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

With no annotations, the description carries the full burden of behavioral disclosure. It is transparent about the output contents (columns, numeric flags, row count, provenance banner) and implies a non-mutating, lightweight informational call. It doesn't explicitly state 'read-only,' but for a zero-parameter schema-introspection tool there are no hidden side effects or severe surprises 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?

Two sentences with no filler. The first sentence front-loads the exact payload and the dataset name; the second delivers a crisp usage directive. Every clause 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 no-argument metadata tool, the description is largely complete: it lists the return contents, identifies the dataset, and gives ordering advice. The main gap is the lack of a return-format description (e.g., JSON shape), but since there is no output schema and the tool name/title already say 'columns and shape,' this is a minor omission rather than a blocking one.

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

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

The input schema is empty (0 parameters), so the baseline is 4. There are no parameters for the description to explain, and it correctly does not attempt to invent any.

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 what the tool returns — columns, numeric indicators, row count, and provenance banner — for a specific dataset. It identifies the resource and implies a read/retrieve action, and the phrase 'learn the schema' sharpens the purpose. However, it does not explicitly distinguish itself from siblings like dataset_stats or dataset_provenance that also surface row counts or provenance info, 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?

The description gives an explicit usage directive: 'Call this first to learn the schema.' This tells the agent when to use it relative to other tools. It does not, however, name alternatives or provide when-not-to-use guidance, so it doesn't reach the full bar of explicit exclusions and alternatives.

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 burden, and it does a good job: it discloses that rows are filtered by 'any of the given values' and that output order follows the supplied value order. It does not mention matching semantics such as exact/case-sensitive matching, but the core behavior is transparent for a straightforward read operation.

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 with no filler, and the most important behavioral facts are front-loaded: dataset, filter by column, any-of values, and order preservation. Every clause 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?

Given the simplicity of the operation, two required parameters, and no output schema, the description covers the essential selection and ordering behavior adequately. It could add edge-case context such as what happens when no rows match, but nothing critical is missing for a basic agent call.

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 0% description coverage, but the description compensates by explaining both parameters in plain language: 'column' is the field to match, and 'values' is the list whose order determines result order. This meaningfully goes beyond the bare JSON Schema, though it could more explicitly name the parameters and define value format constraints.

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 target dataset and the selection mechanism: rows whose column value matches any of the supplied values, in the given order. Combined with the title 'Compare rows side by side', the core purpose is clear enough for an agent to understand the operation. It does not explicitly name a sibling tool it is distinct from, so it doesn't quite reach the top score.

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 'for "X vs Y" questions' gives a clear, concrete use context, telling the agent when this tool is appropriate. It does not explicitly exclude alternatives or name sibling tools such as dataset_row or dataset_search, so it stops short of full routing 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?

No annotations are provided, so the description carries the behavioral burden. It communicates that this is a read-only provenance lookup and outlines what information the agent should expect to receive. It does not mention permissions or edge cases, but the tool's simplicity and zero-parameter interface make that omission minor.

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 direct sentence with no wasted phrasing. It front-loads the output contents and closes with a practical attribution purpose, making it easy for an agent to parse quickly.

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 no-argument provenance retrieval tool, the description sufficiently covers what the agent will receive and why it should be called. There is no output schema, so the description's enumeration of source, date, licence, and citation is enough for correct usage.

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 description does not need to explain parameter behavior. It still adds semantic value by indicating that the returned provenance includes source, computed date, licence, and citation.

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 the exact resource ('Background Check Quotes dataset') and the specific information returned: source, computed date, licence, and citation. This makes it immediately distinguishable from sibling tools like dataset_search, dataset_stats, and dataset_columns.

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 contextual guidance: read this to attribute a figure correctly. It does not explicitly name alternatives or state when not to use it, but for a provenance metadata endpoint the suggested use case is clear enough.

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?

Since annotations are none provided, the description must carry behavioral disclosure. It mentions case-insensitive matching, which is a useful behavioral trait beyond the schema. However, it does not disclose the return format (e.g., number of rows, structure) or any limits (e.g., returns first match only). The description adds some value but lacks depth for a read operation.

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, concise and front-loaded with the main operation. It includes the key detail of case-insensitivity without unnecessary fluff. It could be slightly more structured by breaking out the parameters, but it's efficient.

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 that this is a lookup tool with no annotations and no output schema, the description is thin. It does not specify what a successful result looks like, whether it returns a single row or multiple, or any edge cases (e.g., no match, case handling for non-string values). For a tool that an agent might use to retrieve data, more context is needed for correct invocation and interpretation.

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 for parameter semantics. The description explains that 'value' is the value to match and 'column' is the column to match against, which adds meaning beyond the schema's bare type definitions. However, it doesn't provide details on allowed column names or value formatting (e.g., case-insensitivity implies any case is acceptable). This is adequate but not comprehensive.

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

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

The description states a clear verb ('look up') and resource ('rows of the Background Check Quotes dataset'), and specifies the matching semantics ('where a column equals a value exactly (case-insensitive)'). While it doesn't explicitly differentiate from siblings, the clarity of the operation is high, and the title reinforces the purpose.

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 clearly implies when to use this tool: when you need to look up rows by an exact key match. It does not explicitly state when not to use it, but given the sibling tools like dataset_search are likely for fuzzy or more complex queries, the context is clear enough. However, it does not name an alternative or condition for exclusion, so it falls short of a 5.

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

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

No annotations are provided, so the description carries the full behavioral burden. It discloses two useful traits: case-insensitive matching and a 50-row cap. However, it does not describe the return structure or edge-case behavior (e.g., empty result handling), and the read-only nature is only implied by 'search' rather than stated. This is serviceable but leaves gaps for a 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 tight sentence with zero filler—the action, scope, matching rule, and cap are packed into one line. All information is front-loaded and every word contributes meaning. There is nothing extraneous to trim.

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

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

For a simple two-parameter search tool with no output schema and no annotations, the description covers the core search behavior, case sensitivity, and row cap. Still, the absence of an output schema means the return format is unspecified, and the read-only/safety profile is unstated given no annotations. It is adequate but not fully complete for an agent that must infer response shape.

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

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

Schema coverage is 50% (only 'query' has a description; 'limit' does not), so the description partially compensates by binding 'cells contain the query' to the query parameter and 'up to 50' to limit. It adds the case-insensitivity detail absent from the schema, which is genuine value. It does not, however, elaborate on limit semantics beyond the redundancy with the schema's maximum.

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 verb and resource ('Rows of the Background Check Quotes dataset') with concrete behavior: cells containing the query, case-insensitive, capped at 50. This is clear and actionable. It falls just short of a 5 because it doesn't explicitly contrast with siblings like dataset_row or dataset_top, though the 'any cell' matching behavior implicitly distinguishes it from column-level and row-by-id lookups.

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 intended use case—searching any cell for a query string—is clearly implied by the description, but there is no explicit mention of alternatives or when-not-to-use guidance. With siblings like dataset_row, dataset_top, dataset_compare, and dataset_provenance present, an agent might benefit from routing hints, but the description's 'any cell' wording conveys the primary scenario adequately without naming 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 burden of disclosing behavior. It goes beyond simple 'computes statistics' by explaining that grouping commas and currency are handled, non-numeric rows are excluded and counted, and the output includes count, min, max, mean, median, sum. This provides concrete expectations about data cleaning and output content, which is valuable behavioral context. However, it doesn't disclose edge cases like empty columns or error behavior, but for a stats function with these details, a 4 is justified.

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 packs key information: the metrics computed, the dataset context, data cleaning behavior, and exclusion handling. It is concise but potentially dense; the information is front-loaded with the metrics list. It earns its place, though the sentence could be slightly unwieldy due to parentheses, but it's still efficient, so 4.

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

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

The tool has one parameter, no output schema, and no annotations, with low complexity. The description covers the purpose, metrics, and data cleaning but omits specifics like output format (e.g., JSON structure), return value details, or edge cases (empty column all non-numeric). Since the output schema is absent, the description should clarify returns, but it only lists the stats without describing the response format有理. This leaves some gaps, but for a simple stats tool, it's adequate, so 3.

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

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

Schema coverage is 0%, so the description must compensate for the 'column' parameter. It mentions 'numeric column of the Background Check Quotes dataset', which indicates the parameter refers to a column name, but does not specify format, case sensitivity, or how column names are validated. For a single parameter with zero schema description, the description offers minimal meaning beyond 'column', so this is a significant gap, scoring 2.

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

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

The description states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column, clearly identifying the verb 'compute' and the resource 'numeric column'. It distinguishes from siblings like dataset_row (row retrieval) and dataset_search (search) by focusing on aggregation, though it doesn't explicitly name alternatives. This is clear but lacks explicit sibling differentiation, so 4 is appropriate.

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 the tool is for statistical analysis of a numeric column, and mentions that non-numeric rows are excluded and counted, giving context on data handling. However, it doesn't explicitly state when to use this tool versus alternatives like dataset_top (which might show top values) or dataset_compare (which might compare columns). The guidance is useful but implicit, warranting a 3.

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 disclose behavior itself. It communicates that rows are sorted by a numeric column and either highest or lowest are returned depending on the ascending flag. It does not mention default limits, tie handling, or behavior for non-numeric columns, but the core ranking behavior is 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 compact sentence that conveys the action and intent without filler. It is front-loaded with the core behavior and the illustrative quote adds usability.

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 ranking tool, the description covers the main purpose and ranking direction. However, with no output schema and no annotations, the agent still lacks details about the returned row format, limit defaults, or behavior on invalid columns.

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 low at 33% since only 'ascending' has a description. The description clarifies that 'column' must be numeric and that ascending=false gives highest values, but it does not explain the 'limit' parameter, its default, or how the parameters interact. This is inadequate compensation for the sparse 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 the operation: returning the highest or lowest rows of a specific dataset by a numeric column, with a helpful 'most/least X' framing. It does not explicitly distinguish itself from sibling tools like dataset_search or dataset_stats, but the ranking behavior is distinctive enough to infer the tool's purpose.

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

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

The phrase 'which is the most/least X' provides clear context for when to use this tool. However, it does not contrast with alternatives or state when not to use it, leaving some inference required.

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?

There are no annotations, so the description carries the behavioral disclosure burden. It explains that submit_enquiry is free and not a guaranteed quote, and says this tool returns who receives the details, consent wording, and how confirmation works. However, it does not explicitly state that enquiry_describe itself is read-only or has no side effects.

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 front-loaded with the actionable instruction 'Read first' and names the related tool early. It uses a compact structured set of statements, although some phrases are slightly repetitive. No extraneous fluff beyond the necessary context.

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 documentation/help tool, this is reasonably complete: it explains the meaning of an enquiry, its non-purchase nature, and exactly what the returned output covers. The lack of an output schema is mitigated because the description enumerates the returned information.

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

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

The input schema has zero parameters and full coverage, so the description does not need to explain parameter behavior. There is nothing for the agent to configure, and the baseline for a zero-parameter tool 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 names the specific action: it states plainly what submit_enquiry does on Background Check Quotes and returns the content of that enquiry. This clearly separates it from submit_enquiry and the other sibling tools, though the wording could define the 'describe' behavior more directly.

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

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

'Read first.' gives a clear usage signal and positions this as the tool to consult before using submit_enquiry. The description explains that it clarifies the enquiry flow, pricing, consent, and confirmation, which tells an agent when the tool is relevant. It does not explicitly name when not to use it, but the intent is 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?

With no annotations, the description carries the burden. It clearly discloses the content (all fields with their metadata) and the relationship to submit_enquiry. However, it does not explicitly state whether the operation is read-only, whether it requires authentication, or whether there are any limits—though the nature of a field listing makes side effects unlikely.

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, information-dense sentences. The first sentence enumerates the field attributes concisely; the second sentence connects the output to the submission workflow. Zero 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?

The description fully explains what the tool returns and how the output should be used (keys for submit_enquiry). Since there is no output schema and no annotations, having the description spell out the field metadata is especially valuable. Minor omissions like error handling or pagination do not hurt this simple, parameterless 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 input schema is empty, so there are no parameters to describe. The description adds meaning by explaining what the response will contain (the field attributes) rather than just saying zero parameters, which gives the agent a sense of what data it will receive.

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 explicitly names the resource ('the Background Check Quotes enquiry') and enumerates exactly what is returned (key, label, type, required flag, help text, allowed options). It is clearly distinguishable from siblings like submit_enquiry (which acts on the fields) and dataset_columns (which deals with datasets).

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 implicitly tells the agent how to use the output: 'Pass answers to submit_enquiry keyed by field key.' This gives clear context for the tool's role in the enquiry workflow, but it does not explicitly state when to avoid this tool or compare it to other siblings (e.g., enquiry_describe).

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 full burden. It discloses the two-step nature, the requirement for user consent, the email link click requirement, and the exact consent text. It also warns it is not a purchase or guaranteed quote, preventing misuse. This is exceptionally 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 long but well-structured with a clear opening purpose and numbered steps. Every sentence carries necessary information (steps, consent, token, email, consent text). It is front-loaded with the core purpose and caveats, making it efficient for the complexity involved.

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 two-step nature and the need for explicit user interaction, the description covers all necessary aspects: validation, summary display, consent line, token usage, submission condition, and email notification. There is no output schema, but the description adequately explains the returned items (summary, token). It is complete 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 schema already covers all parameters with descriptions (100% coverage), so the baseline is 3. The description adds valuable context beyond the schema: it explains answers are keyed by field keys from enquiry_fields, clarifies the consent meaning, and specifies the confirmation token's role across steps. This elevates it above the baseline.

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 explicitly states the action ('Submits an enquiry to Background Check Quotes'), the resource, and the key caveats ('NOT a purchase, NOT a guaranteed quote'). It clearly distinguishes this from sibling tools like enquiry_fields and enquiry_describe by describing the submission flow, so an agent can immediately identify its purpose.

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 explicit step-by-step usage: Step 1 with answers and consent, returning a summary and token, and Step 2 only after user agreement with the token. It also instructs to show the summary and consent line, which is critical for correct interaction. This leaves no ambiguity about when and how to call the tool.

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