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

No annotations are present, so the description carries full responsibility for disclosing behavior. It names the content (criteria and filters) but does not state what operation is performed, whether this is a read-only lookup, what output format is returned, or whether it affects any state. The phrase 'by capability comparison' is vague.

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 short and the key noun phrase is front-loaded. However, the sentence is grammatically incomplete and 'by capability comparison' is awkwardly placed, slightly reducing structural clarity.

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-input tool, the description is close to adequate: it states the domain and the subject matter. But it omits a clear operation verb and any hint about how this tool relates to the sibling comparison tools, so an agent would still need to infer the expected return behavior.

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

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

The tool has zero parameters and 100% schema coverage (empty schema), so there are no parameter semantics to explain. The description adds context about what subject matter the no-input tool covers, which is sufficient for this dimension.

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 subject matter — criteria and filters of US live answering and virtual receptionist services — and the title 'What is compared' reinforces the resource. However, it lacks an explicit verb such as 'lists', 'returns', or 'describes', so the action is implied rather than stated.

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

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

There is no guidance on when to use this tool versus siblings like compare_options or compare_table. No exclusions, alternatives, or contextual triggers are provided, leaving the agent to infer the correct choice from the tool name and description 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?

With no annotations, the description carries the behavioral disclosure burden. It discloses the compared fields and optional tag filtering, which is useful, but it does not clarify how filtering works, what the result structure looks like, or whether this is strictly a read-only 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 short and contains no filler, but it is grammatically awkward and reads as a noun phrase rather than a clear tool instruction. It would benefit from a leading verb and more logical sentence structure.

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 is incomplete for a tool with no annotations, no output schema, and two undocumented parameters. It omits filterKey semantics, does not distinguish itself from related sibling tools, and gives no indication of output shape or behavior beyond the field list.

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

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

Schema coverage is 0%, so the description must compensate. It partially explains filterValue by mentioning 'filtered by a tag value,' but filterKey is left entirely unexplained, and the relationship between filterKey and filterValue is not described.

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 communicates that the tool returns options for US live answering and virtual receptionist services compared by capabilities such as name, best-for, and tags. It is clear about the resource and the kind of output, though it lacks a direct verb and does not explicitly distinguish itself from sibling tools like compare_criteria or compare_table.

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

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

No guidance is provided about when to use this tool versus compare_criteria, compare_table, or other siblings. The optional tag filtering is mentioned, but there is no context about typical use cases, prerequisites, or when not to use the tool.

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 of behavioral disclosure. It explains what content the table contains but does not disclose behaviors like read-only status, error handling, how the option parameter narrows results, or what the output actually looks like.

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 conveys the core content of the table. It is efficient, though the trailing phrase 'by capability comparison — the full table' adds some redundancy after 'every criterion.'

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 read-only table tool, the description gives a reasonable sense of the return content. However, without annotations or an output schema, it leaves uncertainty about the exact return format and how sibling tools relate to this 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?

Schema description coverage is 100%, and the option parameter is already documented as 'one option's name, else all.' The description reinforces the full-table concept but adds no new semantic meaning beyond the schema.

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

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

The description specifies that the tool presents each option's value on every criterion, making it clear the result is a full comparison table. This is a clear verb+resource pairing, though it does not explicitly contrast with compare_criteria or compare_options beyond the phrase 'full table'.

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 'full table' implies this tool is for a comprehensive view, but the description does not explicitly say when to use compare_table versus compare_criteria or compare_options. Usage is implied rather than directly stated.

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 burden of explaining safety and side effects. It explicitly states that nothing is bought, ordered, or paid, that no quote is guaranteed, and that the service is free. It also discloses what information the tool returns, giving the agent a clear picture of expected 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 compact and front-loaded with the most important instruction ('Read first'). Each sentence adds value: the first defines the tool's role, and the second clarifies consequences and return content. No redundant wording is present.

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, informational tool with no output schema, the description is complete: it explains what the tool does, what submit_enquiry involves, what the user can expect, and what the tool returns. Nothing essential is missing for an agent to decide to invoke it.

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

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

The tool has zero parameters and 100% schema description coverage, so there is no parameter-semantic burden for the description. The description appropriately focuses on the tool's output and purpose rather than parameter details.

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 explains what submit_enquiry does, naming the resource (Answering Service Picker) and the action (starts an enquiry with human providers). It also differentiates the tool's informational role from the actual submission flow, making its purpose unmistakable.

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

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

'Read first' explicitly positions this tool as a prerequisite before using submit_enquiry or related actions. It does not name alternatives or exclusion conditions, but the context strongly implies it should be read before proceeding, which is sufficient guidance for a documentation-style tool.

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 fully discloses the returned data's composition and the intended downstream use with submit_enquiry, which makes the tool's read-only, schema-like nature clear. It does not mention auth, rate limits, or side effects, but for a parameterless field-listing tool these are not material gaps.

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 exactly two sentences with no wasted words. The first sentence front-loads the complete field enumeration, and the second immediately provides actionable guidance for using the result. 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 parameterless tool with no output schema, the description fully defines the return content and the expected next step. An agent can confidently call this tool and know exactly what information it will receive and how to use it with submit_enquiry.

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 an empty input schema, so the description has no parameters to explain. The baseline of 4 for a zero-parameter tool applies, and the description adds relevant context about how the output should be used.

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 exactly what the tool returns: every field of the Answering Service Picker enquiry, listing key, label, type, required, help text, and options. It distinguishes itself from submit_enquiry by establishing that this tool provides the field keys used when submitting answers, and from the compare_* siblings by focusing on the enquiry's fields rather than comparisons.

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 workflow context: 'Pass answers to submit_enquiry keyed by field key.' This tells an agent to call this tool before submitting and explains how to map results to parameters. It does not explicitly mention when not to use it or compare against sibling tools, so it falls just short of full explicit routing.

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

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

With no annotations, the description carries the full burden and does so: it discloses that step 1 is validation-only and returns a summary, consent line and confirmation token; that step 2 is the actual submission; that a double opt-in email link must be clicked before any provider sees the data; and it quotes the exact consent semantics including the 'up to three US providers' disclosure. This is unusually complete for an unannotated mutation 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?

Front-loaded with the operation type and the two negations before the procedural detail, and every sentence carries procedural weight. It is on the long side and the verbatim consent sentence duplicates what the schema already states for the consent parameter, which is the only real waste.

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 three-parameter, no-output-schema tool, the description covers the full flow an agent needs: inputs per step, what step 1 returns, when to proceed, what the end user must do (click the email link), and what consent legally means. Nothing material for correct invocation is missing.

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

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

Schema description coverage is 100%, so baseline is 3, but the description adds real meaning beyond the schema by explaining the temporal role of each parameter: answers keyed by field key from enquiry_fields, consent=true in both steps, and confirmation populated only in step 2 from the step-1 token. It stops short of describing the answer value types or validation failure behavior, so it is not a 5.

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

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

Names a specific verb (submits) and resource (an enquiry to Answering Service Picker), and immediately bounds the scope with two explicit negations: 'NOT a purchase, NOT a guaranteed quote.' An agent can tell this apart from the compare_* siblings and from the enquiry_fields/enquiry_describe prerequisites without opening any schema.

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?

Gives an explicit ordered procedure: Step 1 call with answers + consent=true, show the person the summary and consent line; Step 2 only if the person agrees, call again with the same answers plus the confirmation token. The gating condition for the second call is stated, and the dependency on enquiry_fields for the answer keys is implied clearly. Nothing is left to inference.

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