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

No annotations are provided, so the description bears the full burden of explaining behavior. It only states criteria and filters as the subject matter and does not disclose whether the tool returns data, lists available options, or performs any other observable action.

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 places 'criteria' first, but it is an ungrammatical fragment with a long prepositional chain. It could be much clearer with a simple verb-headed sentence such as 'Returns the criteria and filters used for the comparison.'

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 should clearly state what the call yields. It gestures at criteria and filters, which is probably sufficient for a trivial invocation, but it does not clarify the output format or distinguish this tool from sibling comparison tools.

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?

This tool has zero parameters, so there is no parameter information for the description to add. The schema already covers everything needed, and the description does not need to compensate for missing parameter docs.

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 resource: criteria and filters in the context of a Field Service Management software comparison. However, it is a noun phrase without an explicit verb like 'lists', 'returns', or 'compares', so the tool's action remains somewhat vague.

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 compare_criteria versus compare_options, compare_table, or the enquiry_* sibling tools. No alternatives or conditions are mentioned, leaving tool selection to inference.

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 full responsibility for behavioral disclosure. It does not state whether the tool is read-only, how filtering behaves, what the output looks like, or any side effects. This is insufficient for an unannotated 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 single run-on sentence is not well-structured and front-loads little useful information. The title 'The options' contributes no value, and the description is vague enough that the clarity cost outweighs its brevity.

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 tool with no annotations, no output schema, and 0% parameter coverage, this description fails to explain return value, parameter semantics, or usage context. An agent would be guessing about filterKey and the overall 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?

Schema coverage is 0%, and the description only clarifies that filtering is by 'tag value' (likely mapping to filterValue). The purpose and format of filterKey remain entirely unexplained, leaving the agent to guess how to construct a valid call.

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 resource ('the options' in FSM software for UK multi-engineer operations) and hints at what it returns (name, best-for, tags), but it lacks an explicit verb. It reads as a noun phrase rather than stating an action like 'list' or 'compare', so an agent must infer the operation.

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 given on when to use this tool versus siblings like compare_criteria or compare_table. The optional tag filter is mentioned, but there is no context about when filtering is appropriate or what problem this tool solves.

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 behavioral disclosure, but it only describes the table's content, not the tool's behavior. It does not state output format, whether omitting 'option' returns all options, read-only status, or any other operational trait.

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 front-loads the key scope (each option's value on every criterion) and ends with the clear 'full table' qualifier. There is 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 simple one-parameter read tool, the description is minimally adequate: it conveys the table's content. But with no output schema and no annotations, it leaves the exact return shape and the effect of the optional 'option' parameter implicit, and it does not differentiate clearly from sibling tools.

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 already documents the single 'option' parameter with 100% coverage. The description adds no extra meaning about the parameter's format, allowed values, or default behavior, so the baseline score of 3 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 the resource (the comparison table) and the exact granularity: every option's value on every criterion. The verb is only implicit (returns/provides), but 'the full table' makes the retrieval intent clear and helps separate it from sibling tools like compare_criteria and compare_options.

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 'the full table' implies this tool is for obtaining a complete option-by-criterion comparison, offering some usage context. However, it never explicitly says when to prefer this over compare_criteria or compare_options, nor does it mention any exclusions or conditions.

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 transparency burden, and it does well by disclosing that nothing is bought, ordered, or paid, that no quote is guaranteed, and that the service is free. It also tells the agent what information is returned: who receives the details, the consent wording, and how confirmation happens.

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') followed by the tool's informational purpose. It includes a few helpful specifics about return content without excessive verbosity, though the title already conveys some of the same 'not a purchase, not guaranteed' messaging.

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, no-output-schema descriptive tool, the description is complete: it specifies the operation it describes, the behavioral expectations, and what the response will convey. An agent has everything needed to call this tool correctly and understand the result.

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-level documentation is not needed. The schema covers this vacuously, and the baseline of 4 applies because there is nothing for the description to add.

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 'States plainly what submit_enquiry does' on Which Field Service, framing itself as the descriptive counterpart to that action. It also clearly differentiates from the sibling submit_enquiry tool by emphasizing the outcome is an enquiry with human providers, not a purchase or guaranteed quote.

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 explicit advice to use this tool before submit_enquiry, which is strong usage guidance for a descriptive companion tool. It does not mention the other sibling tools, but they are not direct alternatives to this one, so the guidance is adequate.

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 transparency burden. It tells the agent that the tool returns a comprehensive field definition and that submission is handled by submit_enquiry, which implies this tool itself is read-only and non-mutating. It could be more explicit about side-effect-free behavior or output shape, but the description is reasonably transparent for a metadata-listing 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 two tight sentences with no filler. The first sentence enumerates exactly what is returned, and the second provides the key usage instruction. It is front-loaded and every phrase 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 zero-parameter metadata tool with no output schema, the description covers the essential content and its relationship to submit_enquiry. It could more explicitly describe the return structure (e.g., list vs object) or confirm the absence of side effects, but none of these are critical given the tool's simplicity and the presence of the sibling hint.

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 cannot add much parameter-level detail. It does add value by explaining that field keys from the output are the expected keys for submit_enquiry, which is semantically useful. This meets the 0-parameter baseline and adds a relevant cross-tool hint.

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 exposes: every field of the enquiry with key, label, type, required flag, help text, and allowed options. It connects to submit_enquiry by saying answers should be keyed by field key, which helps distinguish it from the comparison-focused siblings. However, it lacks an explicit verb like 'lists' or 'returns', and it does not contrast with 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?

The instruction 'Pass answers to submit_enquiry keyed by field key' provides clear context and a concrete usage pattern. It implies this tool should be consulted to learn the field keys before submitting an enquiry. It does not explicitly state when not to use it or name alternatives, so it stops 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 does this well by disclosing that the first call does not submit, that a confirmation token is required for the real submission, that an email with a click-link is sent, and that providers only see the enquiry after that click. It also discloses the exact consent wording and that this is not a purchase or quote.

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 every clause carries necessary operational information. It front-loads the core purpose and non-purchase disclaimer, then organizes the two steps explicitly. The length is justified by the tool's genuine procedural complexity.

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

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

There is no output schema, so the description correctly explains the step-1 return values (summary, consent line, confirmation token) and the final outcome (submission, email, link click, provider visibility). It also references enquiry_fields for answer keys, completing the contextual picture for an agent using 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 coverage is 100%, and the description goes further: it explains that answers must be keyed by field key from enquiry_fields, that consent is only true when the person agrees to the exact consent text, and that the confirmation token comes from step 1 and enables the final submission. This meaningfully enriches the schema definitions.

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?

Describes a specific action — submitting an enquiry — and clearly distinguishes it from a purchase or guaranteed quote. The title and description together make it obvious this is not one of the comparison tools or the enquiry_fields descriptive tool.

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 an explicit two-step invocation sequence: step 1 validates and returns a token, step 2 submits only after person agrees and the token is supplied. It also states the condition for proceeding ('only if the person agrees') and notes the email-link requirement, so the agent knows 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.