ('look up'), resource ('rows of the Take-Home Compass dataset'), and the exact matching semantics ('equals a value exactly (case-insensitive)'). This clearly distinguishes it from sibling dataset_search, which implies broader/fuzzy search, making the purpose 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 description implies exact-match lookup is the intended use, but it does not explicitly state when to use this tool versus dataset_search or other siblings. No alternatives or exclusions are mentioned, so the agent must infer usage context from the matching semantics alone.

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?

Annotations are absent, so the description carries the full burden. It implies a read-only search operation with no side effects, and mentions the limit behavior, but it does not explicitly state read-only status, failure modes (e.g., empty results), or output format. This is adequate but not fully transparent.

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 description is a single, concise sentence that communicates the essential behavior without unnecessary detail. It is well-structured and easy to parse.

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

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

For a simple search tool, the description is sufficient for an agent to invoke it correctly. It lacks an explicit output schema description, but that is not required since the tool's purpose and parameters are clearly defined. Omitting details like empty-result handling is acceptable for this complexity level.

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 description adds meaningful context beyond the schema: it clarifies that the query is matched case-insensitively and that the limit parameter caps results at 50. This enhances understanding of both parameters, though it does not specify default limit behavior or edge cases.

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 clearly states the tool's function: searching rows of the Take-Home Compass dataset for a case-insensitive query across cells, returning up to 50 results. It distinguishes from sibling tools like dataset_row (fetch specific row) and dataset_top (top rows), making its purpose 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 description provides clear context on when to use the tool (when you need to find rows containing a text query) and specifies case-insensitive matching and a limit, but it does not explicitly name alternatives or state exclusions. Still, the sibling tool names make the use case clear.

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

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

No annotations are provided, so the description carries the full burden. It discloses two important behavioral details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This adds valuable context beyond the schema. It does not mention error handling or return format, but for a simple stats tool this is reasonable 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?

The description is a single sentence that front-loads the list of statistics, then adds necessary caveats about formatting and non-numeric rows. It is concise, with no redundant words or phrases, and every element earns its place.

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

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

For a one-parameter tool with no output schema, the description covers the main behaviors: which statistics are computed, how formatting is handled, and the treatment of non-numeric rows. The missing explicit return format is a minor gap given the simple nature of the tool.

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

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

Schema description coverage is 0%, so the description must compensate. It clarifies that the 'column' parameter is a column name from the dataset and that it should be numeric. This is essential semantic information that the plain string type in the schema does not provide.

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 clearly states that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Take-Home Compass dataset. It is specific about the resource and the operation, making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like dataset_top or dataset_columns, 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?

The description gives clear context that this is for numeric columns, implying when to use it. But it does not mention any alternatives or exclusions, so an agent might not know when to prefer dataset_search or dataset_top. It lacks explicit when-not-to-use guidance.

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 implies a read-only operation (returning rows) and clarifies the numeric column constraint, but does not explicitly mention side effects, permissions, or error behavior. The description is honest but minimal.

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 description is concise and front-loaded: 'The highest (or lowest) rows...' immediately conveys the core function. The parenthetical and quote add clarity without redundancy. Every word earns its place.

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

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

For a simple tool, the description provides a basic understanding of what it does (ranking rows), but lacks details on output structure (what fields are returned), the effect of 'limit' (e.g., default behavior), and edge cases. The absence of an output schema increases the need for such context, which is not fully supplied.

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 description adds meaning to 'column' (numeric) and 'ascending' (highest/lowest order), but does not clarify the 'limit' parameter at all. Since only one parameter has a schema description, the tool description covers part of the parameter semantics but leaves a gap.

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

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

The description clearly states the tool returns rows ranked by a numeric column, either highest or lowest, and specifies the 'most/least X' query intent. This distinguishes it from sibling tools like dataset_search (searching) and dataset_stats (aggregates).

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 implies usage for ranking queries via 'which is the most/least X' but does not explicitly state when to use this tool versus alternatives like dataset_stats or dataset_search. No explicit guidance on when not to use 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 must carry the behavioral burden. It discloses what the tool returns and clarifies what submit_enquiry does not do, but it does not explicitly state that enquiry_describe itself is read-only or side-effect free. The 'Read first' phrasing implies safety, but the description could be more explicit.

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 description is compact and front-loaded with 'Read first.' All sentences convey useful information, though the title and description repeat the 'not a purchase / no guaranteed quote' idea. Overall it is efficient and scannable.

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

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

For a parameterless documentation tool with no output schema, the description gives enough detail about the returned information and the context of submit_enquiry. It could mention output format or length, but the agent has sufficient information to call and interpret the tool 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?

The tool has zero parameters, so parameter documentation is not needed. The description correctly focuses on what the tool returns rather than input semantics, matching the baseline for a parameterless 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 plainly that this tool explains what submit_enquiry does rather than performing the enquiry itself, and it lists the specific content returned (recipient details, consent wording, confirmation method). It is clearly a documentation/read tool, though it does not explicitly contrast itself with sibling tools like enquiry_fields.

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

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

The opening 'Read first' is an explicit usage directive, telling the agent to consult this tool before acting. It also clarifies the scope of submit_enquiry (not a purchase, no guaranteed quote, free), which helps the agent decide when the broader enquiry flow is appropriate. It does not explicitly name alternatives, but the context is sufficient.

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

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

No annotations are provided, so the description carries the behavioral burden. It explains the return content and that it covers every field, but it does not disclose side effects, authentication requirements, or failure modes. For a metadata-listing tool these are low risk, but the description is still the only source of behavioral information.

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 filler. The first sentence packs the full content specification, and the second sentence gives the cross-reference to submit_enquiry, making every clause purposeful.

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 parameters and no output schema, the description must explain what the tool returns, and it does: key, label, type, required, help text, and allowed options. It also connects to the submission flow. It could say more about the exact value format of options, but it is sufficient for correct invocation.

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

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

The tool takes zero parameters and schema description coverage is 100%, so there are no parameter gaps to fill. The description adds useful semantics by explaining that the returned field keys are the ones to use when calling submit_enquiry.

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

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

The description clearly states that the tool enumerates every field of the Take-Home Compass enquiry and lists what those fields contain (key, label, type, required, help text, options). It also distinguishes itself from submit_enquiry, but lacks an explicit verb like 'returns' or 'lists' and does not differentiate from enquiry_describe.

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

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

It gives an explicit usage hint: pass answers to submit_enquiry keyed by field key, which tells an agent to use this tool to discover the field keys before submitting. It does not discuss when not to use it or compare it directly with enquiry_describe, but the context is actionable.

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

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

No annotations are present, so the description carries full burden. It discloses side effects: no purchase, no guaranteed quote, email sent, provider only sees enquiry after link click, and consent text with no data selling.

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?

Well-structured and front-loaded with purpose and steps, but the consent text is repeated verbatim in both the description and the consent parameter description, adding minor redundancy.

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 is provided, but the description explains both step outcomes: validation returns summary, consent line, and confirmation token; final submission sends email and requires link click before provider visibility.

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?

All parameters are described: answers are keyed by field key from enquiry_fields, consent has exact required agreement text, and confirmation is the token from step 1. Schema coverage is complete and enriched.

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?

Description clearly states the tool submits an enquiry to Take-Home Compass, with explicit two-step behavior and negative scope (NOT a purchase, NOT a guaranteed quote), distinguishing it from enquiry_describe and enquiry_fields.

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?

Provides explicit step-by-step instructions: step 1 validates and returns a summary/token, step 2 requires confirmation token and person approval. It also tells when consent must be true and references enquiry_fields for field keys.

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