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

The description discloses useful calculation behavior: prompt caching up to 90%, batch discounts, and cost comparison across named models. However, with no annotations, it carries the full burden and does not state whether it is purely read-only, what output shape it returns, or what pricing data and assumptions it relies on.

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 dense sentence, front-loaded with the action and object, and every phrase adds specific detail such as discount rates and model coverage without repetition or filler.

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

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

For a four-parameter calculator with no required inputs and a fully described schema, the description is nearly complete: it specifies the computation domain, applicable discounts, and model coverage. The only notable gap is the absence of an explicit output description, but the phrase 'cost disparity' conveys the comparison intent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes all four parameters, so the baseline is 3. The description adds value by tying parameters to caching discounts and batch discounts, and by naming the model set the cost disparity calculation covers, which contextualizes promptTokens, completionTokens, cacheHitRatio, and isBatch.

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 opens with the verb 'Calculate' and a specific resource: multi-model LLM API token inference costs. It further enumerates caching discounts, batch discounts, and the exact model set, making it clearly distinguishable from siblings like cloud_egress_finops.

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 guidance is given for when to prefer this tool over alternatives, and no prerequisites or when-not-to-use conditions are stated. The intended use case of comparing LLM token costs across models is only inferred from the name and terms like 'arbitrage'; there is no explicit trigger or context.

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

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

With no annotations, the description carries the full burden. It does convey that this is a compute-only analysis rather than a mutating action, and it names the 183-day PE trigger. However, it does not disclose assumptions, whether the output is a single number or a report, or how the gross-up is rounded/applied.

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 dense, well-structured sentence with no filler. The core action is front-loaded, and treaty/PE details follow naturally without wordiness.

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 computation scope and parameter roles are well covered, especially with fully described schema fields. However, there is no output schema and the description does not state what the tool returns, nor does it provide explicit routing guidance versus sibling tax/finance tools. Adequate but with clear 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 coverage is 100%, so the baseline is 3, but the description adds conceptual meaning: invoiceNetRequired relates to gross-up, statutoryRatePercent and treatyRatePercent map to the statutory vs treaty comparison, and daysInCountry connects to the 183-day PE trigger. It frames the parameters without repeating their schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Compute') and a distinct resource: cross-border B2B software/consulting invoice gross-up, statutory vs DTAA treaty WHT rates, W-8BEN forms, and 183-day PE triggers. This clearly separates it from sibling tools like fx_invoicing or indian_income_tax.

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 cross-border B2B withholding/gross-up analysis, but it never explicitly states when to prefer it over related siblings or when not to use it. Context is present, but exclusions and alternative routing are missing.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists three outputs but does not reveal the support conditions assumed (e.g., simply supported, cantilever), which materially affect results. It also does not mention unit conversions or constraints such as positive load, leaving the agent to infer behavior from the raw formula.

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?

A single, front-loaded sentence with no filler. It names the outputs and the theory in an efficient, scannable way.

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 and no annotations, the definition is incomplete for a tool with four required parameters and three outputs. It fails to specify support conditions, output units, or formula assumptions, leaving notable ambiguity for an agent deciding how to interpret and verify results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter with units. The description adds only the Euler-Bernoulli context, which implies linear elastic behavior but does not explain how the parameters combine or what the outputs will be. Baseline 3 is appropriate because the description neither compensates nor detracts from the already-complete 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 states a specific verb ('Calculate') and a specific resource ('structural engineering beam deflection, max bending moment, and peak stress'), and further identifies the theory ('Euler-Bernoulli beam'). This clearly distinguishes it from sibling tools like projectile_motion or rlc_circuit, which address unrelated domains.

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 guidance is given about when to use this tool versus alternatives, nor are prerequisites or boundary conditions stated. The description only implies use when beam deflection/moment/stress calculations are needed, but offers no explicit when-to-use or when-not-to-use context.

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

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

With no annotations, the description must fully disclose behavior, but it leaves key assumptions unexplained, such as how SECA taxes and filingStatus are applied and what exactly 'non-billable buffer drag' means. More importantly, it claims 47 working weeks while the schema's vacationWeeks parameter defaults to 4, implying 48 weeks if the year has 52, so the description conflicts with the schema's own data.

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 compact sentence leads with the tool's output before listing the relevant factors, with no filler. It could be structured into multiple sentences for scannability, but it remains appropriately sized.

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?

Because there is no output schema and no annotations, the description should clarify what the returned value represents, its units, and the calculation's assumptions. It does state the result is a rate, but the 47-week inconsistency, omitted filingStatus role, and lack of caveats leave the description incomplete for safe 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 description coverage is 83%, so the schema already documents most parameter meanings. The description adds high-level mapping of business expenses, health insurance, and non-billable time, but it adds no specific detail about filingStatus, which remains undocumented in both the schema and the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific computation—solving for the minimum billable hourly rate needed to reach a target spendable cash income—and lists the main cost and tax factors. It clearly distinguishes the tool from generic siblings by its unique output, though it never names or differentiates a specific alternative 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?

No guidance is given for when to use billable_floor versus closely related siblings such as contractor_parity, scorp_optimizer, or solo_401k_shield. There are no stated prerequisites, exclusions, or conditions; the intended usage is only implicit from the calculator-style name and 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?

No annotations are provided, so the description carries the full behavioral burden. It clearly discloses that the tool computes both prices and Greeks and specifies the model and option type. It does not enumerate Black-Scholes assumptions like no dividends or lognormal returns, but the core computational behavior is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that wastes no words and front-loads the primary action. It quickly communicates the resource, the specific outputs, and the model, making it easy for an agent to parse.

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 input schema fully documents all parameters, and the description names both the output categories and the mathematical model. There is no output schema, and the description does not specify the return structure, but for a straightforward computational tool this is a minor gap rather than a blocking omission.

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 all five parameters are already documented with descriptions and defaults. The tool description adds no additional parameter-level meaning, so it stays at the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: it computes European option prices and Greeks via the Black-Scholes model. It also distinguishes itself from the sibling calculators by naming the unique outputs (Call/Put prices, Delta, Gamma, Vega, Theta) and the model used.

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 pricing European options and computing Greeks, which is enough to orient an agent. However, it does not explicitly state when to use this tool versus alternatives or mention any exclusions, such as not supporting American options or exotic derivatives.

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

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

With no annotations provided, the description carries the transparency burden. It explicitly discloses the computation's full output scope, which is the core behavioral trait for a pure calculation tool. It does not discuss edge cases like non-positive contribution margin, but for a read-only calculator this is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, tightly worded sentence front-loads the primary purpose and then lists all delivered metrics. There is no filler or 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 there is no output schema and no annotations, the description compensates well by listing the main results. It is slightly incomplete regarding input validity assumptions or behavior when unitPrice is less than or equal to unitVariableCost, but it is sufficient 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?

Schema description coverage is 100%, so the parameters are already fully documented in the input schema. The tool description adds no parameter-specific meaning beyond the output names, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Calculate') plus a clear resource ('business break-even threshold') and enumerates distinct outputs (units, revenue, contribution margin ratio, margin of safety, operating leverage degree). This clearly differentiates it from sibling financial calculators like npv_irr or black_scholes.

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 clearly implies when to use the tool: whenever break-even, contribution margin, margin of safety, or operating leverage analysis is needed. It does not explicitly list exclusions or alternatives, but the specialized output set makes the context obvious among the sibling tools.

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

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

With no annotations provided, the description carries the full disclosure burden. It does reveal that the tool calculates three distinct financial quantities and references the Fisher effect, which is useful, but it does not specify return format, whether all results are returned together, or assumptions such as constant annual growth or compounding frequency.

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, front-loaded sentence that lists the tool's core outputs without filler. Every phrase adds information, and the structure makes the tool's purpose immediately readable.

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

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

There is no output schema, so the description needs to convey sufficient context for an agent to know what results to expect. It names the outputs but does not explain the return structure, calculation assumptions, edge cases, or how the optional inflation parameter affects the real purchasing-power result, leaving meaningful gaps for a numeric calculator.

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 parameters themselves are already documented with defaults and units. The description adds no additional parameter-level meaning or relationships among initialValue, finalValue, periodsYears, and inflationRatePercent, so the baseline score 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 names a clear verb, 'Calculate,' and specific financial outputs: CAGR, inflation-adjusted purchasing power via the Fisher effect, and investment doubling horizon. It is more specific than a generic growth tool and should help an agent distinguish it from broad siblings like compound_wealth, though it bundles three outputs rather than one focused resource.

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 states what the tool computes but gives no guidance on when to choose it over siblings such as compound_wealth, sip_investment, or npv_irr. There are no explicit conditions, exclusions, or alternative routing hints, so the agent must infer usage from the metric names alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention how the mode is selected via the 'type' parameter, what output shape is returned, or how edge cases like negative discriminants or singular systems are handled.

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 one sentence with no filler, front-loading the core purpose. The generic opening phrase 'Scientific solver' adds little value, but the rest is tightly packed.

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?

Without an output schema and with 7 parameters, the description is under-specified. It does not explain how to invoke the simultaneous mode or what result to expect, so an agent is unlikely to call the tool correctly for the non-default path.

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 0%, so the description must compensate. The quadratic formula defines a, b, and c as coefficients, but a2, b2, c2, and the 'type' parameter are never explained, leaving the simultaneous-equation mode ambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names specific mathematical operations: solving a quadratic equation ax^2 + bx + c = 0 and 2-unknown simultaneous linear equations. This is a clear resource-and-operation mapping, though it does not explicitly contrast the tool with any sibling 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?

Usage is implied by the mathematical formulas: an agent can infer that this tool is for quadratic roots or simultaneous linear equations. However, there is no explicit guidance about when to choose this tool over alternatives, nor any exclusions or prerequisites.

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

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

No annotations are provided, so the description carries the full burden. It transparently states the tool analyzes and calculates savings, which suggests a read-only estimation or calculator behavior. However, it does not disclose methodology, assumptions, or output format, leaving some ambiguity about exactly what results will be returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, information-dense sentence with no filler or repetition. It front-loads the core purpose and communicates the comparison and output calculation efficiently. It is slightly long but every phrase 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?

For a tool with only two well-documented parameters and no nested objects, this description is reasonably complete. It explains the purpose, the comparison scenario, and the expected output (monthly and annual cost savings). It does not detail formula specifics or output precision, but those are not required given the tool's moderate 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?

The input schema already provides complete descriptions for both parameters, including defaults and ranges, so the schema coverage is 100%. The description adds contextual framing around cloud egress and edge caching but does not materially extend the meaning of cacheHitRatio or monthlyEgressGB 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 identifies the tool's action ('Analyze'), the resource ('tiered public cloud data transfer egress fees'), and the comparison scenario ('vs Cloudflare Zero-Egress Bandwidth Alliance and edge caching proxy'). It also states the computed outcome: monthly and annual infrastructure cost savings. This is specific enough to distinguish it from siblings like fx_invoicing or black_scholes.

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 analyzing public cloud egress costs against Cloudflare alternatives, so an agent can infer when to use it. However, it does not explicitly say when not to use it or mention any alternative tools. The usage context is present but relies on inference rather than direct 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?

With no annotations, the description must carry the behavioral burden. 'Simulate' signals a calculation rather than a mutation, but the description does not state whether the result is a final balance, a schedule, or a projection, nor which assumptions (taxes, fees, inflation) are ignored.

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?

A single front-loaded sentence; the key verb and domain appear first, followed by the specific eligible plans. There is no filler, repetition, or unnecessary detail.

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 the description is nearly sufficient, but with no output schema and no annotations, it leaves the return format and modeling assumptions unstated. The schema covers the inputs well, so the main missing piece is what the tool returns when invoked.

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 already covers all five parameters with descriptions and defaults, so the baseline is 3. The description adds account-scope context but no extra information about how annualRatePercent, monthlyDeposit, or compoundFrequency interact.

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 uses the specific verb 'Simulate' with a concrete object ('exponential compounding wealth') and narrows the domain to named account types (401(k), Roth IRA, UK ISA, European ETF savings plans). This makes it plainly distinct from the sibling calculators even though it does not name them.

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 account-type list gives a clear trigger context: retirement or tax-advantaged savings plans in the US, UK, or EU. It stops short of explicit alternatives or exclusion statements, e.g., it never mentions that sip_investment may be a better fit for Indian SIP-style contributions.

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

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

With no annotations provided, the description carries the behavioral transparency burden. It clearly states that this is a calculation tool producing tax, benefits, net cash, and breakeven rate outputs. However, it does not disclose key behavioral details such as reliance on default assumptions, tax jurisdiction applicability, or the fact that international FX drag and payment rails are part of the model. No contradiction with annotations exists.

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 dense sentence with no filler or redundancy. It front-loads the action ('Calculate'), specifies the comparison subjects, and ends with the key output. Every word contributes to the agent's 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?

For a 13-parameter tool with no output schema and no annotations, the description conveys the core purpose but leaves gaps: it does not explain the return format, the role of the many optional parameters, or the assumptions embedded in the calculation. It is adequate but not rich enough for a fully informed 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 description coverage is 92%, so the schema already documents almost every parameter. The description adds useful context around the contractorHourlyRate as the 'breakeven billing rate' but does not provide meaning beyond the schema's existing parameter 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 names a specific verb ('Calculate'), a clear domain ('tax, benefits parity, net spendable cash between W-2 salaried employment and 1099 independent contractor billing'), and a concrete deliverable ('exact breakeven billing rate ($/hr)'). This clearly distinguishes it from siblings like scorp_optimizer or indian_income_tax.

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 strongly implies the intended use case: comparing W-2 vs. 1099 compensation and finding the breakeven rate. However, it does not explicitly state when to use this tool versus any alternative, nor does it mention any exclusions or scenarios where another sibling tool would be more appropriate.

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

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

With no annotations, the description carries the behavioral disclosure burden. It says the tool evaluates physical presence days, exclusion limits, and domicile audit risks, but it does not state whether the tool returns a calculation, a risk score, a flag, or a report, nor does it mention any caveats.

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 dense sentence with no filler; every clause contributes a distinct element: physical presence test, exclusion limit, and state audit risk. It is front-loaded with the core topic and remains highly scannable.

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 domain, key thresholds, and relevant states, and the schema documents all parameters. However, with no output schema and no annotations, the absence of any statement about what the tool returns or how results are presented leaves a meaningful gap for an agent relying on the description alone.

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 adds meaningful semantic context beyond the schema by linking the 330-day threshold to daysOutsideUSInRollingPeriod, the $130k limit to foreignEarnedIncome, and CA/NY/VA/SC to stateDomicile.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Track'), a specific IRS form (2555), and concrete thresholds (330 days, $130k, high-risk states), making the tool's purpose unmistakable. It clearly distinguishes this from sibling tax tools by focusing on FEIE physical presence and domicile audit risk.

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 establishes clear context: use this for IRS Form 2555 FEIE tracking, physical presence testing, and state domicile audit risk. It does not explicitly name alternatives or say when not to use this tool, so it stops short of full 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It communicates that the tool compares providers and benchmarks, but does not state what the tool returns, assumptions, data freshness, or whether results are estimates. The verb 'Deconstruct' is metaphorical and does not clarify 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?

One sentence, dense with relevant detail including the provider list and benchmark comparison. It is front-loaded with the action and resource, and contains no filler. The phrasing is slightly jargon-heavy but still 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?

With no output schema or annotations, the description should explain expected output and use context, but it does not. It tells the agent the comparison dimensions but not what a call returns or any caveats, leaving significant gaps for a two-parameter calculation 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 input schema already documents invoiceUsd and targetCurrency with defaults and enum values. The description adds little beyond the schema—'local currency' maps to targetCurrency, 'invoice' maps to invoiceUsd—so 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?

Description names a specific resource—cross-border contractor invoicing fee drag—and lists concrete providers compared (Wise, Deel, Stripe, etc.). It clearly identifies the tool's function, though it does not explicitly distinguish it from sibling tools like contractor_parity.

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 guidance on when to use this tool versus alternatives; it never mentions sibling tools or conditions that select this over others. The 'deconstruct/compare' phrasing implies use for fee comparison, but that is left to inference.

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

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It clearly states it computes EMI, total interest, and amortization schedule, which implies a read-only calculation with no side effects. However, it does not disclose assumptions like monthly compounding frequency, rounding behavior, or handling of zero/negative values. For a pure computation tool, this is adequate but not exhaustive.

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?

A single, concise sentence that front-loads the primary action (Compute) and immediately states the outputs (EMI, total interest, amortization schedule). Every word earns its place; there is no fluff or 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 tool with no output schema, the description adequately communicates the return values: EMI, total interest, and amortization schedule. However, it could specify the exact format (e.g., monthly schedule rows, currency, rounding) and any key assumptions (e.g., annual compounding). Despite these minor gaps, it is sufficiently complete for an agent to use the tool correctly for typical home loan calculations.

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 all parameters (principal, tenureYears, interestRatePercent) are documented in the schema. The description adds context about the reducing balance method but does not provide additional parameter-level details beyond the schema. Baseline 3 is appropriate because the schema handles the parameter documentation.

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 'Compute' and the specific resource: reducing balance monthly loan EMI, total interest, and amortization schedule. It distinguishes this tool from siblings like mortgage_piti by specifying the reducing balance method and home loan context. An agent can immediately identify the tool's purpose without ambiguity.

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 home loan EMI calculations but does not explicitly contrast with alternatives like mortgage_piti or sip_investment. There are no exclusions or conditions for when to choose this tool over siblings. Usage is inferred from the name and description, but no explicit routing guidance is provided.

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

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

With no annotations, the description carries the behavioral burden, and it does name the main computation features (regimes, rebate, standard deduction). It does not disclose whether surcharge/cess is included, how isSalaried=false changes the calculation, or what the output contains, leaving notable gaps for an unannotated tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, information-dense sentence with the verb and key tax details front-loaded. There is no filler or redundant restatement of the tool name.

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 two-parameter calculator the description conveys the core purpose, but with no output schema or annotations it leaves the return shape, surcharge/cess treatment, and edge-case behavior undefined. It is adequate but not complete enough to fully predict the tool's behavior.

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; ctc and isSalaried are already described in the schema. The description adds some context by mentioning the ₹75,000 standard deduction, but it does not introduce meaningfully new parameter semantics.

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 uses a clear verb-resource pair: 'Compute Indian Income Tax' and narrows scope to Budget 2025-26 New Tax Regime vs Old Tax Regime, naming specific features (Section 87A rebate, ₹75,000 standard deduction). This is distinct from the sibling calculators, none of which address income tax.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: an agent can infer to use this when asked for Indian income tax under new vs old regime. However, the description does not explicitly state when to prefer it over alternatives or list exclusion cases (e.g., non-salary income, surcharges/cess).

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

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

No annotations are provided, so the description carries the full burden. It lists the outputs but does not disclose behavior such as whether edge cases (vertical lines, constant y-values, invalid or fewer than 2 points) produce errors, nulls, or approximations. An agent cannot predict failure modes from this description alone.

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-formed sentence that front-loads the core purpose and names all three outputs with no filler. Every word contributes to understanding the tool.

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 one fully documented parameter and clearly named outputs, this description is largely complete. It lacks explicit return structure and edge-case behavior, but the tool's simplicity and the schema coverage make the remaining gap minor.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents the single 'points' parameter completely, including that each point has x/y and that a minimum of 2 points is required. The description adds no extra parameter-level meaning, which is acceptable given 100% schema coverage, so the baseline score 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 uses a specific verb ('Calculate') and names three concrete outputs: OLS best-fit line, Pearson r, and R². This clearly distinguishes the tool from the arithmetic/domain calculators in the sibling list and tells an agent exactly what it computes.

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 intended usage is implied by the description and tool name: use it when you need ordinary least squares regression. However, it does not state when not to use it, mention any prerequisites, or reference alternative tools, though no direct regression sibling exists among the provided tools.

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

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

With no annotations, the description carries the full behavioral burden, but it only states what is calculated. It does not disclose that tenureYears/loanTermYears can change the amortization term, that PMI applies conditionally when down payment is below 20%, or other calculation assumptions. The claim of a '30-year' schedule also conflicts with the schema's variable loan term parameters.

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 no filler, and the core purpose is front-loaded. However, given the tool's parameter complexity, it is slightly too terse to serve as a complete standalone guide, keeping it from a perfect 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?

This is an 8-parameter calculation tool with no output schema and no annotations, but the description only provides a one-line summary. Missing details include variable term handling, conditional PMI behavior, tax/insurance calculation basis, and what the amortization schedule contains, making it insufficient for confident invocation in 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 schema already documents all parameter meanings. The description adds only a high-level PITI framing and output type, which maps loosely to parameters but does not clarify relationships like tenureYears vs loanTermYears or how taxes/insurance are applied.

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 action ('Calculate') and the resource ('US monthly mortgage payment (PITI)'), then names the concrete output ('30-year amortization schedule'). This distinguishes it from siblings like home_loan_emi by emphasizing the US/PITI scope.

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 gives useful domain context (US mortgage, PITI) that implies when it should be used, but it never explicitly states when to prefer this tool over alternatives or when not to use it. The intended usage is inferable, not spelled out.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It does disclose that the computation uses 'iterative Newton-Raphson polynomial convergence', which usefully signals an approximate numerical method. However, it does not mention potential convergence failures, multiple IRR scenarios, or how outputs are returned, so behavioral transparency is adequate but incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that front-loads the core purpose before adding method and context details. It is reasonably concise, though the phrase 'iterative Newton-Raphson polynomial convergence' is somewhat technical and could be trimmed without losing selection 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?

There is no output schema and no annotations, so the description should ideally clarify what the tool returns and any edge-case behavior. It explains the calculation approach and use cases, but does not specify whether the tool returns both NPV and IRR, in what form, or how non-convergence is handled. This is a moderate gap for an unannotated 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 input schema already documents all three parameters clearly. The description adds no additional parameter-level meaning beyond what the schema provides, so the baseline score 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 uses a specific verb and resource: 'Calculate Net Present Value (NPV) and Internal Rate of Return (IRR)'. It clearly identifies the mathematical method and the intended application domains, and the name plus description distinguish it from financial siblings like black_scholes or breakeven_margin.

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 phrases 'for capital budgeting, investment appraisal, and payback periods' give clear intended-use context for an agent deciding between this tool and other financial calculators. It does not name alternative tools or explicit exclusion criteria, but the usage domain is specific enough to guide selection.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It accurately states the calculations performed, but it does not describe the return format, output units, or assumptions such as laminar vs turbulent flow handling. For a read-only calculator this is acceptable but not fully transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence that names the calculation verb and all key outputs with no filler or repetition of schema details. Every word contributes to selecting and invoking the tool.

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 what the tool computes and clearly names the four output quantities, which is valuable given there is no output schema. It omits output units and return structure, but the fully documented input schema and the straightforward calculator nature keep the tool navigable.

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 provides full descriptions and units for all six parameters, so the description adds little parameter-level meaning beyond naming the relevant output quantities. With 100% schema coverage, the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Calculate') and resource ('Darcy-Weisbach friction factor, Reynolds number, head loss, and pressure drop in pipes'), which clearly identifies this as the pipe-flow calculator. The mention of pipes and specific fluid-dynamics quantities differentiates it from sibling tools like beam_bending and rlc_circuit.

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 makes the usage context clear: an agent should use this tool when pipe flow calculations involving friction factor, Reynolds number, head loss, or pressure drop are needed. It does not explicitly name alternatives or exclusions, but the pipe-specific language leaves little ambiguity among the sibling 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists what is calculated but does not mention underlying assumptions (e.g., no air resistance, flat ground), output format, or edge-case behavior. This is a noticeable gap for a physics calculator.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the action and resource, then lists the calculated outputs. There is no filler or 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 three well-documented parameters, the description covers the main purpose and outputs. However, with no output schema, it omits return structure and physical assumptions, so the agent still has unanswered questions about the exact response 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 description coverage is 100%, so the schema already documents all three parameters and the default for gravityMs2. The description adds no parameter-level detail, so the baseline score 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 uses a specific verb and resource: 'Calculate 2D physics projectile kinematics' and explicitly lists the outputs (max height, horizontal range, flight time, velocity components). This clearly distinguishes it from siblings like black_scholes or rocket_deltav even without a title.

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 intended use case is implied by '2D physics projectile kinematics,' and the sibling tools are mostly unrelated calculators, so an agent can infer when to select it. However, there is no explicit statement of when to use this tool versus alternatives or any exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does list the outputs, which is useful, but it fails to explain the role of the optional frequencyHz parameter—whether impedance magnitude is computed at resonance when frequencyHz is omitted or whether it is required for that output. This ambiguity about a key behavioral path is a meaningful gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that front-loads the action and enumerates the key outputs without wasted words. Every element in the sentence contributes to the agent's understanding of the tool's 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?

The description names all major outputs, which partially compensates for the lack of an output schema, and the schema covers parameter definitions. However, the optional frequencyHz behavior is unclear, and there is no mention of assumptions (e.g., ideal components) or edge cases. For a tool with four parameters and no output schema, this leaves notable gaps in the full calling context.

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%, meaning all parameters are already documented in the schema with units and defaults. The description adds no additional parameter semantics beyond naming outputs like f0, which indirectly relate to inductance and capacitance but does not clarify how the parameters map to computations. 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 a specific verb ('compute') and resource ('resonant RLC circuit electrical properties'), and enumerates the exact outputs: resonant frequency f0, Q-factor, bandwidth, and AC impedance magnitude. It distinguishes itself from all siblings, which are predominantly finance, tax, and mechanical calculators, leaving no ambiguity about the tool's domain.

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 RLC circuit analysis by naming the computed properties, but it provides no explicit guidance on when to use this tool versus alternatives or when not to use it. There are no exclusions or alternative tool references, though the sibling list makes cross-domain confusion unlikely. Usage context is only implied, not stated.

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

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

With no annotations, the description must carry the behavioral burden; it clearly states that this is a calculation, not a mutation, and lists computed quantities. It does not disclose assumptions (e.g., ideal rocket equation) or output formatting, but for a pure math tool this is minimally adequate.

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 front-loaded sentence with no filler; every phrase adds meaning.

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 schema documents all four inputs with defaults and units, and the description names the calculated outputs (delta-v, mass ratio, propellant consumption). Without an output schema, a bit more detail about result units or shape would be ideal, but the tool can be invoked correctly as described.

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 and the schema already documents all parameters and their units. The description adds no parameter-level detail such as how gravityMs2 modifies the calculation.

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 uses a specific verb ('calculate') and names the exact resource: Tsiolkovsky rocket equation delta-v, mass ratio, and propellant consumption. The 'Aerospace & orbital mechanics' label distinguishes it from the other calculators in the sibling list.

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 gives a clear domain context ('Aerospace & orbital mechanics') and the exact equation/quantity, so an agent can tell when to reach for it. It does not explicitly name alternatives or exclusions, but the context is sufficient for this calculator set.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly signals a pure calculation tool by naming the computed outputs and calling the breakeven 'mathematical.' It does not detail all assumptions or the return format, but the deterministic, read-only nature is reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence that begins with the primary action and enumerates all calculation components without filler. Every phrase contributes distinct information, and the structure makes the multi-part computation easy to parse.

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?

All five parameters are fully documented in the schema, and the description names the key calculation areas and the governing IRS ruling, so an agent can select and invoke the tool correctly. The absence of an output schema is a minor gap since the description does not explicitly state the result format, but the computed concepts are listed.

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 every parameter already includes a name, default, and unit in the input schema. The description adds domain context like 'salary split' and 'overhead netting,' which helps an agent connect parameters to the calculation, but it does not provide any additional per-parameter meaning 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 opens with the specific verb 'Calculate' and names the exact resource: an S-Corporation reasonable salary analysis. It enumerates the distinct outputs — salary split, FICA tax shield, overhead netting, and breakeven threshold — and anchors them to IRS Rev. Rul. 74-44, making the tool clearly distinguishable from the other 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 usage for S-Corp owners evaluating reasonable compensation and entity-level tax/overhead tradeoffs, but it never explicitly states when to use this tool versus alternatives. No when-not-to-use guidance or alternative tool references are provided, so the agent must infer selection from the domain language alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the core computation and the optional step-up feature, but it does not describe key behavioral details such as compounding frequency, how the step-up is applied over time, or what exact value is returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the verb and resource, expands the acronym, and includes the key optional behavior. There is no filler or redundancy, making it highly scannable for an agent.

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 schema fully documents the parameters, and the description states the output concept ('maturity') and the step-up feature. However, since there is no output schema, the description could be more explicit about the exact return value and the assumptions behind the calculation, such as annual compounding or how the step-up changes the monthly investment.

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 four parameters with defaults and examples. The tool description adds only a mention of the optional annual step-up percentage, which reinforces stepUpPercent but does not materially add meaning 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 uses a specific verb ('Compute'), a specific resource ('Systematic Investment Plan (SIP) mutual fund maturity'), and adds a distinguishing feature ('optional annual step-up percentage'). This makes the tool's purpose immediately identifiable and separates it from sibling calculators like compound_wealth or home_loan_emi.

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 the tool should be used: whenever a SIP maturity calculation is needed. However, it provides no explicit guidance about when not to use it or how it compares to sibling tools such as compound_wealth or home_loan_emi, leaving the agent to infer the appropriate selection.

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

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

With no annotations provided, the description carries the full burden. It does state the core behavior (calculating maximum shelter and tax savings) and the governing legal caps, which gives useful context. However, it does not disclose assumptions, output format, or limitations such as whether results are annual estimates or how state taxes are incorporated.

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 no filler, and it starts with the main verb 'Calculate'. The noun phrase is somewhat dense but each element adds specificity. It could be slightly restructured for readability, but it is appropriately concise.

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?

There is no output schema, so the description must clarify return values. It names two outputs (shelter amount and cash tax savings) and the relevant cap, but it is ambiguous whether 'maximum' means the higher of the two plans or the max for each plan separately, and whether the result is a single recommendation or a comparison table. This leaves meaningful room for misinterpretation.

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 parameters are already fully documented in the schema. The description adds no parameter-specific meaning beyond what the schema fields already convey, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Calculate' and names the exact resource comparison: Solo 401(k) vs. SEP-IRA maximum legal tax-deductible retirement shelter and immediate cash tax savings, with explicit IRS Notice caps. This clearly distinguishes it from all sibling tools, none of which focus on retirement plan comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The only contextual clue is the IRS Notice reference, but there is no explicit 'use when...' or comparison to sibling tools like scorp_optimizer or contractor_parity.

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

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

No annotations are supplied, so the description carries the behavioral disclosure burden. It does convey a pure modeling/computation action and lists the output domains, but it omits return format, modeling assumptions, and how the individual computations relate to each other. This is adequate but not fully transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence with no filler and the main action is front-loaded. The jargon-heavy phrase 'unallocated option pool shuffle dilution waterfall' is somewhat unwieldy, but the description remains efficient and 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?

There is no output schema, and the tool is a complex eight-parameter model, yet the description does not state the result format or the assumptions behind the dilution waterfall. Inputs are well documented, and the outputs are named, but the agent is left to infer the output contract.

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 already provides 100% coverage with descriptions, units, and defaults for all eight parameters, so the baseline is 3. The description adds no additional parameter-level detail or relationships beyond what the schema contains, but none is strictly required.

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 uses a specific verb ('Model') and enumerates four distinct outputs: net burn rate, cash runway zero-cash date, Post-Money SAFE cap dilution, and Series A option pool dilution waterfall. This clearly differentiates it from sibling tools like black_scholes or home_loan_emi, which address entirely different domains.

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 intended context is implied through startup-specific terms like cash runway, SAFE, and Series A, but there is no explicit statement of when to use this tool versus an alternative. No exclusions or when-not-to-use conditions are provided, so the agent must infer usage from domain cues.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It usefully states that the tool calculates tip, handles tax inclusion, and produces a per-guest split. However, it does not clarify whether the tip is computed pre-tax or post-tax, what the returned itemized split looks like, or how the default values behave, which is meaningful ambiguity for a financial 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 a single, well-structured sentence that front-loads the primary verb and packs in the key aspects: tipping, tax, and per-guest split. There is no filler or redundancy, and every phrase contributes to the agent's 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?

The tool is simple—3 flat parameters, no output schema—and the input schema covers the parameters well. However, the description leaves important gaps for an agent: it does not specify the shape of the returned per-guest itemized split, nor does it resolve the ambiguity around how tax is treated, which could lead to incorrect invocation for tax-related 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 adds some contextual meaning by linking 'per-guest' to numPeople and 'tax inclusion' to billAmount, but it does not provide additional detail beyond the schema about value formats, precedence, or defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's function with a specific verb ('Calculate') and a concrete resource ('restaurant bill'), and distinguishes it from the unrelated sibling tools by mentioning tipping, tax inclusion, and per-guest split. However, 'tax inclusion' is slightly ambiguous—it could mean adding tax to the bill or estimating a tip based on a tax-inclusive amount—so it stops short of a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'restaurant bill tipping... per-guest itemized bill split' gives a clear context for when an agent should use this tool: restaurant dining scenarios requiring tip calculation and bill splitting. It does not explicitly state when not to use it or name alternative tools, but the sibling tools are largely unrelated, so no strong exclusion is needed.

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

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

With no annotations provided, the description carries the full behavioral burden. It does disclose the core transformations (Net -> Gross and Gross -> Net) and mentions statutory rate presets, but it does not clarify rounding behavior, output format, or whether those presets are actually selectable through the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that front-loads the core function and modes. It contains no redundant filler or unnecessary background.

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

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

The tool is relatively simple and the parameters are fully explained by the schema, but the absence of an output schema and the vague 'statutory rate presets' claim leave some ambiguity about results and preset behavior. It is adequate, though not 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 description coverage is 100%, so the parameters are already well documented. The description adds context around the modes but does not provide additional semantics for amount or vatRatePercent beyond what the schema already states.

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 uses a specific verb ('Calculate') and clearly identifies the resource ('European VAT and global Sales Tax'), while also naming the two modes (Add and Remove). It does not differentiate from siblings by name, but the domain is distinct enough among the listed calculation 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 when to use the tool: whenever VAT or sales tax needs to be added or removed. However, it gives no explicit guidance on when not to use it or how it compares to sibling tax-related tools such as indian_income_tax or b2b_withholding_risk.

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