LeaseIQ Pro

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

With no annotations provided, the description carries the full burden. It discloses significant behavioral details: money factor rent charge, residual-based depreciation, state tax methods, fee capitalization, zero-drive-off iteration, MSD money-factor discounts with ROI, and the auto-conversion between money factor and APR. This is rich context beyond the schema. It could mention the output format, but it does hint at buyout amounts. No contradiction with annotations (none).

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?

Three sentences, no waste. The first sentence is dense but every clause adds value (tax methods, fee cap, zero-drive-off, MSD). The second clarifies input selection, and the third gives use cases. It is front-loaded with 'Exact consumer car lease calculator' and maintains a logical flow.

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 19-parameter tool, the description covers major behaviors: tax methods, MSD, zero-drive-off, and buyout computation. However, with no output schema, it doesn't state exactly what the return object contains (e.g., monthly payment, total cost). It also doesn't explicitly mention required parameters (msrp, term, residualPercent). These minor gaps prevent a 5.

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 covers 89% of parameter descriptions, so the baseline is 3. The description adds critical value by clarifying the OR relationship between moneyFactor and apr ('Accepts either a money factor... or an APR (auto-converted)'), which the schema does not enforce. It also references zero-drive-off and MSD behaviors that map to parameters, enhancing understanding beyond raw 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?

The description clearly identifies the resource ('consumer car lease'), the action ('calculate'), and the exact scope ('real captive-lender math...'). It distinguishes itself from generic lease calculators by enumerating specific features like state tax methods, MSD discounts, and zero-drive-off iteration. This is a specific verb+resource with strong differentiation from siblings.

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 ends with explicit use cases: 'verify dealer quotes, compare lease offers, or compute drive-off and lease-end buyout amounts.' This provides clear context for when to use the tool. However, it does not mention alternatives or exclusions (e.g., comparing to calculate_lease or solve_lease_structure), 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the two modes (finance vs. cash) and the outputs, but does not disclose important behaviors such as the default of purchaseMethod='finance', the handling of omitted rate/term for cash purchases, or what exactly 'upfront cash' includes. This is adequate but incomplete.

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: two meaningful sentences plus a use-case sentence. The first sentence is somewhat redundant with the title, but it still adds specificity. No wasted words.

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 covers the main functionality and mentions the lease comparison sibling, but lacks detail on parameter interactions (e.g., which parameters are used for cash vs. finance), exact meaning of outputs like 'upfront cash', and differences from other siblings like generate_amortization_schedule. Given the moderate complexity and lack of output schema, it is complete enough for basic use but has gaps.

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 the baseline is 3. The description itself does not add parameter-specific detail beyond the schema; it focuses on outputs and use cases. The schema already documents parameters well, so the description provides marginal extra value.

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 purpose: an exact auto loan payment calculator for vehicle purchases, listing specific outputs (monthly payment, total interest, total cost, upfront cash for finance, all-in cost for cash). It distinguishes itself from sibling tools by explicitly mentioning calculate_auto_lease for lease-versus-buy 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 provides clear context for when to use the tool (vehicle purchases, loan vs. cash) and suggests pairing with calculate_auto_lease for comparisons. However, it does not explicitly exclude alternative tools like generate_amortization_schedule or state when not to use this 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 full burden. It discloses deterministic, penny-accurate behavior ('Regression-tested against Excel-verified amortization to the penny'), distinguishes itself from LLM estimation, and lists return fields (payment, tax, interim rent, totals). This adds meaningful context beyond the schema. It doesn't cover every edge case but is solid for a calculator 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 four sentences, each with a distinct purpose: state the tool's function, enumerate supported structures, provide usage guidance, and list returns. It is front-loaded with the core purpose and contains no redundant 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?

For a 20-parameter tool with no annotations and no output schema, the description covers the essential context: what it does, when to use it, accuracy guarantees, and return values. It doesn't explain all structural nuances, but the schema already documents every parameter, making this adequate and slightly above the minimum viable level.

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 is 3. The description names structure types (level, balloon, step-up/step-down, etc.) that mirror the paymentStructure enum, and mentions interim rent which relates to fundingDate/leaseStartDate, but it doesn't explain individual parameters beyond what the schema already provides. It meets the baseline but doesn't exceed it.

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 verb and resource: 'Computes the exact periodic payment for equipment leases and equipment financing agreements'. It clearly distinguishes from sibling tools like calculate_auto_lease and calculate_auto_loan by focusing on commercial equipment, and lists supported structures (level, balloon, step, custom, interest-only, skip).

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 says 'Use this instead of estimating lease payments' and explains why (LLM arithmetic on compounding interest is unreliable). While it doesn't name alternative tools, the phrasing clearly communicates when to use this calculator over ad-hoc estimation, and the focus on equipment leases differentiates it from auto loan/lease tools.

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 burden of behavioral disclosure. It explains that the tool supports all payment structures and day counts, and lists the output fields, giving a solid understanding of behavior. However, it does not mention limitations, error handling, or explicitly confirm the operation is a safe read-only calculation, leaving room for more transparency.

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 three sentences, each with a distinct purpose: statement of the core function, supported features, and usage guidance. It is front-loaded with the primary action and contains no redundant or unnecessary information.

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 covers the core output fields and lists common use cases, which is essential since no output schema exists. It could be slightly more detailed about default behaviors or return object structure, but given the tool's complexity (20 parameters), the description provides a well-rounded understanding of what the tool does and when to use 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 schema has 100% description coverage for parameters, so the baseline is 3. The description adds minimal parameter-specific meaning, though it mentions support for all payment structures (matching the paymentStructure enum) and both 30/360 and Actual/365 day counts (which are not explicit parameters). This adds some context but does not significantly enhance understanding of individual parameters beyond what the schema provides.

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 as generating a complete period-by-period amortization schedule for commercial equipment leases or finance agreements, listing specific output fields (payment date, amount, interest, principal, balance, totals). This specific verb+resource+scope distinguishes it from sibling tools like solve_lease_structure, which focus on solving a single lease structure rather than generating a full schedule.

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 states when to use the tool: 'Use when the user needs the full payment schedule, a payoff balance at a specific period, or interest/principal splits for accounting.' This provides clear context for selection, though it does not explicitly name alternative tools or list when-not-to-use conditions, so it stops short of the top score.

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?

The description discloses the Newton-Raphson algorithm and the iterative nature, giving insight into how it works. It does not cover potential convergence issues, error conditions, or output format, but with no annotations provided, this is a reasonable amount of behavioral detail for a calculation 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 four sentences, front-loaded, and wastes no words. The final sentence about LLM reliability is somewhat extra but arguably useful for tool selection.

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 lacks explicit guidance on which parameters to supply for each solveFor value, which is critical given 8 parameters and no output schema. The schema hints at omitting the solved variable, but does not clarify the full required input set for each scenario, leaving invocation ambiguous.

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 covers parameter descriptions for 75% of the fields, and the description adds minimal extra parameter semantics. The description's examples map to the solveFor values but do not explain the dependencies between parameters (e.g., which inputs are required for each solveFor scenario).

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 explicitly identifies the tool as a 'reverse solver' that solves for an unknown variable (rate, term, residual, or PV) given a target payment. It clearly differentiates from sibling tools by specifying 'commercial equipment leases' and the reverse-solving nature.

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 usage context with example questions like 'what rate am I really paying?' and notes this tool handles iterative solving that LLMs cannot do reliably by hand. However, it does not explicitly contrast with forward calculators like calculate_lease or state when not to use them, so it falls 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.