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

With no annotations, the description carries the behavioral disclosure burden. It states what the tool returns (columns, numeric flags, row count, provenance) and implies a read-only metadata operation, but it does not explicitly confirm side-effect freedom or access/auth requirements. Adequate but not rich.

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 the key content front-loaded: what the tool returns and the explicit instruction to call it first. No filler 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 zero-parameter schema-introspection call, the description covers the important context: usage order, output contents, and dataset scope. It lacks explicit guidance on when not to use this tool relative to siblings likedataset_provenance or dataset_stats, but the low complexity makes the definition largely 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 input schema has no parametershare, and schema description coverage is 100%. The description adds meaning around what the no-arg call returnso that the agent knows why to call it. With zero parameters, the baseline of 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 identifies a specific operation: retrieving the dataset's schema information (columns, numeric flags, row count, provenance banner). It reads a bit like a noun phrase rather than a verb-first instruction, but 'Call this first to learn the schema' clearly frames the purposetar. It is distinguishable from siblings like dataset_row or dataset_search, though less explicitly from dataset_provenance.

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 explicitly says 'Call this first to learn the schema,' which gives clear usage ordering/context. It does not explicitly name alternatives or when to prefer siblings like dataset_provenance, but the first-call guidance is strong enough for an agent to select it for schema discovery.

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 full burden. It discloses valuable behavior: matching is 'any of' (OR semantics) and rows preserve the order of the given values. It does not explicitly state that the operation is read-only, nor mention limits or error behavior, but for a simple selection tool the stated semantics are sufficient.

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, dense sentence that states the operation, the target dataset, the filter semantics, the ordering rule, and the intended use case. No filler 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?

The description covers the core matching and ordering behavior, which is enough for a straightforward filter tool. It does not mention pagination, limits on the number of values, whether the match is case-sensitive, or how rows beyond the selected values are handled. Given the sparse schema and no output schema, those details would materially help an agent call 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 0%, so the description is the only source of parameter meaning. It defines column as a dataset column and values as the values to match, and the phrase 'in the order given' clarifies the ordering semantics of `values`. That adds real value beyond the bare schema.

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

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

The description names a specific dataset ('Sell My Business Quotes'), a concrete operation (select rows where the column is any of the given values), and a distinctive behavior (preserving the order of the given values). It also signals the comparison use case ('X vs Y' questions), which helps separate it from sibling tools like dataset_search or dataset_stats, though it never explicitly names a verb like 'return' or 'fetch'.

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 provides one clear usage context ('for X vs Y questions') which tells when to reach for this tool. However, it does not contrast with obvious siblings such as dataset_row or dataset_search, nor does it state when not to use it. The guidance is present but minimal.

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 communicates that this is a read-only lookup ('Read this') and enumerates exactly what information will be returned: source, computed date, licence, and citation. Some output formatting details are omitted, but there are no hidden effects or destructive behaviors to disclose.

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 plus title conveys the resource, the specific metadata fields, and the intended use. Every part contributes; there is no repetition or filler.

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

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

For a zero-parameter read-only provenance tool with no output schema, the description is sufficient: it names the dataset, lists the returned metadata dimensions, and states the intended use case. No additional structured fields need clarification.

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 parametersags, so the baseline is 4; the description does not need to add parameter meaning. It instead clarifies what the tool returns, which is appropriate for a no-input provenance endpoint.

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 ('Read'), a clear resource (dataset provenance), and the exact content returned: source, computed date, licence, and citation. The title reinforces this by framing it as where data comes from and how to cite it, making the tool's role unambiguous relative to sibling dataset tools.

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 explicitly tells the agent when to use the tool: 'Read this to attribute a figure correctly.' It does not enumerate exclusions or contrast with sibling tools, but the intended use case is clear enough for a zero-parameter provenance lookup.

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 behavioral disclosure burden. It does disclose the exact-match and case-insensitive matching behavior, which is meaningful, but it says nothing about no-match behavior, multiple matches, read-only nature, or return shape.

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 immediately states the matching semantics, with no filler. The title reinforces the purpose and the description is appropriately 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?

The tool is simple, but with no output schema and no annotations, the description should say more about what the agent can expect (e.g., result rows, no-match behavior) and when to prefer sibling tools. The core call semantics are present, but completeness is only average.

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 clarify the parameters. It does so by explaining that 'column' is matched exactly against 'value', but it adds no detail about valid column names, value formatting, or edge cases. This is adequate but minimal compensation for the 0% schema coverage.

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 title 'Look a row up by an exact key' plus the description's 'where a column equals a value exactly (case-insensitive)' names a specific operation (exact lookup), a specific resource (Sell My Business Quotes dataset), and a clear scope. This distinguishes it from sibling dataset_search, which implies matching beyond exact equality.

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 'equals a value exactly (case-insensitive)' establishes a clear context: use this tool when an exact, case-insensitive column match is needed. It does not explicitly name alternatives or exclusion conditions, hence not a 5, but the intent is evident.

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 burden of behavioral disclosure. It discloses case-insensitive matching, the 50-row cap, and that it searches any cell. It does not mention pagination, ordering, or what happens when more than 50 rows match, which would be useful for a search 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?

The description is a single sentence that front-loads the dataset name and the core search behavior, then adds the case-insensitivity and limit. Every word earns its place; no filler or repetition.

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 with two parameters and no output schema, the description is mostly adequate. However, it lacks details about result ordering, pagination, and behavior when the limit is exceeded. Given the absence of annotations and output schema, a bit more context would make it fully 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?

Schema description coverage is 50%: the 'query' parameter is described in the schema, but 'limit' is not. The description adds the 'up to 50' cap, which partially explains the limit parameter, but it does not clarify that limit is optional or how it interacts with the cap. The description adds some value but does not fully compensate for the missing limit description.

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 ('search'), a resource ('Sell My Business Quotes dataset'), and a clear behavior: return rows whose cells contain the query, case-insensitive, up to 50. It is clear and distinct from siblings like dataset_row or dataset_stats, though it does not explicitly name a sibling 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 description implies usage: use this tool to find rows by cell content. It does not explicitly state when to prefer dataset_row, dataset_top, or dataset_columns, nor does it mention exclusions. The 'up to 50' limit is a useful constraint, but no alternative routing 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?

The description discloses important behavioral details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. Since no annotations are provided, the description carries the full burden, and it does so well by explaining data cleaning 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 a single, well-structured sentence that front-loads the statistics list and then adds important caveats about data handling. 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 one-parameter tool with no output schema, the description covers the operation, data cleaning behavior, and the dataset context. It could mention the return format or error cases, but the description is largely complete for an agent to select and invoke 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 description coverage is 0%, so the description must compensate. It explains the column parameter is a numeric column of the dataset, but does not specify exact column name format or how to discover valid columns. However, with only one parameter and a clear description, the meaning is mostly conveyed.

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of a specific dataset. It distinguishes itself from siblings like dataset_row, dataset_search, and dataset_top by focusing on aggregate statistics rather than raw data retrieval.

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 when to use this tool: when aggregate numeric summaries are needed, as opposed to row-level or search operations. It does not explicitly name alternatives or exclusions, but the context of sibling tools and the clear statistical scope provide adequate 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 provided, the description must disclose behavior such as that it only works with numeric columns and that the default order is descending (highest first). The description does not mention these limitations or default behaviors, nor does it indicate what the output format is, leaving the agent to infer 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 one sentence and directly states the purpose, including the key use case. It is front-loaded with the action and scope, and every word contributes to understanding, with no wasted 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 the simple functionality (ranking rows), the description is somewhat adequate but lacks details on numeric column requirements, default ordering, and output format. The absence of an output schema means the description should have clarified return values, but it does not. The complexity is moderate, so a 3 is appropriate.

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 provides minimal coverage (33%) as only the 'ascending' parameter has a description. The tool description does not elaborate on the 'column' or 'limit' parameters beyond what the schema shows, but the description's mention of 'highest (or lowest) rows' partially clarifies the 'ascending' parameter. The description adds some context but does not fully compensate for the low schema coverage, especially for the required 'column' parameter.

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 it ranks rows by a numeric column, and the phrase 'highest (or lowest) rows' indicates the core functionality. It distinguishes itself from siblings by focusing on ranking/ordering rather than searching or retrieving individual rows, but it does not explicitly name a sibling to distinguish from.

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 answering 'which is the most/least X' questions, which provides context. However, it does not explicitly state when to use this tool versus alternatives like dataset_search or dataset_stats, nor does it mention any prerequisites such as the column being numeric or the need to check available columns via dataset_columns.

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 behavioral burden. It discloses that it is read-only, free, that nothing is bought/ordered/paid, and that it returns details about consent and confirmation flow. This is informative for an agent choosing whether to invoke it, though it could mention whether it triggers side effects. The description clearly implies a documentation/informational interaction and covers the essential expectations.

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 very concise, front-loaded with 'Read first', and packs important facts into a few sentences. It avoids fluff and directly explains purpose, limitations, and returned information. 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?

Given the tool's simplicity (zero parameters, no output schema), the description is quite complete. It explains what the tool does, what it returns, and hints at the user journey. It could have stated explicitly that it is a documentation/summary tool rather than an action, but the title and text make that clear. Slight gap: it doesn't mention how the output is presented or whether it includes the exact submit_enquiry description.

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?

There are no parameters in the schemachers, so parameter semantics are trivial; with zero parameters, there is nothing to document. The description references the relevant context (what the enquiry does), which is enough. Even though the schema is empty ecc100%, the description adds value by explaining the nature of the tool 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?

The description states a specific purpose: it is the read-first companion that explains what submit_enquiry does. It uses concrete language ('states what submit_enquiry does', 'nothing is paid', 'no quote is guaranteed') and differentiates itself from the actual submission sibling by being informational, not transactional.

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 imperative 'Read first' provides clear guidance to consult this tool before acting on submit_enquiry dropped. It doesn't explicitly list alternatives, but the subject matter is uniquely tied to submit_enquiry, and the title reinforces the distinction. However, there is no explicit 'when not to use' statement or comparison to other siblings like enquiry_fields.

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 is the only source of behavioral information. It implies a read-only introspection query and describes the field-level information the agent will receive. It doesn't disclose possible edge cases such as response shape, absence of data, authentication expectations, or side-effect-free guarantees, though the metadata nature of the tool makes those less critical.

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 contain the necessary information: what the tool returns and how that output connects to submit_enquiry. The description is front-loaded and contains no filler.

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

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

For a zero-parameter tool with no output schema, the description explains the returned field vocabulary and gives a downstream usage pattern. It stops short of specifying the exact response structure, but it is likely sufficient for an agent to invoke the tool and interpret its 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?

The tool has zero parameters and the input schema already covers 100% of the argument surface, so no parameter-level explanation is required. The phrase 'Every field' further signals that the call is unfiltered and returns the complete set of enquiry fields, satisfying the baseline for a no-argument 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 clearly identifies the resource ('Sell My Business Quotes enquiry') and states the exact content of the tool: every field with key, label, type, required flag, help text, and allowed options. It makes a 4 rather than a 5 because it doesn't explicitly distinguish itself from sibling tools such as enquiry_describe or dataset_columns; only its resource-specific naming makes the distinction clear.

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 routes usage toward submit_enquiry ('Pass answers to submit_enquiry keyed by field key'), giving the agent a concrete downstream step. It does not, however, say when to prefer this tool over sibling tools or when not to use it, so the usage guidance remains implied rather than explicit.

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 burden, and it does so thoroughly: it discloses that no purchase occurs, that validation returns a summary and token, that submission only happens on the second call, and that providers see nothing until the person clicks an emailed link. The exact consent wording is included, leaving no ambiguity about the human consent requirement.

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 detailed but every sentence earns its place, covering purpose, step 1, step 2, and the exact consent line. The two-step structure is front-loaded and reinforced by the title, making the flow easy to follow without excessive padding.

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 the two-step interaction, nested answers object, and no output schema, the description is complete: it explains return values, the confirmation token, the email link behavior, and the consent text. An agent has everything it needs to invoke the tool correctly and to coordinate with the human user.

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?

Although the schema already has 100% coverage, the description adds crucial workflow meaning: answers are keyed by enquiry_fields keys, consent must be true only after the person agrees to the exact quoted statement, and confirmation is the token returned from step 1. This turns bare parameter names into an actionable interaction protocol.

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 submits an enquiry to Sell My Business Quotes, explicitly distinguishing it from a purchase and from a guaranteed quote. The two-step process is named in the title and elaborated in the description, making the tool's purpose unmistakable relative to siblings like 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?

The description gives an explicit, step-by-step protocol: call first with answers and consent=true, show the summary and consent line, then call again with the same data plus the confirmation token only if the person agrees. It also tells the agent to key answers by field keys from enquiry_fields, effectively naming the prerequisite sibling tool.

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