LeaseIQ Pro

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

With no annotations, the description fully discloses behavioral traits: it details the calculation methodology (money factor rent charge, residual-based depreciation, state tax methods, fee capitalization, zero-drive-off iteration, MSD discounts with ROI) and notes auto-conversion of APR. This is comprehensive for a calculator.

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 concise given the complexity; it is front-loaded with the core purpose ('Exact consumer car lease calculator') and then lists specifics. Every sentence adds value. It could be slightly shorter, but overall well-structured.

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 complex tool with 19 parameters and no output schema, the description explains the calculation approach and mentions outputs like drive-off and buyout amounts. However, it does not fully specify the return format or all output fields, which is a minor gap.

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 89% (high), so baseline is 3. The description adds context about the overall math but does not provide additional parameter-level details beyond what the schema already describes. It briefly lists key parameters in context, but the schema's own descriptions are thorough.

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 it is an 'Exact consumer car lease calculator using real captive-lender math' and lists specific operations (money factor rent charge, residual-based depreciation, state tax methods, fee capitalization, zero-drive-off iteration, MSD discounts). It also specifies use cases: verify dealer quotes, compare lease offers, compute drive-off and lease-end buyout amounts. This distinguishes it from siblings like 'calculate_auto_loan' and 'calculate_lease'.

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 clear guidance on when to use the tool ('verify dealer quotes, compare lease offers, compute drive-off and lease-end buyout amounts'). It also mentions that it accepts money factor or APR. However, it does not explicitly exclude scenarios or differentiate from sibling tools like 'solve_lease_structure'.

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 provided, so description carries full burden. It describes the tool as an 'exact' calculator and lists what it computes, which is appropriate for a read-only computation tool. No contradictions or omissions for a calculator.

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, front-loaded with main purpose, no superfluous words. Every sentence adds value.

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 7 parameters and no output schema, the description covers the main outputs and use case. Could mention edge cases or return format, but for a calculator it is reasonably complete.

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 86%, so baseline is 3. The description adds overall context (exact calculator, outputs) but does not significantly elaborate on individual parameters beyond the schema. Adequate.

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?

Clearly states it's an auto loan payment calculator for vehicle purchases, listing specific outputs (monthly payment, total interest, etc.) and distinguishing from sibling calculate_auto_lease via lease-versus-buy comparison.

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?

Explicitly mentions usefulness with calculate_auto_lease for comparisons, providing context for when to use this tool together with another. Lacks explicit 'when not to use' but the context 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?

No annotations are provided, so the description carries the full burden. It states the tool is 'deterministic' and 'regression-tested against Excel-verified amortization to the penny,' indicating reliability. It also mentions return values (payment, tax, etc.) and that it is a calculator (presumably read-only). However, it does not disclose if any side effects exist or any authorization needs, but for a calculator, this is sufficient.

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 paragraph of about five sentences, front-loading the core purpose and then detailing structures, reliability, usage guidance, and return values. Every sentence is substantive; no filler. It is efficiently structured for quick understanding.

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 complexity (20 parameters, no output schema, no annotations), the description provides a good high-level overview of capabilities and return values. It mentions 'Returns payment, tax, interim rent, and totals (total interest, total of payments, final balance).' While it doesn't detail every output field, for a calculator tool this is adequate. The sibling context helps with scope.

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 each parameter has clear descriptions in the schema. The tool description adds context by explaining how parameters like 'steps' and 'skipMonths' work, such as 'let the engine solve it' for Calculated type. This adds value beyond the schema, justifying a score above the baseline of 3.

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 is a 'deterministic commercial equipment lease and finance calculator' that computes exact periodic payments for various structures. It explicitly distinguishes itself from siblings like calculate_auto_lease and calculate_auto_loan by focusing on commercial equipment, not auto. The verb 'computes' and specific resource 'lease payments' are unambiguous.

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 explicitly advises using this tool over manual estimation: 'Use this instead of estimating lease payments: LLM arithmetic on compounding interest is unreliable, this engine is not.' It implies when to use it but does not explicitly mention when not to use or list alternative tools. However, the sibling list provides differentiation, and the context 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?

No annotations are provided, so the description carries full burden. It explains capabilities but does not explicitly state behavioral traits like idempotency, data modification (none expected), or performance implications. The description is adequate but lacks explicit safety or side-effect information.

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 concise (about 60 words), front-loads the main action, and every sentence adds value: output details, supported variations, and use cases. No redundancy or filler.

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 complexity (20 parameters, no output schema), the description sufficiently explains what the output will contain (payment date, amounts, splits, balance, totals). It lacks explicit output structure but is complete enough for an agent to understand the tool's purpose and expected results.

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. The description adds context about supported day counts and structures but does not elaborate on individual parameters beyond the schema. Thus it meets baseline but does not add significant extra meaning.

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 verb (generates), resource (amortization schedule), and distinguishes from sibling tools like calculate_lease by focusing on comprehensive period-by-period output. It specifies supported payment structures and day counts, leaving no ambiguity about 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 description provides explicit use cases: 'Use when the user needs the full payment schedule, a payoff balance, or interest/principal splits.' While it does not mention when not to use or alternatives, the sibling list makes distinctions clear, and the guidance is practical enough for an AI agent.

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 provided, so description must cover behavioral traits. It discloses the iterative Newton-Raphson method and the fact that it solves for unknowns. However, it does not describe return format, error handling, or side effects. Adequate but not thorough.

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 plus example list. Every sentence is informative. No verbose or redundant content. Well-structured, starting with core function then examples.

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 complexity (8 params, iterative method, no output schema), description covers core functionality but omits return value description, error cases, and parameter dependencies. Leaves gaps for an agent to infer.

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 high (75%), so baseline is 3. Description reiterates the purpose but adds little detail beyond schema descriptions. Does not explain conditional requirements (e.g., omit parameters when solving for them).

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?

Description clearly states it is a reverse solver using Newton-Raphson for commercial leases, solving for rate, term, residual, or PV. Includes example questions that distinguish it from sibling tools like calculate_lease.

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?

Explicitly describes when to use ('questions like ...') and mentions that LLMs cannot reliably do the iterative solving manually. Lacks direct comparison to siblings but context is clear.

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