Xearno Tools

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

Annotations already declare readOnlyHint=true. The description adds context about the real cost of APR and compounding frequency effects, going beyond the annotations to explain the calculator's behavioral significance.

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 the key concept. Every sentence adds value with 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?

For a simple calculator with clear parameters and annotations, the description is sufficient. No output schema needed; the return value is implied by the conversion.

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% with parameter descriptions already present. The description adds no new parameter-specific details but provides conceptual background that aids understanding.

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 converts between nominal APR and effective APY at any compounding frequency, using specific terms and distinguishing the tool's purpose from generic rate conversion.

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 explains the concept of APR vs APY but does not explicitly state when to use this tool over siblings like compound_growth or cagr, nor does it mention when not to use it.

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 goes beyond annotations (readOnlyHint=true) by detailing the calculations performed: contribution margin, break-even units and revenue, margin of safety, and price change leverage. It accurately discloses the tool's read-only nature and output components.

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, each providing essential information: the core purpose and the list of outputs. No redundancy or unnecessary 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?

Given the lack of an output schema, the description effectively communicates the return values (contribution margin, break-even units, revenue, margin of safety, price change leverage). Parameter coverage is complete via schema. The tool's complexity is moderate and well-covered.

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 100% coverage for all four parameters. The description adds context like the margin-of-safety analysis tied to 'currentUnits', but this is not substantial beyond the schema's own descriptions. Baseline 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 states the tool's purpose: computing units and revenue needed to cover costs and the impact of pricing changes. It uses specific terms like 'break-even units', 'contribution margin', and 'margin of safety', making it distinct from siblings like 'pricing_margin' or 'unit_economics'.

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 usage for cost-volume-profit analysis but does not explicitly state when to use this tool versus alternatives. It omits exclusions or when not to use it, leaving the agent to infer from sibling tool names.

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?

Annotations already declare readOnlyHint=true, so the description's behavioral disclosure is minimal. It adds that the tool computes both CAGR and reverse projection, but does not elaborate on any other behavioral aspects (e.g., no mention of calculations or side effects). The description does not contradict annotations.

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 sentence with a clear front-loaded definition of CAGR. It is somewhat verbose with the 'honest average' metaphor, but overall efficient and structured for quick understanding. Slightly more concise could improve score.

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 output schema, so the description should ideally explain what the tool returns (e.g., CAGR value, reverse projection). It mentions 'CAGR calculation and reverse projection' but does not specify output format or if both are returned. With 3 parameters and no output schema, the description is adequate but leaves ambiguity about return values.

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 100% coverage with descriptions for all three parameters (years, endValue, startValue). The description mentions 'start and end value' and 'period', which maps to parameters but adds no new semantic meaning beyond what the schema already 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 states the tool calculates CAGR (compound annual growth rate) between start and end values over a period, and also performs reverse projection. It distinguishes itself by explicitly noting why CAGR is better than 'average return' for evaluating investments or revenue, contrasting with sibling tools like simple_interest or percentage.

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 CAGR over 'average return', but does not explicitly mention when not to use it or suggest alternative sibling tools. The context is sufficient for an agent to understand its typical use case, though exclusion criteria are absent.

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?

Annotations already declare readOnlyHint=true, so the agent knows it's a safe computation. The description adds value by disclosing the 'negative-equity warning' and 'long-loan trap', which are behavioral traits beyond the basic calculation.

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 concise sentences. First sentence captures the core purpose and unique warning. Second sentence lists inputs and reiterates the warning. No fluff; every sentence is informative.

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 6 parameters and no output schema, the description adequately explains inputs and the key behavioral trait (warning). It does not specify the exact output format, but the purpose is clear enough for a calculator 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% with each parameter described, so baseline is 3. The description restates the input parameters at a high level but does not add new semantic details 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?

The description clearly states it computes 'Monthly car payment' and lists the key inputs (down payment, trade-in, sales tax), also mentioning a unique 'negative-equity warning'. This distinguishes it from siblings like 'loan_payment' or 'mortgage' which are more generic.

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 the tool is for car loan calculations by mentioning 'dealership finance office', and highlights a distinct warning about negative equity. However, it does not explicitly tell the agent when to choose this over sibling tools like 'loan_payment' or 'mortgage'.

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?

Beyond the annotations (readOnlyHint=true), the description discloses the key behavioral trait: tax increases each month as cumulative income climbs brackets, causing take-home to fall. This adds valuable context for agent decision-making.

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 relatively concise with a clear structure: first sentence hooks with 'real China take-home', then explains method and behavior. Some redundancy in explaining cumulative method could be trimmed, but overall efficient.

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 no output schema, the description adequately explains the computation scope (monthly and annual IIT, net pay). It covers the cumulative method sufficiently for typical use, though missing edge cases like mid-year salary changes.

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% with detailed descriptions for each parameter. The description adds minimal extra meaning, but does clarify that monthlyGross is assumed constant across the year. Baseline 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 states it computes monthly and annual IIT using cumulative withholding method, distinguishing itself from simple monthly-bracket calculators. The verb 'computes' specifies the action on the resource 'China salary tax & net pay'.

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 explains when to use this tool for cumulative method and warns about inaccuracies of simple calculators. However, it does not explicitly mention when not to use it or provide direct comparisons to sibling tools like 'income_tax' or 'china_social_insurance'.

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 annotations already set readOnlyHint=true. The description substantially enriches transparency by detailing the tool's reliance on official cohort tables, the progressive delayed-retirement reform, and the precise components of the pension estimate. It does not contradict annotations.

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 well-structured with a clear purpose-first approach, but at four sentences it's slightly verbose. Each sentence adds value, but the last sentence could be integrated more concisely.

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 (12 parameters, no output schema), the description provides sufficient context about the reform and estimation logic. It explains the two modes and the key input (track) comprehensively, making it complete for an agent to decide when and how 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?

With 100% schema coverage, the description goes beyond schema descriptions by explaining the significance of parameters like 'track' (the single most disputed point) and 'mode' (project vs at-retirement), adding valuable context that aids correct parameter selection.

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 starts with a clear, specific verb 'Computes' and identifies the resource: statutory retirement age/date and monthly pension under China's 2025 reform. It explicitly distinguishes this tool from generic AI knowledge by noting that it uses official cohort tables, effectively differentiating it from siblings (none of which handle Chinese pensions).

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 by noting the reform is under two years old and that general AI quotes old ages. It implies use when accurate, reform-compliant data is needed. However, it lacks explicit when-not-to-use guidance or direct alternatives among sibling tools, though no sibling exists for this niche.

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?

Annotations indicate readOnlyHint=true, and the description consistently describes a calculation (computes, flips answer). It adds details on rounding rules, caps, and the role of termination reason, which go beyond the annotations. No contradictions.

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 the main concepts (statutory severance, N/N+1/2N, caps). It is well-structured but slightly verbose; each sentence adds value, though it could be tightened.

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?

No output schema exists, but the description explains the calculation logic and inputs sufficiently for an agent to understand what the tool does. It covers the key rules (rounding, caps, reason-dependent outcome). Minor gap: no explicit mention of the return format.

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% with descriptions for all 6 parameters. The description adds overall context about how parameters interact (e.g., reason flips answer) but does not significantly add per-parameter detail beyond what the schema already 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 states it computes statutory severance under China's Labour Contract Law, explaining N/N+1/2N and caps. It specifies the tool's domain (China job leaving) and distinguishes it from sibling financial calculators without needing explicit differentiation.

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 when to use: for computing severance when leaving a job in China. It highlights the termination reason as the decisive input. While it does not explicitly state when not to use, the context of sibling tools makes the use case 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?

The description provides significant behavioral details beyond the readOnlyHint: it explains the contribution base concept, floor/ceiling clamping, city-specific rates resetting in July, and that defaults are illustrative. No contradiction with annotations.

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 with two paragraphs, front-loading key information. Every sentence adds value, with 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 is comprehensive for the tool's behavior and inputs, but lacks explicit mention of return value structure. Given no output schema, a brief note on output format would improve completeness.

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 parameter descriptions add meaningful context (e.g., gross description clarifies contribution base vs gross; city description explains floor/ceiling). Adds value beyond the schema alone.

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 computes China social insurance and housing fund for employee deduction and employer cost, with correct contribution-base floor/ceiling. It specifies the verb 'computes' and resource, and distinguishes from general calculators by highlighting the floor/ceiling application.

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 explains what the tool does and its advantage over general calculators, but does not explicitly state when not to use or mention alternative sibling tools. The usage context is clear from the description.

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?

Annotations declare readOnlyHint=true. The description adds that it sanity-checks assumptions and splits outcome into contributions vs earnings, providing useful behavioral context.

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 with no wasted words. The first sentence is slightly vague but overall concise and front-loaded.

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?

No output schema, but description explains output splits contributions vs earnings and sanity-checks. However, the nature of sanity checks is unspecified, leaving some 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 100%, so the schema already documents all parameters. The tool description does not add significant new meaning 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 states it projects future value of a lump sum plus monthly contributions, splitting contributions vs earnings. It distinguishes from siblings like simple_interest and savings_goal.

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 use for computing growth with monthly contributions, but does not explicitly guide when to use versus alternatives like sip or simple_interest.

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?

Annotations already indicate readOnlyHint=true, so the description builds on that by explaining it computes months and total interest, and highlights the minimum-payment trap and payoff acceleration. No contradictions. Adds meaningful behavioral context beyond annotations.

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 and front-loads the main purpose. While slightly poetic, it is efficient and every sentence adds value. No redundant 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?

Given the simple input schema (3 numeric parameters with defaults) and no output schema, the description adequately covers the tool's purpose and outputs (months, total interest, payoff acceleration). It is complete for an agent to understand what the tool computes.

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% with brief descriptions for each parameter (APR, balance, payment). The description does not add significant detail about parameter constraints or formatting beyond what the schema provides. Baseline 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 states the verb (calculate) and resource (credit card payoff), specifying the outputs: months and total interest. It distinguishes from siblings by focusing on credit card minimum-payment trap and payoff acceleration, which is unique among the sibling financial calculators.

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 use for credit card payoff analysis and comparing payment amounts, but does not explicitly state when to use versus alternatives like loan_payment or simple_interest. No exclusion criteria or prerequisites 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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable context: it uses ECB daily reference rate, is neutral mid-market, and reveals bank spreads. This goes beyond annotations by explaining the data source and additional output, with no contradictions.

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 extraneous information. It front-loads the core purpose and unique value proposition, making it easy for an agent to quickly understand the tool. Every sentence 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 the tool's simplicity (3 optional params, no output schema, strong annotations), the description is fairly complete. It explains the data source and unique output (spread), though it does not detail return format or default behaviors, which are in the schema. Overall adequate for the complexity 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% but parameter descriptions are minimal (just 'To', 'From', 'Amount'). The tool description does not add further meaning to parameters, such as format or expected values. Baseline 3 is appropriate as schema covers the existence of params but lacks depth.

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 converts between 31 currencies using ECB rates and adds the unique value of revealing bank spread. It uses a specific verb 'convert' and resource 'currencies', and distinguishes itself from sibling financial calculators by offering currency conversion with spread disclosure.

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 usage for currency conversion needing neutral mid-market rates and spread info, but does not explicitly state when to use or not use it, nor does it compare to alternatives among siblings (which are different types of tools). Some guidance is provided via the unique feature, but it lacks direct 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?

Annotations set readOnlyHint=true, which aligns with a calculation tool. The description adds behavioral context: it computes total interest and includes a warning about the flat-rate trap, enhancing transparency beyond annotations.

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 key actions and warnings. Every sentence adds value without 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?

Despite no output schema, the description covers expected outputs (EMI, total interest, warning). For a calculator tool with all parameters documented, it is fully 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 coverage is 100% with descriptions for all 3 parameters. The description reinforces the reducing-balance assumption and adds the 'flat-rate trap' concept, providing slightly more meaning than the schema alone.

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 calculates loan EMI, total interest, and warns about flat vs reducing rate differences. It distinctly separates from sibling tools like 'loan_payment' and 'mortgage' by focusing on EMI and the flat-rate trap.

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 specifies use for home, car, personal loans and mentions comparing offers. It lacks explicit alternatives or when-not-to-use, but the context of 'comparing offers' and the unique warning provide good guidance.

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 adds value beyond the readOnlyHint annotation by explaining the computation steps (target, projection). It does not contradict annotations and provides context about 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 a single, well-structured sentence that front-loads the key purpose ('The portfolio that makes work optional') followed by the computation details.

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 calculator tool with no output schema, the description adequately conveys the output (years to reach target). All parameters are schema-covered, and the description adds context about the financial independence concept.

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 description does not detail individual parameters, but the input schema already covers 100% of parameters with descriptions. Baseline 3 is appropriate as the schema does the heavy lifting.

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 computes a financial-independence target and projects years to reach it, using specific verbs like 'computes' and 'projects'. It distinguishes from sibling tools (e.g., savings_goal, cagr) which focus on different financial calculations.

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 usage for FIRE calculations but does not explicitly state when to use this tool versus alternatives like savings_goal or compound_growth. No exclusions or alternative tool names are mentioned.

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?

Annotations declare readOnlyHint=true, confirming it is a read-only calculation. The description adds behavioral context by explaining the backward calculation from target income and listing the factors considered (unbillable time, overhead, taxes). No contradictions or surprising behaviors are hidden.

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, front-loaded with the main outcome, and concise. Every sentence adds value without redundancy. It is well-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 tool's simplicity (no output schema, no nested objects, all parameters described), the description is complete. It explains the calculation logic and the factors involved, leaving no obvious gaps for an AI agent to misunderstand.

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 input schema already documents all parameters well. The tool description adds minimal extra meaning beyond paraphrasing the schema's parameter descriptions. It does not introduce new details about parameter usage or constraints.

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: calculating the hourly rate needed to achieve a target income after accounting for non-billable time, overhead, and taxes. It uses specific verbs and resources, and distinguishes itself from sibling financial calculators by focusing on freelancing.

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?

While the description does not explicitly state when not to use or list alternatives, it implies the tool is for freelancers setting rates. The context of sibling tools (various financial calculators) makes the usage scenario clear. However, explicit guidance would improve this dimension.

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 behavioral traits such as working in both directions (exclusive to inclusive and back) and using specific rate structures, which adds value beyond the readOnlyHint annotation.

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 with two sentences, front-loading the main purpose. However, it could be slightly more streamlined by avoiding repetition of rate values.

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 simplicity and lack of output schema, the description provides sufficient context for a calculator tool. It mentions key features but does not describe return values.

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?

While the input schema covers all parameters, the description adds meaning by explaining the 'rate' enum values correspond to different country slabs and that 'mode' represents directionality.

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 ('Add or extract') and resource ('GST'), and specifies the applicable countries and rate slabs, distinguishing it from sibling tools like 'vat'.

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 context on when to use this tool (GST calculations for India and specific countries) but does not explicitly state when not to use it or mention 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?

Annotations indicate readOnlyHint=true (safe calculation). The description adds context about the 28/36 rule and flagging disagreements, providing behavioral insight beyond annotations. No contradiction with annotations.

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 concise sentences with no wasted words. The first sentence immediately conveys the core purpose; the second adds essential detail about the method and output behavior. Well front-loaded.

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 calculation rule and inputs but omits details about the output format (e.g., exact value returned, whether it includes two limits). Since no output schema exists, the description should ideally explain what the tool returns. It is moderately complete but lacks output specification.

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% with descriptions for all 5 parameters. The tool description does not add new parameter-level details but contextualizes them as inputs to the 28/36 rule. Baseline 3 is appropriate since the schema is already informative.

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 calculates 'how much house you can afford' using the 28/36 rule, specifying inputs like income, debts, down payment, and rate. It distinguishes from sibling tools (e.g., mortgage calculator) by focusing on affordability based on lender underwriting rules.

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 explains the tool uses the 28/36 debt-to-income rule lenders underwrite with, implying when to use for realistic affordability. It also mentions flagging when the two limits disagree. However, it does not explicitly state when not to use or mention alternatives like the mortgage calculator for payment-only queries.

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?

Annotations declare readOnlyHint=true, and description adds that it uses current official progressive brackets and returns multiple metrics. No contradictions; it provides sufficient behavioral context beyond annotations.

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?

Description is concise: one sentence for core functionality plus a list of countries. Front-loaded with key purpose, no fluff, though the country list could be omitted or summarized.

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?

Provides output metrics (tax owed, effective rate, marginal rate, take-home pay) despite lack of output schema. Mentions current brackets, but lacks details on handling edge cases like non-residents or multiple deductions. Adequate for typical use.

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?

Input schema coverage is 100% with descriptions for each parameter, including the enum values for country. The description lists countries redundantly but adds no new meaning beyond schema. Baseline 3 applies.

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 calculates personal income tax for 24 countries, listing specific output metrics (tax owed, effective rate, marginal rate, take-home pay) and enumerates all supported countries, distinguishing it from sibling tools like salary_to_hourly or loan calculators.

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 explicit guidance on when to use this tool versus alternatives. The description implies usage for income tax calculations, but does not mention when not to use or how it differs from other tax-related tools among siblings.

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?

Annotations already declare readOnlyHint=true, so the description's job is to add context. It does so by detailing the specific calculations: monthly payment, total cost, interest share, and extra payment savings. No contradiction with annotations.

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, 35 words, no filler. Every clause adds value: first sentence lists outputs, second explains the tool's scope. Excellent 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?

With 3 parameters (all with defaults), no output schema, the description covers both inputs and outputs: it names the key outputs (monthly payment, total cost, interest share, extra payment savings). Siblings are numerous but the description is distinct enough.

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%, with each parameter already described. The description adds no additional parameter-specific meaning beyond the schema. Baseline 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 states the tool computes monthly payment, total interest, and extra payment savings. It distinguishes itself from sibling tools like mortgage and simple_interest by mentioning the extra-payment scenario. The verb 'computes' is implied, and the resource 'loan payment' is explicit.

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 use for standard amortized loan math with optional extra payments, but does not explicitly state when to use this tool over siblings like mortgage, car_loan, or emi. No exclusions or alternative recommendations 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?

Annotations already declare readOnlyHint=true, so the tool is safe. The description adds behavioral context: it is a bottom-up calculation that includes a plausibility check, which is useful beyond the annotation. No destructive behavior is disclosed, but none is needed.

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, front-loading the purpose. Every sentence adds value: first states what it does, second highlights the unique plausibility check. 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?

Given the absence of an output schema, the description explains the tool's logic and the plausibility check well. However, it could mention the output format (e.g., returns TAM/SAM/SOM values or a verification). Overall, it is sufficient for a simple calculator 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 description coverage is 100%, so the baseline is 3. The tool description does not add additional meaning to the parameters beyond what the schema already provides (e.g., arpa, accounts). The description focuses on the overall method rather than 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 states it performs bottom-up market sizing (TAM, SAM, SOM) with a built-in plausibility check, using specific verbs and resources. It distinguishes itself from sibling tools, which are all other financial calculators, none of which focus on market sizing.

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 usage for market sizing with a sanity check (''the check most pitch decks skip''), but does not explicitly state when not to use or provide alternatives among siblings. However, the context makes it clear that this is the only tool for market sizing.

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 explains that PMI is included when down payment under 20%, which is a notable behavioral detail. It does not contradict the readOnlyHint annotation. The behavioral traits are well disclosed for a read-only 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 moderately sized (4 sentences) and front-loaded with the core purpose. Each sentence adds distinct value: first sentence states the full cost, second explains the calculation, third adds PMI detail. Could trim redundancy but is well-structured overall.

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 is complete for a read-only calculation tool: it explains what is computed and the key inputs. However, since there is no output schema, the description does not specify the return value format (e.g., a numeric monthly payment) or any additional outputs like an amortization schedule. This omission is acceptable but prevents a higher score.

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?

Input schema covers all 6 parameters with descriptions and defaults (100% coverage). The description adds meaning by explaining how each parameter (price, down payment, rate, term, property tax, insurance) contributes to the full PITI calculation. This contextualizes the parameters beyond the 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 computes the full PITI monthly mortgage cost, including principal, interest, taxes, insurance, and PMI. It distinguishes from simpler loan calculators by emphasizing it includes costs often omitted. The verb 'monthly mortgage payment' and resource 'price, down payment, rate, term' are specific.

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 when to use this tool: when you need the full monthly cost including taxes, insurance, and PMI, as opposed to a simple loan payment. It contrasts with 'lender ads' that omit these costs, but does not explicitly name sibling tools like 'loan_payment' as alternatives. The context is clear but could be more directive.

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?

Annotations already provide readOnlyHint=true, indicating it's a safe computation. The description adds that 'IRR pitfalls are flagged instead of hidden', which gives behavioral detail beyond annotations. However, no specifics on error handling or output format.

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?

One sentence that is engaging and informative. No wasted words. Front-loaded with the core purpose.

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 no output schema, the description does not explain return values, but the tool name and inputs imply the outputs. It covers the key financial metrics. Could be more complete by mentioning that multiple measures are returned.

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 already provides 100% coverage with clear descriptions for rate and cashflows. The description does not add new parameter-level information; the schema is sufficient for parameter understanding.

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 computes NPV, IRR, payback period, and profitability index for cash flows, distinguishing it from sibling tools like roi, break_even, cagr. The phrase 'discounted, not vibes' reinforces the specific capital-budgeting context.

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 it is for investment evaluation using discounted cash flows, and mentions 'standard capital-budgeting toolkit'. It does not explicitly state when not to use, but sibling tools provide context for alternatives. Could mention if not suitable for simple ROI or break-even.

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?

Annotations already provide readOnlyHint=true, so no destructive behavior. The description adds context on using take-home pay and handling recurring costs, which aids understanding but does not disclose any hidden behaviors. No contradiction with annotations.

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 somewhat lengthy but each sentence adds value. It front-loads the core concept ('converts any price into hours') and then elaborates on recurring costs and investment alternatives. Could be trimmed slightly but remains informative.

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 no output schema, the description does not explain return format, but it provides a thorough conceptual overview. For a calculator tool, the description adequately prepares the agent for what the tool does, though output details are missing.

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 descriptions cover all 5 parameters (100%), so baseline is 3. The description adds value by explaining the rationale behind converting gross pay to net pay and why takeHomePct is important, going beyond schema syntax to convey 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 tool converts prices into hours and workdays of personal labor using take-home pay. It specifies both one-time and recurring costs, and distinguishes itself from sibling financial tools by focusing on labor time rather than just monetary conversion.

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 usage for personal finance decisions but does not explicitly state when to use this tool versus alternatives. It lacks when-not-to-use guidance and does not reference sibling tools, which limits clarity for an AI agent deciding between similar calculators.

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?

Annotations already declare readOnlyHint=true, so the tool is safe to call. The description adds value by clarifying that it 'computes' and 'shows' results, and provides educational context about the math behind discounts. No contradiction with annotations.

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, no fluff. The description is front-loaded with the key purpose, then expands with value proposition. Every sentence contributes essential 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?

For a calculator with three optional parameters and no output schema, the description sufficiently explains the computed outputs (gross margin, markup, discount volume math) and the educational intent. No gaps in understanding how to use 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 covers 100% of parameters with descriptions. The description enhances meaning by linking parameters to the margin vs markup confusion and discount volume analysis, providing conceptual context 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?

The description clearly states it computes gross margin and markup from cost and price, and shows the volume impact of discounting. It distinguishes itself by addressing the common confusion between margin and markup, and by emphasizing the 'brutal volume math' behind discounts, which is not covered by sibling tools.

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 usage when dealing with margin/markup confusion or discount analysis, but does not explicitly state when to use this tool versus alternatives. Given the large set of sibling financial calculators, stating conditions or exclusions would improve selection accuracy.

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?

Annotations already indicate readOnlyHint=true, so the description adds context about what the tool calculates but no new behavioral traits beyond that. No contradictions.

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 verbose with multiple sentences; while informative, it could be more concise without losing meaning. Maintains front-loading of purpose.

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 should clarify return format. It hints at two outputs (months and a cross-over boolean) but does not specify structure, leaving room for ambiguity.

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 description adds narrative context (e.g., highlighting health insurance in extraCosts) but does not provide significant new meaning beyond what the parameter descriptions already convey.

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 calculates how long savings last after quitting a job, answering two specific questions. It distinguishes itself as the personal version of a startup runway, differentiating from the sibling 'runway' 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?

It implies usage for personal financial planning vs business runway, but does not explicitly state when to use this tool over alternatives like 'runway' or when not to use it.

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?

Annotations already indicate readOnlyHint=true (safe read). The description adds context that the tool provides both simple and annualized ROI, and explains why annualization matters. No behavioral surprises beyond what is stated.

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, efficiently communicating purpose and key features without waste. It is front-loaded with the core action.

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 details about the return format or output structure, which is notable given the absence of an output schema. However, it adequately covers the tool's purpose and differentiation among many 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?

Schema coverage is 100% with clear descriptions for all three parameters (cost, years, finalValue). The description adds conceptual meaning but does not enhance parameter understanding beyond what the schema already 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 states it computes simple and annualized ROI from cost and final value, with an optional time period. It distinguishes itself from sibling financial calculators by focusing on comparability across time periods.

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 usage for investment performance evaluation through the 'comparable numbers' phrasing, but it does not explicitly state when to use this tool versus alternatives like CAGR or simple interest. The guidance is clear for basic ROI calculation.

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?

Annotations declare readOnlyHint=true, which aligns with the description (computation only). The description adds value by explaining that results are interpreted against fundraising norms (3-6 month raise time, 18-24 month post-raise horizon) and supports burn trending. No contradictions with annotations.

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 that front-load the main question and efficiently explain the computation and its practical interpretation. No redundant information; every clause 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?

For a simple calculator with 3 fully-described parameters and no output schema, the description adequately covers what the tool does and how to interpret results. It lacks explicit output format details but the intent is clear. Completeness is slightly above minimal.

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% with clear parameter descriptions. The description adds context by mentioning trend direction (up/down) related to burnChange, but does not significantly augment what the schema already provides. 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 states it computes cash runway (months of cash) and provides fundraising timing context. It distinguishes from sibling tools like 'break_even' or 'fire_number' by focusing specifically on runway and fundraising implications.

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 usage when needing to know cash duration and fundraising timing. While it doesn't explicitly exclude alternatives, the context is clear for startups tracking runway. No explicit when-not-to-use or alternative tool names are given, but the purpose is well-defined.

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?

Annotations already declare readOnlyHint=true, and the description adds behavioral context by stating it computes contributions and shows the cost of waiting, which is consistent and informative.

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 concise sentences, front-loaded with the purpose, no wasted 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 good parameter descriptions and no output schema, the description adequately covers the tool's function and key output (cost of waiting), though it lacks mention of limitations or edge cases.

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 does not add extra meaning beyond summarizing the inputs already well-documented in 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?

The description clearly states it computes the monthly saving needed to hit a target by a deadline, and distinguishes itself from sibling financial tools by specifying inputs and the unique 'cost of waiting' output.

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 usage for savings goal planning but does not provide explicit when-to-use or when-not-to-use guidance relative to siblings like compound_growth or simple_interest.

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?

Annotations mark as readOnlyHint=true (non-destructive). Description adds behavioral traits: 'honestly assumed' return rate, splits invested amount vs gains. No contradictions, and adds value beyond annotations.

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 effectively convey purpose and key features. Every sentence adds value; 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?

No output schema, but description explains output includes corpus breakdown. Parameter descriptions are detailed. For a calculator tool, this provides sufficient context for an AI agent to use 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?

All 4 parameters have schema descriptions (100% coverage). Description mentions step-up but does not add significant meaning beyond what is already in the schema. Baseline score 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?

Clearly states it calculates SIP returns, includes step-up, and splits invested amount from gains. Distinguishes from sibling financial tools by specifying it's for systematic investment plans with optional step-up.

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?

Indicates usage for projecting monthly SIP with optional step-up, implying when realistic salary growth assumptions are needed. Does not explicitly state when not to use or compare with alternatives like compound_growth or savings_goal, 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.

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

The description adds rounding behavior and shows remainder, but annotations already indicate readOnlyHint: true. The description does not contradict annotations.

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 no wasted words. Key information is front-loaded.

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 3-parameter tool with no output schema, the description sufficiently describes output (rounded amount plus remainder). Complete enough for agent invocation.

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% with descriptions for all parameters. The description adds context about rounding and optional tip, enhancing understanding beyond 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?

The description clearly states the tool's purpose: splitting a bill evenly with tip and rounding, and it distinguishes itself from sibling financial calculators.

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 explicit when-to-use or alternatives are provided. The context implies use for bill splitting, but no guidance on when not to use or comparison with sibling tool 'tip'.

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?

Annotations indicate readOnlyHint=true, and the description reinforces that it is a calculator ('as a calculator'), so no contradictions. The description adds behavioral context (e.g., 'safe monthly salary', 'actual swings require') beyond the annotations, explaining what the tool computes.

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 concise three-sentence paragraph with no wasted words. It front-loads the core idea and efficiently conveys key details without 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?

Given the tool's simplicity (3 parameters, no output schema), the description adequately explains what the agent can expect (salary, buffer, spike months). It is complete for a calculator tool, though it could mention edge cases (e.g., insufficient months).

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 schema already documents all three parameters well. The description adds minor contextual phrases (e.g., 'What's in the account that smooths the gaps' for buffer) but does not significantly expand on the schema's 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 states the tool's purpose: converting variable income into a stable salary and buffer. It names specific target users (freelancers, tipped workers, etc.) and outputs (salary, buffer, spike months), distinguishing it from sibling financial calculators.

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 on when to use the tool ('For freelancers, tipped workers, sellers, and seasonal earners') and what inputs to provide. It does not explicitly exclude alternatives or compare to siblings, but the context is sufficient for an AI agent to determine applicability.

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?

Annotations declare readOnlyHint=true and openWorldHint=false. The description adds behavioral context beyond annotations: it explains the calculation accounts for unpaid waiting and driving time, full per-mile costs, and self-employment tax, and honestly compares to minimum wage. No contradictions.

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?

Description is a single, tight paragraph of about 80 words. It front-loads the core purpose and then explains components in a logical flow. Every sentence adds value without 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?

The tool has 6 parameters and no output schema. The description explains the concepts but does not specify the output format (e.g., a number, comparison statement). It mentions comparison to minimum wage but lacks details on what the user can expect as a result. This is a gap given the complexity.

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 baseline is 3. Description adds context by mentioning 'deadhead' time and vehicle costs but does not elaborate on each parameter individually. The schema's parameter descriptions already provide meaningful detail (e.g., taxPct mentions self-employment and income tax brackets). Overall, description adds moderate value beyond 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?

The description clearly states the tool computes the true hourly wage for gig work after vehicle costs, waiting time, and self-employment tax. It distinguishes from sibling tools like salary_to_hourly and freelance_rate by focusing on the net hourly earnings from gigs, including wait time and mileage costs.

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 when to use: to evaluate gig or side-hustle earnings relative to minimum wage, with the nuance of being 'honest when the answer is stay home'. It does not explicitly state alternatives or when not to use, but the context and sibling tools make usage boundaries 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?

Annotations already declare readOnlyHint=true, so description adds value by detailing the computation logic and benchmarking. Does not introduce contradictions, but behavioral traits beyond read-only are minimally disclosed.

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?

Single, well-structured sentence that conveys purpose, inputs, and outputs without redundancy. Every part is necessary and front-loaded with the key result.

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?

No output schema exists, so description compensates by hinting at output (benchmark comparison). Could be more explicit about return format (e.g., ratios or status), but overall adequate for a calculation tool with complete parameter documentation.

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% with detailed parameter descriptions. The description adds meaning by explaining how parameters combine to compute LTV and compare to benchmarks, providing formula context beyond individual parameter 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?

Description clearly specifies the tool computes LTV, LTV:CAC, and CAC payback, and benchmarks against standard SaaS metrics. It uses specific verbs like 'computes', 'compares', and 'reads' and differentiates from siblings like break_even and roi by focusing on subscription unit economics.

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?

Implies use when analyzing subscription business unit economics. Does not explicitly state when not to use or name alternatives, but the context is sufficiently clear among financial siblings.

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?

Annotations declare readOnlyHint=true, so no side effects are expected. The description adds no behavioral traits beyond confirming it is a calculation tool. It does not disclose any behavioral nuances beyond what annotations already provide.

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 long, front-loaded with the core purpose, and contains no filler or redundant 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?

Given the tool's low complexity, complete schema coverage, and no output schema requirement, the description provides sufficient context for an agent to understand and use the tool 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 description coverage is 100%, so each parameter already has a description. The tool description does not add new information about parameters beyond the schema; it only provides a general context.

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 compares two package options by price per unit and quantifies savings. It uses specific verbs ('compares', 'quantifies') and identifies the resource ('two package options'), distinguishing it from sibling financial tools.

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 suggests usage context (supermarket shelf math) but does not explicitly state when not to use or list alternatives. However, the purpose is clear enough for an agent to infer appropriate use.

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?

Annotations already declare readOnlyHint=true, so the description only needs to add context beyond that. It does warn about the divide-not-subtract trap, which is useful. However, it does not disclose other behaviors like the default values or numeric bounds, which are already in the schema. No contradictions with annotations.

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, front-loaded with the key action and a notable warning. Every sentence serves a purpose: first explains the tool and highlights a common mistake, second clarifies the math. No 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 calculator with 3 parameters, the description covers the main use case and a common pitfall. However, it does not explain the return value (expected output), and with no output schema, this gap reduces completeness. Sibling tools are similar, so minimal extra guidance is needed.

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% with detailed descriptions for each parameter. The description adds further context with country-specific rate examples and the extraction direction nuance, but the baseline is 3 due to high schema coverage. Additional value is marginal.

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 adds or removes VAT at any rate, distinguishing it from sibling calculators like gst. It uses specific verb-resource pairing ('Add or remove VAT') and explains the mathematical operation, making the purpose 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 implies usage for VAT calculations (add/remove) and warns about a common error, but it does not explicitly guide when to choose this tool over siblings like gst or other financial calculators. No 'use this when' or 'avoid for' statements are provided.

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