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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 the full burden of behavioral disclosure. It only describes content ('criteria and any filters') without stating whether the tool is read-only, what output it produces, or how the data is presented. This leaves the agent without a clear model of the tool's 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 extremely brief and contains no filler or redundant phrasing. It front-loads the core subject matter, though its noun-phrase structure sacrifices some clarity for 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 zero-parameter tool, the description conveys the subject matter but not the expected action or return value. The absence of an output schema and lack of behavioral context mean the description is minimally complete but not fully self-sufficient.

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 properties, so there are no parameters to document. The description does not need to explain any parameter semantics, and the baseline for zero-parameter tools is 4.

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 clear resource: 'the criteria and any filters' for the US HVAC field service management platforms comparison. However, it is phrased as a noun fragment rather than a specific action like 'returns' or 'lists', so the operation is inferred rather than stated. It distinguishes itself somewhat from sibling tools by referencing criteria/filters rather than options or a 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?

There is no guidance on when to use compare_criteria versus alternatives like compare_options or compare_table. The purpose implies it provides the comparison criteria, but no explicit usage conditions, exclusions, or selection criteria are 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?

With no annotations, the description must disclose behavior itself. It states what data is included and that filtering by a tag value is optional, giving a partial read-only picture. However, it does not describe output format, unfiltered behavior, filter matching rules, or side effects, which leaves a moderate transparency gap.

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 with no filler or redundant content. It is slightly awkward because it lacks a verb and front-loads the scope in a parenthetical, but it remains efficient and readable.

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 with two optional parameters and no output schema, but the description leaves filterKey unexplained and does not state the response shape or how optional filtering behaves in practice. An agent could understand the general purpose but would struggle to construct a correct filtered call confidently.

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 and no enums, so the description must compensate. It clarifies that filtering is by a 'tag value', which maps loosely to filterValue, but it leaves filterKey undefined and does not explain how the two parameters interact or what valid tag values are. This is only partial compensation for zero 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 description identifies a specific resource (options in the US HVAC field service management platforms comparison), lists the data fields (name, what it is best for, tags), and notes optional tag filtering. It does not use an explicit verb like 'list' or 'get', but the noun and scope are clear and distinct from sibling tools like compare_criteria and 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 when-to-use or alternative guidance is provided. The only usage hint is the optional tag filter, but the description does not say when compare_options should be chosen over compare_criteria or compare_table, nor when the filter should be applied.

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 behavioral disclosure burden. It does communicate the return scope — all options and all criteria — and the 'full table' phrasing implies a read-only retrieval, but it does not describe output format, default behavior beyond the schema, or error handling. This is acceptable for a simple tool 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?

The description is a single focused sentence with no filler. It front-loads the core idea — each option's value on every criterion — and the closing 'the full table' reinforces completeness without being redundant.

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

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

For a simple tool with one optional parameter and no output schema, the description adequately explains what is returned. The schema covers the parameter, so nothing essential is missing for correct invocation, though a brief pointer to sibling tools would improve it further.

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 provides 100% coverage of the single 'option' parameter with 'one option's name, else all'. The description adds no further parameter-level meaning, so the baseline 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 clearly defines what the tool returns: a full matrix of every option's value on every criterion for the US HVAC comparison. This distinguishes it from siblings like compare_criteria and compare_options, which likely return individual dimensions rather than the complete 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 description implies this tool is for retrieving the full comparison table, but it does not explicitly state when to use it instead of compare_criteria or compare_options. No exclusions or alternatives are mentioned, so the agent must infer usage from the sibling names and context.

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 behavioral burden, and it does so thoroughly. It discloses that the underlying action starts an enquiry, that nothing is purchased or paid, that no quote is guaranteed, that it is free, and what information is returned.

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, front-loaded with the most important instruction ('Read first'), and every sentence adds value. It avoids redundancy and clearly organizes the key facts about cost, commitment, and return content.

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 no parameters and no output schema, so the description must explain both behavior and return value. It does: it explains what the tool communicates, what is not true about the enquiry, and what additional information is returned. There are no significant gaps for an agent to call 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?

The tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the empty schema already fully covers the input surface.

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, specific purpose: it explains what submit_enquiry does, and differentiates itself from the actual submission action by being a 'read first' informational tool. It also names the exact scope (HVAC Software Picker) and the key outcome (starting an enquiry with human providers).

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 'Read first' gives an explicit usage directive: consult this tool before using submit_enquiry. It does not explicitly name sibling alternatives or state when not to use it, but the context is clear enough that an agent would know to call this before taking action.

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. It communicates that the tool provides field metadata and that answers belong with submit_enquiry, which implies a read-only lookup. It does not explicitly state that nothing is submitted or modified, nor does it mention any authentication or rate considerations.

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 sentences with no filler. The first sentence enumerates the complete field content, and the second gives actionable guidance on how to use the results with submit_enquiry. 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 zero parameters and no output schema, the description covers the main return contents and how to consume them, satisfying the key burden. It could be slightly more explicit about the tool's read-only nature and its relationship to enquiry_describe, but the essential information for correct invocation is present.

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 accepts zero parameters, so there are no parameter semantics for the description to clarify. The baseline of 4 applies, and the description appropriately avoids inventing unnecessary 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 identifies the resource—the HVAC Software Picker enquiry fields—and enumerates exactly what is included: key, label, type, required, help text, and allowed options. It is distinguishable from submit_enquiry and the compare_* siblings, though it lacks an explicit verb like 'Returns' or 'Lists'.

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 downstream guidance by telling the agent to pass answers to submit_enquiry keyed by field key, implying that this tool supplies the form schema. However, it does not explicitly explain when to use this tool instead of enquiry_describe or the compare_* tools, or state any 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 behavioral burden and does so thoroughly: it discloses the two-step validation flow, the returned summary/consent line/confirmation token, the post-submission email-link gating before providers see details, and the exact consent meaning. This gives the agent a clear picture of side effects and required user interactions.

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 purpose and uses a clear step-by-step structure appropriate for a complex two-step tool. The verbatim consent quote is lengthy and duplicated from the schema, slightly reducing conciseness.

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 workflow, consent requirements, lack of annotations, and no output schema, the description is complete: it explains return values, side effects, and the post-submission email verification. An agent has everything needed to 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 coverage is 100%, so the schema already documents each parameter, establishing a baseline of 3. The description adds meaning by linking the confirmation token to step 1's output and clarifying that the same answers must be resubmitted, which enriches parameter semantics 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?

States a specific verb ('Submits') and resource ('enquiry to HVAC Software Picker'), and clarifies it is not a purchase or guaranteed quote. However, it does not explicitly differentiate from sibling tools like compare_options or enquiry_describe, so it falls short of 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?

Provides explicit step-by-step conditions for when to call (step 1 with answers and consent; step 2 only if the person agrees) and references enquiry_fields for field keys. It does not name alternative tools for the core action, so no explicit alternative routing.

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