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

With no annotations provided, the description carries the full disclosure burden and does reasonably well: it lists the four result values an agent should expect and explicitly states that missing inputs fall back to documented defaults. For a pure computation tool this covers the meaningful behavioral surface, though error handling and edge cases are not addressed.

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 the run instruction and output summary front-loaded. The semicolon-delimited output list is slightly choppy, but every clause earns its place and nothing is 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?

Despite 8 optional parameters, no output schema, and no annotations, the description covers the expected outputs and default-fallback behavior, so an agent can safely invoke it. It falls short by not addressing when to select it over 'calculator_describe' or how the 'roughly per month' figure is derived.

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 the baseline of 3 applies: every parameter is already documented with types, enums, and defaults. The description only gestures at parameters through 'your choices' and adds no syntactic or format detail 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 ('Run') and resource (the annuity income calculator), and enumerates four concrete outputs the call produces, so an agent knows exactly what the tool does. It does not explicitly differentiate from the sibling 'calculator_describe', but the run-vs-describe distinction is strongly implied by the wording.

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?

Usage is implied: run this calculator to see income before and after applying choices. However, the description never states when to prefer this tool over 'calculator_describe' or the enquiry siblings, and it provides no exclusions or alternative 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?

No annotations are provided, so the description carries the transparency burden. It states what content the tool returns (inputs, outputs, assumptions, tables), but it does not explicitly confirm that the tool is read-only or describe side effects, limitations, or access requirements. For a descriptive tool this is acceptable but not fully explicit.

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, tightly structured sentence with no filler. It front-loads the main purpose and enumerates the key content categories efficiently.

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?

With no output schema, the description adequately enumerates the return categories: inputs with units/ranges/defaults, outputs, and assumptions/tables. It omits formatting details or examples, but for a documentation-description tool this level of detail is 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 tool has zero parameters and schema coverage is 100%, so the baseline is 4. The description adds useful semantic clarity by indicating that the 'inputs' mentioned are the calculator's documented inputs, not arguments the agent must supply to this describe 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 tool's resource (the calculator) and scope: inputs with units/ranges/defaults, outputs, and assumptions/tables. It is distinguishable from the sibling 'calculate' because it describes documentation content rather than performing a calculation, though it relies somewhat on the tool name for the active verb.

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 understanding the calculator's inputs, outputs, defaults, and assumptions, rather than for running a calculation. However, it does not explicitly state when to use it versus the sibling tools or mention any exclusions or 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, the description carries the full burden of behavioral disclosure, and it performs well: it explicitly states that nothing is bought, ordered, or paid, that no quote is guaranteed, and that it is free. It also reveals what the output contains—who receives the details, consent wording, and confirmation method—which is important because there is no output schema.

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: 'Read first' immediately communicates the intended use, followed by a clear statement of purpose and boundary conditions. Every clause earns its place, with 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 zero-parameter, annotation-free tool with no output schema, the description covers the essential context: what the tool does, what it returns, and what it guarantees (nothing). It does not describe exact output formatting, but that is unlikely to block correct invocation for an informational describe 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 has zero parameters, so there is nothing for the description to explain about parameters. The baseline of 4 applies because no parameter documentation burden exists.

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 resource and action: it 'states plainly what submit_enquiry does' on Current Annuity Rates, clearly identifying itself as the explanatory companion to submit_enquiry. It also distinguishes the tool from the actual submission by highlighting that nothing is purchased or guaranteed, so an agent can tell this is a describe tool rather than an action 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?

The opening directive 'Read first' explicitly tells the agent to consult this tool before doing anything else, which is strong temporal guidance. It does not enumerate exclusions or alternatives, but for a describe tool the relevant alternative is clearly submit_enquiry, and the description makes the relationship plain.

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 transparency burden. It clearly enumerates the returned field metadata and implies a read-only lookup, but it does not explicitly state that there are no side effects, whether authentication is needed, or how the field collection is returned. For a zero-argument metadata tool this is adequate but not richly 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?

Two sentences accomplish both description and usage guidance with no redundancy. The core information about what the tool returns is front-loaded, and the follow-up sentence adds practical integration guidance.

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 carry the burden of explaining return values. It covers the output fields and the path to submit_enquiry well. It does not specify the exact return container (e.g., array of objects), but for a metadata listing this is 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 tool has zero parameters and the schema is empty, so the baseline is 4. The description's mention of passing answers to submit_enquiry keyed by field key refers to the downstream tool's parameters rather than this tool's, and provides useful context for how the output should be consumed.

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 exposes every field of the Current Annuity Rates enquiry and lists the exact attributes returned (key, label, type, required, help text, allowed options). It is specific about the resource and content, but it does not explicitly differentiate itself from the sibling tool 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 description tells the agent to pass answers to submit_enquiry keyed by field key, which clearly indicates when this tool should be used: before submitting an enquiry. It does not explicitly name alternatives or exclusions, but the submission workflow is clearly implied.

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 disclosure burden. It thoroughly discloses the two-step nature, validation, returned artifacts, consent wording, email delivery, and the post-email click requirement before any provider sees the enquiry. This is exemplary behavioral transparency for a conversational submission 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 well-organized paragraph with a clear front-loaded disclaimer and numbered steps. It is fairly long, but every sentence carries necessary operational detail; the step structure makes the length justified rather than bloated.

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 output schema but a nontrivial two-step workflow, the description covers almost everything an agent needs: inputs, consent semantics, step sequencing, conditional second call, returned artifacts, and the email/click side effect. It is complete enough for correct invocation without guessing.

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 describes all three parameters, so the baseline is 3; the description adds extra meaning by explaining that answers must use field keys from enquiry_fields, defining consent in terms of the exact agreement text, and clarifying that the confirmation token comes from step 1 and is required in step 2. This goes beyond the bare schema descriptions.

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's exact function: 'Submits an enquiry to Current Annuity Rates' and immediately distinguishes it from related but different actions ('NOT a purchase, NOT a guaranteed quote'). It also communicates the two-step submission process in the title and body, making the tool's 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?

The description provides explicit step-by-step usage: Step 1 requires answers and consent=true, returns a summary/consent/token; Step 2 is only if the person agrees, reusing the same answers plus the token. It also gives an explicit exclusion ('NOT a purchase, NOT a guaranteed quote') and references enquiry_fields for field keys, which orients the agent toward the correct workflow.

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