calculators

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 only mentions what the user will see (free money, yearly savings, growth) but does not explain how the tiered matching works, what assumptions are made about growth, or whether the operation is read-only. Key behavioral traits like the use of first/second tier match percentages are omitted, limiting transparency.

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

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

The description is a single, front-loaded sentence that quickly conveys the tool's purpose. There is some redundancy between the title and the opening phrase ('401(k) Employer Match Calculator — Calculate your 401(k) employer match'), but it's not overly verbose. It could be tighter by removing the repeated phrase.

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 8 required parameters, no output schema, and no annotations, the description is insufficient for a tool of this complexity. It fails to explain the tiered match structure, the meaning of growth rate and years, or how the calculation works. The description is not complete enough for an agent to select and correctly use the tool without additional 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?

The schema has 0% description coverage, and the tool description only references salary and contribution rate, leaving the six tier and growth parameters unexplained. A user cannot understand what 'firstTierPct' or 'secondTierMatchPct' means from the description, so it adds minimal value for most of the 8 required parameters.

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

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

The description clearly identifies the tool as a 401(k) employer match calculator with a specific verb ('Calculate') and resource ('your 401(k) employer match'). It distinguishes itself from sibling financial calculators by focusing on the match calculation, and the outputs listed (free money, total yearly savings, long-run growth) further clarify its purpose.

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

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

The description provides clear usage context by instructing the user to 'enter salary and contribution rate', implying this tool is used when those inputs are available. However, it does not explicitly state when not to use it or mention alternatives for different savings calculations, so usage guidance is implied rather than explicit.

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 transparency burden. It discloses the core calculation logic (steps, tokens, model pricing, success rate) and the intended transformation. However, it omits details such as whether pricing data is built-in, how cacheHitRatePct is factored in, or what the output format will be, leaving some behavioral ambiguity.

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 tool name and then provides a concise explanation of what it calculates. It is efficient, though it repeats the title 'AI Agent Cost Calculator' and could be slightly tighter. No unnecessary 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?

This tool has 7 required parameters, no output schema, and no annotations, so the description needs to explain the full context. It gives a useful conceptual overview but does not specify the return value (e.g., a currency amount, daily cost, or breakdown), how cache hits affect the result, or any assumptions about model pricing. The description is not complete enough for an agent to fully understand the tool's behavior without additional inference.

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 for the 7 parameters. It covers several conceptually: 'steps' (stepsPerRun), 'tokens' (inputTokensPerStep/outputTokensPerStep), 'model pricing' (modelId), and 'success rate' (successRatePct), but it does not mention runsPerDay or cacheHitRatePct. It adds high-level context but not full per-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 explicitly states the tool's purpose: to calculate the true cost per successful task for an AI agent, incorporating steps, tokens, model pricing, and success rate. It uses a specific verb ('See') and resource ('AI agent cost per successful task'), and the mention of 'successful task' distinguishes it from sibling tools like llm_api_cost_calculator or fine_tuning_cost_calculator.

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 usage context by explaining that it converts 'dashboard cost per run into the true unit cost,' which suggests it is meant for scenarios where per-run costs and success rates are known. However, it does not explicitly state when to use this tool over alternatives, nor does it provide any exclusions or mention of 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 transparency burden. It discloses that the tool 'estimates' and specifies the output metrics, but it does not mention assumptions, limitations, or that it is a pure read-only calculation. This is a moderate gap for a calculator tool.

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

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

The description is a compact two-sentence structure that front-loads the tool's purpose. Every sentence adds value, with no redundant filler or repetition of the title.

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 (8 required parameters, no output schema, no annotations), the description is too sparse. It does not explain how inputs combine, what assumptions are made, or what the return format looks like, leaving significant gaps for an agent trying to invoke 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?

The schema has 0% description coverage across 8 required parameters, so the description must compensate. It only mentions 'task volume, time saved, review effort, and API costs', which maps to roughly half the parameters, leaving loadedHourlyCost, automationCoveragePct, platformCostMo, and buildCost undefined. An agent cannot fully understand the input requirements from the description 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 estimates the payback of automating a process with AI, listing specific inputs (task volume, time saved, review effort, API costs) and outputs (net monthly savings, break-even months). This distinguishes it from sibling tools like ai_roi_calculator or ai_agent_cost_calculator by focusing on process-level automation ROI.

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 clear context that the tool is for estimating AI automation payback, implying when it should be used. However, it does not explicitly mention when not to use it or suggest alternatives, though multiple related calculator siblings exist.

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 burden of explaining behavior. It discloses that it compares monthly costs, finds break-even token volume, and identifies the cheaper option, giving some insight into outputs. However, it does not mention underlying assumptions, units, or limitations, leaving gaps in transparency.

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

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

The description is concise (two sentences) and front-loads the core purpose. However, the first sentence repeats the title nearly verbatim, adding redundancy. The second sentence adds valuable output details, but the overall structure could be tighter.

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 explains the high-level function (cost comparison and break-even calculation) but omits critical context such as input units, assumptions about self-hosting costs, or any formula. Given the lack of an output schema and 0% parameter coverage, the description is only marginally sufficient for a user to invoke 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 0%, and the description does not compensate by explaining parameters. Parameter names like 'apiPricePerMtok' and 'selfHostFixedPerMonth' are cryptic; the description never clarifies what 'Mtok' means (presumably per million tokens) or what 'fixed per month' should include. This is a significant gap for a tool with all-required parameters.

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 ('Compare') and resource (monthly cost of hosted LLM API vs self-hosting on GPUs) and states it finds the break-even token volume. However, it does not distinguish itself from the sibling tool 'llm_self_host_vs_api_calculator', which appears to serve the same purpose.

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

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

No guidance is given on when to use this tool vs alternatives. It does not mention any exclusions, prerequisites, or scenarios where another calculator (e.g., llm_api_cost_calculator or llm_self_host_vs_api_calculator) would be more appropriate. The usage context is only implied by the description's purpose.

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 must carry the burden. It discloses the research sources and the important caveat that the result is an estimate, not a prediction about an individual. However, it does not explain the output format, interpretation, or any limitations beyond the 'not a prediction' caveat, leaving room for more transparency.

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

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

The description is a single, well-structured sentence that immediately states the tool's purpose, includes credible sources, and adds a key caveat. Every word earns its place; there is no redundancy or filler.

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

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

Given the tool's relative simplicity, the description is adequate but has clear gaps: it does not state what the output looks like (score range, units, or interpretation) and does not elaborate on how to use the result. The research citations and caveat provide some context, but without annotations or an output schema, more completeness would be expected.

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%, and the description does not explain any of the four parameters. It only says 'estimate your task mix' without detailing how routineCognitive, creativeCognitive, interpersonal, or physical should be interpreted or whether they should sum to a particular value. The description fails to compensate for the lack of schema-level 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?

The description clearly states that this tool computes an AI job exposure score based on published research, using a specific verb ('estimate') and resource scope ('task mix'). It also distinguishes itself from the many other calculators by naming its research basis and adding the caveat that it is 'not a prediction about you.'

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 estimating AI job exposure from a task mix, but it does not explicitly state when to use it or mention alternatives. There is useful context (research-based, not predictive), but no direct when-to-use or when-not-to-use guidance, so it falls at the 'implied usage' level.

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 must carry the transparency burden. It is upfront about the calculation scope (monthly net savings, break-even months, annual return) and inputs, but it does not disclose the underlying formula or assumptions, such as how implementation cost is treated or whether the calculation is per-staff or aggregate.

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 packs in the purpose, inputs, and outputs without unnecessary detail. It loses a point for opening with a near-verbatim repetition of the tool's title before getting to the actionable 'Estimate' clause.

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 5-parameter required-input calculator with no output schema and no annotations, the description is not complete enough. It lists outputs but fails to define all input parameters clearly, and provides no guidance on how break-even or annual return are derived, which is needed for confident 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?

The schema has 5 required parameters with 0% description coverage, so the description must compensate. It mentions 'hours saved per week, staff count, and cost,' which roughly maps to hoursSavedPerWeek, headcount, and monthlyToolCost, but leaves hourlyCost and implementationCost semantically ambiguous and does not clarify whether 'cost' refers to monthly tool cost or combined costs.

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 ('Estimate the ROI and payback of an AI tool') and names the specific resource. It also lists key inputs and outputs, which helps distinguish it from generic ROI calculators, though it does not explicitly differentiate it from very similar siblings like ai_automation_payback_calculator.

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 by specifying the required inputs (hours saved per week, staff count, cost) and outputs, but it provides no explicit guidance on when to choose this tool over alternatives such as generic roi_calculator, break_even_calculator, or ai_automation_payback_calculator, nor 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. It discloses the outputs (net profit, margin, ROI, fee breakdown, break-even price) but does not mention underlying fee rate assumptions, the optional ad cost parameter, or any limitations. This gives functional transparency but leaves some behavioral details implicit.

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

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

The description is concise, two sentences, front-loaded with the verb 'Calculate,' and every clause contributes useful information about inputs or outputs. No filler or redundant phrasing.

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?

Since there is no output schema, the description lists the return values (net profit, margin, ROI, fee breakdown, break-even price). It captures the essential workflow and is complete enough for a simple calculator. Minor gaps include not mentioning the optional adCost parameter or fee rate assumptions, but these are not critical for basic usage.

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. It names price, COGS, and size tier and explains their role in the calculation. However, it omits adCost and referralRatePct, leaving those to be inferred from the schema. It adds some meaning beyond parameter names but does not fully cover all five parameters.

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 'Calculate Amazon FBA net profit, margin, and ROI for any product' with a specific verb and resource. It distinguishes itself from generic calculators by explicitly targeting Amazon FBA and mentioning the full fee breakdown.

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

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

The description provides clear usage context: 'Enter price, COGS, and size tier to see the full fee breakdown and your break-even price.' It tells the agent exactly what inputs to provide and what to expect, but it does not explicitly mention alternatives or situations where this tool should not be used.

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 must carry the burden. It explains the tool's effect (conversion, compounding impact) and implies outputs like final balance, but does not state assumptions, units, or limitations (e.g., max rate, years). This is adequate but not rich in behavioral disclosure.

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 brief and front-loaded, but the first clause repeats the title verbatim. The remaining sentences are efficient and informative, making it mostly concise with minor 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?

With no output schema, the description should clarify what the agent can expect in response. It mentions 'final balance' and 'true annual yield' but does not state whether the tool returns a single number, an object, or formatted text. It also omits input constraints (e.g., rate cap, deposit limits) that are present in the schema, leaving some gaps for a 4-parameter 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 0%, and the description adds some meaning by explaining that nominalRatePct is APR and listing compounding options (though it omits 'annually' from the enum). Basic semantics for deposit and years are conveyed by property names, but units and constraints are not explained.

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 ('Convert') and specifies the resource ('nominal rate (APR) to APY') while also mentioning compounding frequency and final balance. This clearly distinguishes it from sibling calculators like compound_interest_calculator or roi_calculator.

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: it is for converting APR to APY and analyzing how compounding frequency affects yield and balance. It does not explicitly name alternative tools or exclusion cases, but the use case is unambiguous for a calculator of this type.

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 burden. It discloses the outputs and inputs but does not explain the calculation method, assumptions, return format (e.g., JSON vs text), or any side effects. For a simple calculator, the behavior is mostly implied 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, well-structured sentence that front-loads the tool's name and purpose, lists inputs and outputs, and avoids unnecessary verbosity. Every element 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?

Given the simple calculator nature with 4 parameters and no output schema, the description is reasonably complete but misses key details: the meaning of 'inchesPerGallon' and the return format. It lists the outputs but doesn't specify how they are returned, which is important in the absence of an output schema.

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 explains length, width, and height (in inches) but does not mention 'inchesPerGallon' at all. Since schema coverage is 0%, the description should compensate by explaining all parameters, but it leaves one key parameter (inchesPerGallon) unexplained, and its relationship to 'inch-of-fish' is only implicit.

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 aquarium volume and stocking capacity. It specifies the input dimensions (length, width, height in inches) and the outputs (gallons, litres, inch-of-fish, heater wattage), making it distinct from other calculator 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 ('Work out your fish tank volume and stocking capacity') and provides clear context. It does not explicitly mention exclusions or alternatives, but given the unique domain among sibling calculators, the intended use is evident.

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 disclose behavior. It explains the calculation steps (simplify ratio, solve for height) and provides an example output format ('16:9'), but it does not describe the exact return structure, rounding behavior, or edge cases. The title's 'Crop' is not mentioned in the description, creating minor ambiguity.

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 at two sentences, front-loading the purpose and following with actionable instructions. The first sentence partially echoes the title ('Aspect Ratio Calculator (Resize & Crop)'), but it adds the key behavior 'resize while keeping it', so it 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 simple calculator with no output schema and no annotations, the description covers the core resizing workflow well. However, it omits the 'Crop' functionality hinted in the title and does not specify the output format beyond an example, leaving some context incomplete.

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%, but the description compensates by clarifying the roles: width and height are the original dimensions, newWidth is the desired new width, and it explains the relationship between them. This adds meaning beyond the bare schema, which only lists them as numbers.

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 simplifies an aspect ratio and computes a matching height for a new width, with a concrete example ('16:9'). It specifies the resource (aspect ratio) and the verbs (simplify, resize), but does not explicitly distinguish it from siblings like crop_factor_calculator or print_resolution_calculator.

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 instructional context: enter width and height to get the ratio, then enter a new width to find the matching height. However, it does not state when to use this tool versus alternative calculators, nor mention 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 does disclose that output includes 'exact ratio and bake-time guidance' and warns against scaling by diameter, which is useful. However, it fails to mention any assumptions, limits, or edge cases (e.g., what happens if incompatible dimensions are given), leaving moderate gaps.

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 short and mostly front-loaded, but it begins by repeating the tool title ('Baking Pan Size Conversion Calculator —'), which is redundant. The second sentence adds high-value content. Overall, it is concise with minimal verbosity, though the title repetition could be trimmed.

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 complex nested schema, lack of output schema, and no annotations, the description is incomplete. It does not specify how to structure inputs for each shape, which dimension fields are required, or what the exact output format is. Mentioning 'ratio and bake-time guidance' is vague, leaving significant ambiguity for an agent.

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% and the parameters are nested objects with multiple fields. The description never explicitly explains 'original' and 'target', nor does it map shape types to required dimension fields (e.g., round needs diameter, square needs side, rectangular needs length/width). Thus it does not compensate for the absence of schema-level 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 explicitly states 'Convert a recipe between round, square, and rectangular baking pans' with a specific verb and resource. It also notes the key methodological distinction ('by pan area, not diameter') and the output ('exact ratio and bake-time guidance'), which differentiates it from sibling calculators like recipe_scaler_calculator.

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: when converting between different baking pan shapes. It provides guidance on scaling approach (area vs diameter). However, it does not explicitly state when not to use it or name alternative tools, so it misses the highest bar.

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 for behavior. It frames the tool as a calculator that 'sees' savings, implying a read-only computation, but does not disclose assumptions or side effects. Since calculators are inherently non-mutating, it is adequate but lacks explicit detail.

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 state the purpose and key inputs without redundancy. The description is front-loaded and every phrase 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?

With 6 required parameters, no output schema, and no annotations, the description is too brief to fully specify behavior. It does not explain calculation assumptions, output format, or the role of all parameters, making it incomplete for an agent to invoke 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 has 0% description coverage, so description must explain parameters. It mentions fee, promo months, and planned payment, corresponding to transferFeePct, promoMonths, and monthlyPayment, but omits balance, currentAprPct, and revertAprPct. The mappings are not explicit, and key inputs like revertAprPct are left unexplained.

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 0% balance transfer savings versus current APR, with specific inputs. It distinguishes from sibling calculators like credit_card_payoff_calculator by focusing on balance transfer scenarios.

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 use case: evaluating a balance transfer offer versus keeping current APR. It provides context but does not explicitly mention alternatives or exclusions, so 4.

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 burden of behavioral disclosure. It communicates this is an estimation/calculation tool and names the outputs (body fat and lean body mass). However, it does not disclose method limitations, accuracy caveats, or that hip measurement is only required for females, which is a notable behavioral nuance.

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 method name and primary purpose. Every phrase adds value: method, what it estimates, inputs, and outputs. No wasted words 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 calculator with no annotations and no output schema, the description covers the core purpose, inputs, and outputs. However, the female-only hip requirement and the use of centimeters (implied only by schema property names) are missing, which are important contextual details for correct 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?

With 0% schema description coverage, the description compensates by enumerating most parameters in plain English (sex, height, neck, waist, hip). It fails to explain the hipCm parameter is conditional on sex and omits weightKg entirely, which leaves a partial gap in 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 the tool estimates body fat percentage using the US Navy tape-measure method, with a specific verb ('Estimate') and resource ('body fat percentage'). This distinguishes it from sibling calculators like ideal_weight_calculator or tdee_calculator, which target different health metrics.

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 conveys when to use the tool (when body fat percentage is needed via the Navy method) and lists the required inputs. It does not explicitly exclude alternatives, but the method name and input list provide enough context for a knowledgeable agent to select it over 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 mentions the core calculation and output but omits the required targetProfit parameter and gives no caveats such as the relationship between price and variable cost. This is a significant gap for a tool with no annotation support.

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 and front-loaded with purpose in two sentences. However, the opening phrase 'Break-Even Point Calculator' repeats the title, adding slight redundancy, though overall it is efficient and 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?

With four required parameters, no annotations, and no output schema, the description is too thin. It fails to mention targetProfit and does not specify the return structure beyond 'units and revenue,' leaving an agent without enough context to fully understand input requirements and expected output.

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. It names three of the four inputs (fixed costs, variable cost per unit, price) and adds meaning, but it completely omits targetProfit, which is required. Thus the parameter documentation is incomplete.

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 the break-even point in units and revenue, using a specific verb and resource. It is distinct from sibling calculators by naming the exact metric and formula 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 usage by telling users what to enter and what they will see, but it does not explicitly state when to choose this over alternative calculators or mention the required targetProfit input. Context is present but no exclusions or alternatives 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?

With no annotations, the description carries the burden of behavioral disclosure. It states the calculation output (exact grams and strength chart) but does not mention side effects, limitations, error handling, or the fact that this is a pure read-only computation. However, being a calculator, the behavior is largely implied by the description's focus on computation.

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 with no redundant content. It efficiently communicates the tool's purpose, inputs, and outputs in a compact form.

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 annotations, no output schema, and five required parameters, yet the description does not clarify that all parameters are required, how mode alters input requirements, or what the return structure looks like. It mentions some inputs and an output chart but leaves significant gaps for correct invocation, especially regarding parameter combinations and constraints.

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. It references water volume, meat weight, salt percentage, sugar (implied), and wet/dry modes, but does not explicitly name all five parameters or explain the relationship between mode and which inputs are required. The phrase 'enter water volume or meat weight' hints at mode-dependent usage but leaves ambiguity about the exact parameter 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 calculates salt and sugar for wet or dry brines by weight, with a specific verb ('Calculate') and resource ('brine'). It distinguishes itself from sibling calculators by naming the unique domain and the specific output (exact grams and strength chart).

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 context (brine preparation by weight) but does not explicitly state when to use this tool versus alternative calculators or provide exclusions. It mentions entering water volume or meat weight, which gives a hint about mode-dependent input, but there is no direct comparison to siblings or guidance on mode 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, the description carries the full burden for behavioral disclosure. It reveals some behaviors (gross vs net burn, default verdict, simulation), but omits assumptions about how growth parameters interact, edge cases, or what the verdict actually means. It adds value but lacks depth.

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 single sentence with useful detail, but it unnecessarily repeats the tool name 'Burn Rate & Runway Calculator' as a prefix. Otherwise, it is front-loaded and free of fluff.

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 5 parameters, no annotations, and no output schema, the description is incomplete. It lists some output features but does not explain how growth rates are applied, what 'default alive or dead' means, or what the simulation returns. An agent cannot confidently predict invocation behavior or output structure.

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. It mentions revenue growth but never mentions monthlyExpenseGrowthPct, and does not map required parameters (cashBalance, monthlyExpenses, monthlyRevenue) to the calculation concepts. Optional growth parameters are only vaguely alluded to.

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 startup burn rate and cash runway, and lists specific outputs: gross vs net burn, alive/dead verdict, and month-by-month cash balance simulation. This distinguishes it from sibling calculators like break_even_calculator or churn_rate_calculator, which address different financial metrics.

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. It does not mention other calculators or specify scenarios where burn rate/runway analysis is appropriate, relying only on the implied startup finance 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?

No annotations are provided, so the description carries the burden of disclosing behavior. The description lists the outputs (LTV, ratio, payback, gross profit) and mentions 'verdict bands', but lacks details on assumptions or calculation methodology (e.g., how LTV is derived). It adds some value beyond the name, so a 3 is appropriate.

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, focused sentence that lists all key outputs without redundancy. It is front-loaded with the tool name and purpose, making it highly efficient and 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?

The tool has 4 required parameters, no output schema, and no annotations. The description lists the computed metrics but does not explain the formulas or edge cases, which would be helpful for correct usage. It is adequate but not comprehensive for a calculator with this 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 description coverage is 0% and the description provides no explanation for parameters like ARPA, grossMarginPct, monthlyChurnPct, or CAC. The names are suggestive but not defined. The description should compensate for the low schema coverage but does not, leaving the agent to infer meanings.

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 Customer Lifetime Value, LTV:CAC ratio with verdict bands, CAC payback months, and monthly gross profit. The verb 'Calculate' and specific resource 'Customer Lifetime Value' distinguish it from sibling calculators, though it doesn't name a specific sibling.

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 needing LTV/CAC metrics) and the context is clear, but it does not explicitly mention alternatives or exclusions among the many sibling calculators. It provides no 'use instead' guidance, hence a 4 rather than 5.

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

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

With no annotations provided, the description carries the full burden. It discloses the key outputs (daily calorie deficit, weekly loss rate, safety classification) and implies a calculation model. It does not explain the underlying formula or safety thresholds, but this is acceptable for a calculator with no side effects.

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 names the tool's purpose and outcomes without waste. It earns its place by covering all necessary key points efficiently.

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

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

For a 3-parameter calculator with no output schema, the description sufficiently conveys what the tool returns: daily deficit, weekly loss, and safety status. It lacks explicit units or safety criteria, but the context is clear enough for basic 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 0%, so the description must compensate. It explains goalKg and days via 'goal weight' and 'target date', and implicitly maps currentKg as the starting point. However, currentKg is not explicitly described, and units/interpretations are mostly left to the parameter names and schema bounds.

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 function: setting a goal weight and target date to compute daily calorie deficit, weekly loss rate, and safety. It uses a specific verb phrase and distinguishes itself from sibling calculators like calories_burned_calculator or macro_calculator by focusing on goal-based deficit planning.

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 indicates when to use the tool (when you have a goal weight and a target date). It does not explicitly exclude alternatives, but it provides enough context for an agent to select this over other calculators without confusion.

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 disclosure burden. It discloses that it uses the MET formula and supports side-by-side comparison, but it doesn't describe output format, rounding, or units. For a simple calculator this is adequate though not rich.

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 with no fluff. Every phrase adds value, including the formula and the side-by-side comparison feature.

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 enough for a simple calculator with three primitive parameters and no output schema. It explains the conceptual inputs and what is estimated, but it doesn't describe the output format or example values, which would help an agent invoke it 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 coverage is 0%, so the description must compensate. It mentions 'weight, minutes, and activity' but doesn't explicitly map to the actual parameters (met, weightKg, minutes), using 'activity' for the 'met' field. This adds partial meaning but could mislead and leaves the units and MET interpretation unclear.

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 ('calculate') and resource ('calories burned by activity'), with a distinctive method (MET formula) and an additional capability (compare exercises side by side). It effectively differentiates from sibling calorie-based calculators like calorie_deficit_calculator.

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 usage context is implied but not explicit: it says to use this calculator to estimate energy spent with MET, but it doesn't mention when to prefer this over alternatives like tdee_calculator or macro_calculator. No exclusions or alternative references 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?

With no annotations, the description carries the full burden. It discloses the tool's outputs (max loan, car price, rule check) but does not explain how trade-in value factors in or what the 15%-of-income rule check entails. Read-only behavior is implied but not explicit, leaving some ambiguity.

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 with no wasted words. It front-loads the purpose and quickly enumerates inputs and outputs, making it easy to scan.

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 6 parameters, no output schema, and no annotations, the description is relatively sparse. It does not specify the output format, the exact mechanics of the 15% rule, or the role of tradeIn. While it covers the core purpose, an agent would need additional reasoning to handle all parameters and interpret the result 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 0%, so the description must compensate. It maps monthlyPayment, annualRatePct, termMonths, and downPayment to plain-language equivalents and hints at income via the 15% rule. However, tradeIn is omitted entirely, meaning one parameter's semantics remain unexplained.

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 ('see') and specifies the resource ('max loan and car price'), which distinguishes it from sibling tools like car_loan_calculator. The mention of a 15%-of-income rule check adds unique functionality, making the purpose unmistakable.

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

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

The description clearly states the use case ('How much car can you afford?') and enumerates key inputs, giving the agent a sense of when to use it. However, it does not explicitly name alternatives or provide exclusion criteria, so it lacks 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 burden. It discloses that the tool computes a year-by-year value curve, total loss, and percent lost, which gives some behavioral transparency. However, it doesn't disclose the depreciation model, assumptions, or how rates interact.

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 efficiently conveys the tool's purpose and outputs without unnecessary fluff.

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 4-parameter tool with no output schema and no annotations, the description provides an overview but omits critical details about parameter semantics and the calculation logic. It doesn't fully equip an agent to invoke 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 0%, and the description only mentions 'price and depreciation rates' without breaking down the specific parameters (firstYearRate, annualRate, horizonYears). This fails to clarify what each parameter controls.

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: estimating future car value with a year-by-year value curve, total loss, and percent lost. It distinguishes itself from sibling calculators like car_loan_calculator or car_affordability_calculator by focusing on depreciation.

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 want to estimate future car value) but doesn't explicitly address alternatives or situations when not to use it. It lacks a direct comparison to other car-related 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. It discloses the output fields and that it produces an estimate. However, it does not mention limitations, assumptions, or the fact that money factor is a decimal (not an APR), nor does it state that the tool is a read-only calculation with no side effects.

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, and every sentence provides useful information. It avoids redundancy and is appropriately sized for the tool's complexity.

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 gives the core inputs and outputs but lacks parameter definitions, usage guidance, and any note about edge cases or assumptions. Given the absence of annotations and output schema, it is adequate but has clear gaps that could leave an agent uncertain about parameter semantics.

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%, and the description only lists parameter names without defining them. Terms like cap cost, residual, and money factor are not explained, nor are units or formats (e.g., money factor as decimal, sales tax as percentage). The description adds minimal value beyond the schema's property names.

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 estimates monthly car lease payments using cap cost, residual, term, and money factor. It also lists the output breakdowns (depreciation/finance split, sales tax, APR), which distinguishes it from sibling tools like car_loan_calculator or lease_vs_buy_car_calculator.

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 when you have lease-specific inputs like money factor, cap cost, and residual. However, it does not explicitly state when not to use this tool or mention alternative calculators (e.g., car_loan_calculator for loan payments). The context is clear but lacks 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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral aspects by listing the outputs (monthly payment, amortization schedule, total interest, cost of ownership), giving the agent a concrete sense of what the calculation will return. It does not mention any side effects, but as a calculator tool, this is sufficient.

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

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

The description is concise but starts with a redundant repetition of the title, which does not earn its place. The second sentence adds valuable output information. Overall, it is short and readable but not optimally structured.

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

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

With six required parameters and no annotations or output schema, the description provides a high-level overview but lacks details on calculation formulas, trade-in handling, and response shape. It adequately describes what the tool does, but a more complete description would clarify how inputs affect the calculation and how to interpret the 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 0%, so the description must compensate for the six parameters. It only references sales tax and trade-in at a high level, but does not explain how tradeInValue is applied (e.g., deducted before tax), how salesTaxPct is calculated, or whether annualRatePct is APR vs monthly. Parameter names provide some hints, but the description adds minimal semantic value 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 a specific action ('Calculate your exact monthly car payment') and includes the key differentiators of sales tax and trade-in. It also lists specific outputs (amortization schedule, total interest, cost of ownership), which distinguishes it from sibling tools like car_affordability_calculator and loan_amortization_calculator.

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 this tool: when calculating a car loan payment that includes sales tax and trade-in. However, it does not explicitly state exclusions or name alternative tools, so it falls slightly short of a 5.

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

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

With no annotations provided, the description carries the full burden. It discloses the tool's primary outputs (interest, breakdown, blended APY) but doesn't mention assumptions, edge cases, or any side effects. For a calculator this is acceptable but not rich; no mention of how inputs like 'step' affect results.

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 with a dash leading into output details. No wasted words, and it front-loads the core action ('Build a CD ladder') before listing results.

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 four required parameters, no output schema, and no annotations, the description should provide richer context. It explains outputs but leaves key parameter semantics (especially 'step') ambiguous, and doesn't clarify the calculation model or input constraints beyond the schema.

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. It indirectly explains 'principal' (savings) and 'rungs' (staggered-maturity CDs), but 'baseApy' and especially 'step' are not described at all. The agent would need to infer parameter meanings from context or external knowledge.

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: building a CD ladder by splitting savings across staggered-maturity CDs. It names specific outputs (year-one interest, per-rung breakdown, blended APY), distinguishing it from sibling calculators like apy_calculator or compound_interest_calculator.

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 through its definition but provides no explicit guidance on when to use this tool versus alternatives. It doesn't mention exclusions or alternative tools, leaving the agent to infer context from the sibling list.

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 burden of behavioral disclosure. It adds useful context by mentioning compounding conversion and implied average customer lifetime as included outputs, but it does not describe side effects, output format, or assumptions. Since the tool is a calculator, this is partially 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 compact, using only two sentences. However, it opens with 'Churn & Retention Rate Calculator', which repeats the tool's title and adds no value. The remaining content is concise 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?

Given five required parameters, no annotations, and no output schema, the description is incomplete. It does not explain the inputs' roles, the output structure, or the formulas/assumptions behind the calculations (e.g., how annual churn is derived from monthly). An agent would need to infer too much to invoke this tool confidently.

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. It does not describe any of the five parameters (startingCustomers, lostCustomers, startingMRR, lostMRR, expansionMRR). The parameter names are somewhat self-explanatory, but the description does not explain how they map to the calculated metrics or what each represents, leaving the agent to infer.

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 churn and retention metrics, listing specific outputs (monthly/annual customer churn, revenue churn, net revenue retention). It distinguishes from sibling calculators by focusing on retention metrics, though it does not explicitly name alternatives.

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 should be used when calculating churn/retention metrics but provides no explicit guidance on when to use it versus alternatives like saas_mrr_growth_calculator or cac_ltv_calculator. No exclusion criteria 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?

With no annotations, the description carries the burden. It discloses that the tool provides an estimate with typical ranges and cash to close, which is useful behavioral context. It does not detail assumptions or limitations, but the main output behavior is covered.

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 efficiently conveys purpose and output. No unnecessary words or repetition.

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

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

Given the tool is a calculator with no output schema, the description adequately explains the return value (itemized fees, ranges, cash to close). However, the lack of any parameter guidance and no mention of underlying assumptions makes it slightly incomplete for an agent without further hints.

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% and all 7 parameters are undocumented in the description. The description mentions fee categories loosely matching the parameters but does not explain each parameter's meaning, units, or how to set them. This is a significant gap.

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 estimates US mortgage closing costs before purchase, listing specific fee categories (origination, appraisal, title, prepaid) and outputs (typical ranges, cash to close). This distinguishes it from sibling tools like mortgage_piti_calculator or down_payment_calculator.

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 'before you buy' but provides no explicit alternatives or exclusions. It does not mention when to prefer this over other mortgage-related calculators, leaving the agent to infer the 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 provided, the description carries the burden of behavioral disclosure. It discloses the core calculation logic (investment grows to target by retirement) and hints at a gap awareness via 'Mind the gap.' However, it does not specify output format, how current savings are compared, edge-case handling (e.g., currentAge > retirementAge), or potential error messages. It is more than a tautology but lacks detailed 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?

The main sentence is concise, front-loaded, and informative. The second element 'Mind the gap' is short but vague, adding personality rather than clear technical value; it slightly detracts from clarity but does not make the description bloated. Overall, it is 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?

Given the tool's complexity (5 required parameters, no output schema, no annotations), the description is incomplete. It explains the fundamental concept but does not cover expected return values, assumptions (beyond zero contributions), parameter relationships, or error conditions. An agent would struggle to anticipate the tool's full behavior from this 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 0%, so the description must compensate. It mentions 'FIRE target' and 'retirement' and 'invested today,' but does not directly define the five parameters (currentAge, currentSavings, targetFireNumber, retirementAge, realReturnPct). Some meanings can be inferred from parameter names, but the description adds minimal semantic value for the parameters themselves.

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 with a specific verb ('Find') and a specific resource ('your Coast FIRE number'), and defines the concept. It distinguishes itself from sibling tools by explicitly noting the 'zero further contributions' criterion, which sets it apart from general FIRE or compound interest 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 when to use it (for Coast FIRE scenario with zero future contributions) but does not explicitly mention alternatives or exclusions. Sibling tools like fire_calculator exist, yet no direct comparison is made. The phrase 'zero further contributions' provides context, but it lacks an explicit 'use this when' or 'instead of' 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 present, so the description carries the full burden. It discloses output behavior (year-by-year growth and final interest portion) and the optional verifiable certificate behavior via certify=true. It does not discuss edge cases, but for a calculation tool this is sufficient.

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

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

The description is three sentences, each adding distinct value: purpose, output explanation, and optional certificate feature. There is slight redundancy with the title in the first sentence, but no unnecessary filler.

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

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

Given no output schema and no annotations, the description adequately conveys the tool's function, output nature (year-by-year balance, interest portion), and special feature (certificates). It omits detailed return fields and input format nuances, but for a calculator tool the core info is present.

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%, requiring the description to compensate. It clarifies only monthlyContribution and certify, while leaving initialPrincipal, annualRatePct, and years to be inferred from their names. Crucially, it does not specify whether annualRatePct is a percent or decimal, leaving key ambiguity.

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 'Calculate compound interest with monthly contributions,' identifying the exact verb and resource. It also mentions output features (year-by-year balance growth, pure interest breakdown) that distinguish it from 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?

The description provides clear context for when to use the tool: any compound interest calculation with monthly contributions. However, it does not explicitly mention alternatives or exclusion criteria, so it stops short of full when/not 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 provided, the description carries some burden. It discloses the inputs and outputs but does not describe important behavioral details, such as how wastePct affects the calculation, whether bag count is rounded up to whole bags, or any underlying assumptions. The behavior is predictable for a simple calculator, but key nuances are missing.

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 and front-loaded with the title and purpose, but the first sentence largely repeats the title verbatim, creating slight redundancy. Still, it is two sentences with no unnecessary detail, making it easy to scan.

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 this is a 5-parameter calculator with no output schema and no annotations, the description provides a basic understanding of inputs and outputs but misses critical parameter semantics (waste and bag yield) and does not specify unit conventions beyond what parameter names imply. It is minimally sufficient but leaves room for agent confusion.

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 explain parameters. It mentions length, width, and thickness, but omits wastePct and bagYieldFt3, which are required inputs. The parameter names are somewhat self-explanatory, but the description does not clarify these advanced inputs, leaving a significant gap for the agent.

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 concrete volume and bag count for slabs, footings, or pads, with a specific verb ('calculate') and resource ('how much concrete you need'). It distinguishes itself from sibling calculator tools by focusing on concrete-specific outputs (cubic yards, cubic feet, bag count).

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: for any concrete slab, footing, or pad calculation. It clearly defines the input scenario (enter length, width, thickness) but does not explicitly exclude alternatives or mention when not to use it, which is acceptable given the calculator 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 disclose behavioral details. It mentions four of five required inputs but omits contractorGross entirely and does not describe the output or calculation assumptions, leaving a significant gap in transparency.

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

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

The description is a single, well-structured sentence that front-loads the purpose and lists the main inputs. It is concise with no redundant content.

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

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

Despite being a moderately complex calculator with five required parameters and no output schema or annotations, the description is too brief. It fails to document the contractorGross parameter or the returned value, making it insufficient for an agent to invoke correctly without further inference.

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 parameter descriptions are absent (0% coverage), so the description must compensate. It explains salary, employer FICA, benefits, and PTO, but not contractorGross, and does not specify whether employerFicaPct is a percentage or decimal, leaving key ambiguity.

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

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

The description clearly identifies the tool as comparing W-2 and 1099 work and computing the equivalent break-even 1099 gross rate. This specific verb+resource combination distinguishes it from sibling calculators like salary_to_hourly_calculator or employee_cost_calculator.

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

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

The description provides clear usage context: use when comparing a W-2 job to 1099 contract work, with specific inputs. It does not explicitly name alternatives or exclusions, but the intended scenario is unambiguous.

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 lists inputs and output, but it does not disclose the exact calculation method, handling of edge cases (e.g., zero miles), or whether all inputs are in the same currency/period. It is a simple calculator, so a 3 is appropriate—adequate but not rich in behavioral detail.

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 tool's purpose and efficiently lists inputs and output. Every word earns its place 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?

For a 6-parameter calculator with no output schema and no annotations, the description is mostly complete: it tells users what to enter and what to expect. It could be improved by explicitly stating annualMiles and the output format (e.g., dollars per mile), but the core information is present and sufficient for an agent to select and invoke 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 description coverage is 0%, so the description must compensate. It explains the cost categories (yearly fuel, maintenance, insurance, depreciation, financing/lease cost) and implicitly references annualMiles via 'cost per mile driven.' This adds meaning to the parameter names, especially 'financingOrLeaseCost.' However, it does not explicitly name annualMiles as a parameter or clarify units for all fields, so a 4 is warranted.

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 the true cost per mile to own a car, listing all key input categories (fuel, maintenance, insurance, depreciation, financing/lease) and the output (cost per mile driven). This specific verb+resource distinguishes it from sibling car-related calculators like car_loan_calculator or car_depreciation_calculator.

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 it—when you have yearly cost figures and want overall cost per mile—but it does not explicitly state alternatives or exclusions. It lacks guidance such as 'for loan-only calculations use car_loan_calculator.' The context is clear enough to infer usage, but the lack of exclusions keeps it at a 3.

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 mentions the minimum-payment trap but fails to disclose how minimum payment is computed, whether targetMonths alters behavior, or assumptions about compounding. This leaves significant behavioral ambiguity.

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 with an em dash. The second clause is somewhat vague but not overly long; it earns its place by adding the minimum-payment trap context.

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 annotations or output schema, the description is incomplete. It covers the primary use case but omits the optional targetMonths parameter, output format, and key assumptions about minimum payments, leaving the agent without enough context for a 4-parameter 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 coverage is 0% and the description does not explain any of the 4 parameters. 'targetMonths' is especially unclear—whether it sets a target payoff period or compares scenarios is not addressed.

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 purpose: 'Find out how many months until your credit card is paid off' plus the minimum-payment trap insight. This clearly distinguishes it from generic debt calculators and is action-oriented.

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 versus alternatives like debt_payoff_calculator. The description implies credit-card-specific usage but does not state exclusions or alternatives, leaving the agent to infer 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?

No annotations are provided, so the description must disclose all behavioral traits. It mentions converting inputs to outputs but does not disclose that cropFactor and sensorWidthMm are required inputs, nor how sensorPreset and cropFactor interact. This leaves ambiguity about the calculation behavior and necessary inputs.

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 primary conversion purpose and lists the main inputs and outputs. It is compact and free of fluff, although the opening phrase repeats the tool's title before the substantive content.

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

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

With no output schema, the description should clarify return formats and computational assumptions, but it merely says 'equivalent focal, aperture, and field of view' without specifying units or the type of field of view (e.g., horizontal degrees). It also fails to explain the relationship among sensorPreset, cropFactor, and sensorWidthMm, which are all required inputs, leaving significant gaps for a calculator of this 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 description coverage is 0%, so the description must compensate for parameter meanings. It names 'focal length, aperture, and sensor' but omits cropFactor and sensorWidthMm entirely, and provides no units, constraints, or enum explanation. This gives minimal semantic help beyond the raw schema names.

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 function: converting focal length to a full-frame equivalent using crop factor, and also computing equivalent aperture and field of view. The specific verb 'Convert' combined with the resource scope ('focal length to a full-frame equivalent by crop factor') distinguishes it from sibling calculators like depth_of_field_calculator or aspect_ratio_calculator.

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 by showing the inputs and outputs, but it does not explicitly state when to use this tool versus alternatives. There is no mention of exclusions or contrast with related calculators, so the agent must infer the appropriate 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 provided, the description must carry the full burden of behavioral disclosure. It does add useful context: 'A lower rate with a longer term can still cost you more — see the true lifetime numbers,' revealing that the calculator compares total lifetime costs. However, it does not disclose the return format, assumptions (e.g., minimum payments), or that this is a read-only calculation, leaving some behavioral gaps.

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 short, with two sentences of substantive content. The initial 'Debt Consolidation Calculator —' title repetition is minor filler, but the rest is purposeful and front-loaded. It efficiently communicates the key warning about rate versus term.

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 annotations, no output schema, and zero schema description coverage, so the description must provide almost all context. It gives a high-level purpose and warns about total costs, but it fails to explain input requirements, output details, or how it differs from adjacent siblings like debt_payoff_calculator. This leaves the agent under-informed for correct invocation and interpretation.

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 needs to compensate for the three parameters. It only vaguely references 'current debts' and 'consolidation loan' and alludes to rate and term ('lower rate... longer term'), but it does not explicitly explain the 'debts', 'consolidationAprPct', or 'consolidationTermMonths' parameters or their formats. This minimal compensation is insufficient for full 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 the tool's purpose: comparing current debts against a single consolidation loan. It uses the specific verb 'see how compare' and references the resource (debts and consolidation loan), which distinguishes it from sibling calculators like debt_payoff_calculator or balance_transfer_calculator.

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 context—when considering consolidating multiple debts into one loan—but it does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusions. The sentence 'See how your current debts compare against one new consolidation loan' suggests a scenario but lacks clear 'when to use' 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 must fully disclose behavior. It mentions the comparison and outcomes but lacks details on assumptions (e.g., fixed extra payment, interest compounding) or limitations (e.g., ignoring fees, minimum payment handling).

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, directly states the purpose and outputs, and contains no filler. It is appropriately front-loaded with the tool name and comparison focus.

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 requires an array of debt objects and a numeric extra monthly payment, the description omits critical input semantics and provides no output schema. It also doesn't clarify how the calculator handles multiple debts or what the returned comparison looks like.

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%, and the description does not explain the 'debts' array structure, what 'extraMonthly' means, or how the strategy enum maps to the two named strategies. The description mainly repeats the enum values without adding semantic 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 compares the avalanche and snowball debt payoff strategies, which distinguishes it from other debt-related calculators. It also specifies the outputs: interest saved and months sooner debt-free.

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

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

The description provides clear context for when to use the tool (when comparing avalanche vs. snowball strategies), but it doesn't explicitly mention alternatives or exclusions like credit_card_payoff_calculator or debt_consolidation_calculator.

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 were provided, so the description carries the burden. It discloses the calculation behavior and outputs but omits the wastePct and pricePerBoard parameters as required inputs. There is no contradiction, but the behavior is not fully transparent regarding all inputs.

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 tool's purpose and lists inputs/outputs. It is concise and readable, though omitting two required parameters is a notable gap that prevents 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?

With no output schema, the description lists expected outputs (area, linear feet, board count, total cost) but doesn't explain how waste influences results. The 7 required parameters are not fully documented; the description covers 5 of 7. This is a moderate level of completeness for a calculator with this 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 schema has no parameter descriptions (0% coverage), so the description must compensate. It explains deck length, width, board width, gap, and board length, but omits wastePct and pricePerBoard, which are both required. This partial coverage adds some meaning but leaves key parameters undocumented.

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 deck board quantities, listing specific inputs and outputs. The verb 'calculate' and the resource 'deck boards' distinguish it from sibling calculators like flooring or fence 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 alternative guidance is provided. However, the title and description make the tool's purpose unambiguous for deck board estimation, implying usage context. Sibling tools like flooring_calculator could overlap, but no exclusions are 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 carries the burden of transparency. It does disclose that the output includes near and far limits of sharp focus in meters, which is useful. However, it does not describe behavior for invalid inputs, units for hyperfocal distance, or the role of circle of confusion, leaving some ambiguity.

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 concise sentences with no wasted words. It front-loads the main purpose and adds a concrete output detail, making it efficient and easy to scan.

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 a simple calculator but has no output schema and four required parameters. The description covers the general purpose and one output detail, but omits circleOfConfusionMm as an input and does not clearly state all output values (e.g., hyperfocal distance units). This leaves the description incomplete for a full understanding.

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%, and the description only loosely references focal length, aperture, and subject distance, omitting circleOfConfusionMm entirely. This provides minimal added meaning beyond the schema's self-explanatory property names and fails to compensate for the undocumented required parameter.

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 depth of field and hyperfocal distance with a specific verb and resource, distinguishing it from sibling calculators. However, it omits the required circleOfConfusionMm parameter from the input list, which is a notable gap.

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 is provided on when to use this tool versus alternatives. The description implies its use for depth of field calculations but does not mention any exclusions or compare with related calculators like crop_factor_calculator.

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 disclose behavioral traits itself. It mentions the outcome ('project future value and total growth') but does not state assumptions like compounding frequency, whether the yield is annual, how contributions are timed, or any limitations. This leaves significant ambiguity for a financial projection 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 two sentences and front-loaded with the tool name. It conveys essential information without unnecessary filler, though the opening phrase repeats the title. Overall it's efficiently structured.

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

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

The tool has 5 required parameters, no output schema, and no annotations. The description does not explain the return format, assumptions, or edge cases. It provides only a high-level 'project future value' with insufficient detail for an agent to fully understand what the tool computes or returns.

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. It mentions three of the five parameters ('dividend yield, price growth, and yearly contributions') but doesn't clarify that yields and growth are percentages, what 'principal' means, or how the years parameter is used. The semantics are only partially explained.

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 what the tool does: it projects future value and total growth from reinvested dividends, using inputs like dividend yield, price growth, and yearly contributions. It is specific to DRIP scenarios and distinguishes from generic calculators like compound_interest_calculator, though it doesn't explicitly name alternatives.

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: 'See how reinvested dividends compound your portfolio' tells the agent this is for modeling DRIP growth. It lists the key inputs to provide, which serves as guidance on how to use it, but it doesn't state exclusions or explicitly compare to 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?

The description discloses that the tool uses the AKC size-adjusted method rather than the multiply-by-seven myth, which is useful behavioral context. However, it does not describe the output format, assumptions, or limitations (e.g., age range or size class meanings), and no annotations are provided to cover these aspects.

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 and front-loaded, but the first clause repeats the tool title verbatim. The second sentence is a bit redundant but adds a direct call to action. Overall, it is efficient with minimal waste.

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 provides the core purpose and method, but it lacks details about the return value, output format, or edge-case behaviors. Given no output schema and no annotations, some additional context would be helpful.

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

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

The schema has 0% description coverage, so the description must compensate. It mentions 'age' and 'size' but does not explicitly define age units or explain the sizeClass enum values. However, the parameter names and enum are self-explanatory, and the 'size-adjusted' concept adds 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's function: converting dog age to human years using the AKC method with size adjustments. It explicitly distinguishes itself from the common 'multiply by seven' myth, making it specific and different from sibling calculators like puppy_weight_calculator or pet_cost_calculator.

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 by saying 'Enter age and size for a real estimate,' but it does not explicitly state when to choose this tool over alternatives or mention any exclusions. The context is clear but not explicitly guided.

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 burden of disclosing behavior. It does mention the 20% equity threshold and PMI status, which adds useful context. However, the claim to 'compare multiple down payment scenarios' is ambiguous because the input schema accepts only a single downPct value, potentially misleading an agent about the tool's capabilities.

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 tool's purpose and key outputs, and contains no redundant or extraneous information. It is well-paced and immediately understandable.

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 two-parameter calculator, the description lists the main outputs (down payment amount, loan size, PMI status, gap to 20% equity). However, it does not clarify the multi-scenario behavior, and with no output schema or annotations, the agent lacks a full picture of what the tool returns or how to invoke it correctly for multiple comparisons.

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 provides zero descriptions for the parameters, so the description must compensate. It explains that price and downPct feed into down payment amount, loan size, and PMI status, and introduces the 20% equity threshold, providing meaningful context. However, it does not detail how multiple scenarios would be passed, and units are implied rather than explicit.

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

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

The description clearly identifies the tool as a down payment calculator that calculates down payment amount, loan size, PMI status, and the dollar gap to 20% equity. This distinguishes it from sibling calculators like mortgage_piti_calculator or car_loan_calculator, which focus on different financial aspects.

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?

There is no explicit guidance on when to use this tool versus alternative mortgage-related calculators. The description only states what it does, not when it should be preferred or what prerequisites exist, leaving the agent to infer usage 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 disclose behavior. It states inputs and outputs (sheets, screws, mud, tape), but does not explain rounding, unit assumptions, or confirm it's non-mutating. It is adequate but not rich.

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 entire description is one efficient sentence with a dash, front-loaded with purpose. Every phrase adds information, no fluff.

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 numeric inputs, no output schema), the description covers essentials: purpose, inputs, and expected outputs. It could mention units or calculation assumptions but is complete enough for basic 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?

The schema has no descriptions (0% coverage), so the description compensates by naming each parameter: "total wall and ceiling area" for wallArea, "waste percent" for wastePct, and "sheet size" for sheetAreaSqFt. It adds meaning beyond the parameter names, though it lacks explicit units or examples.

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 its function: "Calculate how many drywall sheets you need for a room" with a specific verb and resource. It also lists inputs and outputs, distinguishing it from other material calculators like tile_calculator or concrete_calculator.

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 sets clear context for use: when you need drywall sheets for a room, and what inputs to provide. However, it does not mention alternative tools or exclusions, so it doesn't earn a 5.

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

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

With no annotations, the description carries full burden. It discloses that both front-end and back-end ratios are calculated and compared to thresholds, which is useful. However, it does not explain output format, data interpretation, or how inputs map to the ratios.

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 concise sentences, front-loaded with the purpose and enriched with threshold context. No unnecessary words or repetition.

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

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

Given the simple three-input, no-output-schema calculator, the description covers the core calculation and threshold comparisons. However, it omits parameter definitions, output specifics, and any assumptions, making it adequate but 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 0%, and the description adds no explicit parameter details. While parameter names are intuitive, the description does not clarify the relationship between housingPayment/otherMonthlyDebts and front-end/back-end ratios, nor does it state that figures are monthly.

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 front-end and back-end debt-to-income ratio', which is a specific verb and resource. It also mentions comparison against specific thresholds (36%, 43% QM, 50%), distinguishing it from other finance 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 for use: to assess debt load relative to common lending thresholds. It does not explicitly list alternative tools or exclusions, but for a calculator with obvious purpose, this context is sufficient.

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 disclose that the tool outputs both contribution margin and fully-loaded margin, which is useful behavioral context. However, it does not explain calculation assumptions, edge cases, input validation, or the structure of the result, which is a notable gap for a complex calculator.

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

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

The description is concise at two sentences, front-loading the primary verb and object. The first sentence defines the calculation and inputs, the second clarifies the output types. Every word earns its place with 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 tool with 8 required parameters and no output schema, the description gives insufficient context for confident invocation. It does not explain how inputs map to the two margin outputs, what the return structure looks like, or how percentages are interpreted. The agent would likely need to infer important details, making it incomplete for a tool of this 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 schema has no property descriptions, so the description must compensate. It explicitly names most cost inputs (COGS, shipping, packaging, payment fees, ad spend) and states they are per-unit, which adds meaning. However, it omits platformFeePct, returnsAllowancePct, and price (though price is implicit), providing only partial coverage of the 8 parameters.

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 identifies the resource as 'true ecommerce profit margin', listing key cost components (COGS, shipping, packaging, payment fees, ad spend) and clarifying the two output types (contribution vs fully-loaded margin). This clearly distinguishes it from sibling calculators like margin_markup_calculator or break_even_calculator.

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 ecommerce profit margin calculation with per-unit costs, but does not explicitly state when to use this tool over related alternatives (e.g., product_pricing_calculator, etsy_fee_calculator). No exclusionary statements or alternative tool mentions are provided, leaving the context implicit rather than explicit.

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 burden. It clearly states the inputs and outputs (cost per day, month, and year), which is transparent behavior. However, it does not disclose any edge cases, assumptions (like constant usage), currency defaults, or error handling, which could be relevant for interpreting results. The description adds context but is 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?

The description is a single, well-structured sentence that front-loads the tool's name and then clearly explains what it does. Every part is relevant, with no filler or repetition. It is immediately understandable and appropriately sized for a simple calculator.

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 calculator with no output schema, the description covers the essential information: inputs, what they represent, and the output granularity (day/month/year). It does not explain the underlying formula, but that is not necessary for typical use. Minor gaps like currency or assumptions prevent a 5, but it is largely 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 0%, so the description must compensate. It does so by naming the parameters ('watts, hours per day, and your kWh rate') and connecting them to the output. This adds meaning beyond the raw schema, helping the agent understand that 'rate' is a per-kWh price and 'hoursPerDay' is daily usage. It could be more explicit about units or rounding, but it is sufficient for correct invocation.

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: to calculate the running cost of an appliance based on watts, hours per day, and kWh rate. It uses a specific verb ('work out', 'see the cost') and identifies the resource ('any appliance'). It is distinct from sibling calculators like ev_charging_cost_calculator by focusing on general appliance cost.

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 clear usage context: anyone wanting to know an appliance's daily, monthly, or yearly cost from basic inputs. It does not explicitly mention alternatives or exclusions, but the use case is so specific and self-contained that alternative selection is unlikely to be confusing. No explicit 'when not to use' is given, but it is not necessary for this straightforward calculator.

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. It does disclose the additive calculation ('add employer FICA, benefits, and overhead') and the output ('total and its multiple of pay'), but it omits critical context like whether values are annual, how ficaPct is applied, or the exact return 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?

The description is a single sentence and front-loaded with the tool's purpose. The only minor issue is redundancy with the title ('True Cost of an Employee Calculator' appears both in the title and at the start of the description), but no information is wasted.

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 4-parameter calculator with no output schema and no annotations, the description gives the essential inputs and outputs but lacks units, annual vs monthly assumptions, and precise parameter semantics. It is adequate for invocation but leaves the agent to infer several operational details.

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. It maps 'employer FICA' to ficaPct, 'benefits' to benefitsValue, 'overhead' to overheadValue, and 'pay' to salary, which helps conceptually. However, it does not clarify that ficaPct is a percentage, nor that benefits/overhead are currency amounts; the schema's names and bounds partially fill this gap.

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 ('Calculate') and a specific resource ('the true, fully loaded cost of an employee beyond base salary'). It further distinguishes the tool by naming components (employer FICA, benefits, overhead) and outputs (total and multiple of pay), making it distinct from basic salary 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 use case is implied: users who need a fully loaded employee cost beyond base salary. However, the description does not explicitly mention alternatives such as contractor_vs_employee_calculator or global_hiring_cost_calculator, nor does it provide when-not-to-use 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 provided, the description carries the full burden. It discloses fixed fee rates ($0.20 listing, 6.5% transaction, 3%+$0.25 payment) and notes the fee breakdown is always visible, adding valuable behavioral detail beyond a generic 'calculate'.

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 concise sentence with a supplementary clause about fee breakdown. It front-loads the core purpose and avoids any 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 mentions the key outputs (net proceeds, break-even price) and fee formula, which is helpful. However, it does not address the six required input parameters, and with no output schema or annotations, the overall guidance is incomplete for an agent to invoke it 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 0%, and the description does not explain any of the six required parameters (e.g., cogs, offsiteAds, processingRatePct). The only values mentioned are fixed fee rates, not input parameters, so the description fails to compensate.

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 function with a specific verb ('Calculate') and resource ('Etsy fees, net proceeds, and break-even price'). The Etsy-specific scope distinguishes it from the many generic calculators among 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 clear usage for any Etsy listing, but does not explicitly state when to use it over alternatives or provide exclusions. The context is unambiguous enough for a calculator tool.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It states the core behavior (computing charging costs) and the outputs (cost per charge and per mile), but it doesn't explain calculation assumptions, units, or limitations (e.g., charging losses, efficiency factors). It adds some context but not rich detail.

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 concise sentences, front-loaded with the tool's title and immediately followed by actionable guidance on inputs and outputs. Every word earns its place with 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?

The tool has 7 required parameters and no output schema. The description high-levelises the key outputs but doesn't explain all inputs, their relationships, or calculation assumptions. It provides a decent overview but is incomplete for a tool of this 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 has 7 parameters with 0% description coverage. The description maps some parameters (battery size to batteryKWh, charge window to currentPct/targetPct, rates to homeRate/publicRate) but omits efficiency and milesPerKWh entirely. It also doesn't clarify units or how rates are used, so the compensation for the coverage gap is insufficient.

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: to calculate EV charging costs at home or on public chargers. It uses a specific verb ('See what it really costs') and resource (EV charging), and differentiates itself from sibling calculators like electricity_cost_calculator by focusing on EV-specific scenarios and outputs (cost per charge and per mile).

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

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

The description provides clear context for when to use the tool: when calculating EV charging costs at home or on public chargers. It doesn't explicitly mention alternatives or exclusions, but the EV-focused framing makes its usage context obvious compared to generic cost 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?

With no annotations, the description must disclose behavior, but it only states the calculation outputs without revealing formulas, rounding behavior, assumptions (e.g., center-to-center spacing, waste factor), or how cost is computed. The phrase 'exact posts, rails, and pickets' suggests precision, but no underlying logic is 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?

The description is a single, focused sentence that front-loads the tool's name and core function. It efficiently conveys key inputs and outputs with no redundant or extraneous text.

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 has 8 parameters, no output schema, and no annotations, the description is too sparse. It fails to explain all required inputs, the output format, cost calculation specifics, or any constraints (e.g., units, rounding). The description covers the broad purpose but leaves substantial gaps for correct invocation and interpretation.

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 0%, and the description only maps three of eight parameters: 'run length' (fenceLength), 'post spacing' (postSpacing), 'picket width' (picketWidthIn). It omits gapIn, railsPerSection, and the price parameters entirely, leaving those meanings vague despite their presence in the schema. Partial compensation is insufficient for 8 parameters.

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: 'Calculate fence materials fast' with a specific resource (fence posts, rails, pickets) and mentions inputs (run length, post spacing, picket width) and outputs (exact posts, rails, pickets, optional cost). This distinguishes it from sibling calculators like deck_boards_calculator or concrete_calculator.

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: users with fence measurements would use this calculator. However, there is no explicit 'when to use this vs alternatives' or exclusion criteria. The description gives a functional hint but lacks direct guidance compared to more explicit alternatives.

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

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

With no annotations provided, the description carries the burden of explaining behavior. It states the calculation outputs (pounds per feeding and per season) and implies a calculation based on inputs, but it does not disclose the underlying formula, assumptions, or how numApplications affects the seasonal total. It is a read-only calculator, so risk is low, but behavioral detail is minimal beyond the output statement.

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. It front-loads the tool name and immediately explains the function and expected outputs, with no filler or repetition. Every part adds value, making it highly 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?

For a simple calculator with no output schema and no annotations, the description is mostly adequate: it explains the purpose, inputs, and outputs. However, it misses the numApplications parameter and does not specify calculation assumptions or the formula, leaving some ambiguity about how the 'per season' output is derived. It is sufficient for a basic understanding but lacks full 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?

The schema has 0% description coverage, so the description must compensate. It maps three of four parameters (lawn size, target nitrogen rate, bag N number) but omits numApplications entirely. It also does not explain units (e.g., areaSqFt is square feet, targetNper1000 is per 1000 sq ft) or provide the formula connecting parameters. The parameter names are self-explanatory to some degree, but the description fails to fully bridge the coverage gap.

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: to calculate fertilizer application amounts based on lawn size, target nitrogen rate, and bag nitrogen percentage. It uses a specific verb ('Work out how much fertilizer to spread') and names inputs and outputs, distinguishing it from the many sibling calculators. It also clarifies the output ('pounds of product per feeding and per season') which fully defines the 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 provides clear usage context by telling the user what to enter and what they will receive. However, it does not explicitly state when not to use this tool or mention alternative calculators, though the domain-specific wording implies the appropriate scenarios. The context is solid but lacks explicit exclusions or alternatives.

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

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

With no annotations, the description carries the full burden. It discloses the core behavior (estimating costs and comparing approaches) but does not explicitly state that it is a read-only calculation, mention assumptions, or note any limitations. The description covers the main functionality but lacks a full safety profile.

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 name and purpose, with no fluff. Every phrase adds value: self-managed vs API, wall-clock time, cheaper path.

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 8 required parameters and no output schema, the description gives an overview but omits detailed input semantics, output format, and calculation assumptions. It is enough to understand the gist but not enough to invoke correctly without inspecting the schema.

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. It references the two cost models (GPU-hours and per-token API pricing), which maps to some parameters, but does not explain individual parameters like datasetTokensM, trainingTokensPerSecAggregate, or apiFineTuneId. The parameter names are partially self-explanatory, but the description falls short of fully compensating for the zero coverage.

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 'Estimate' and identifies the resource ('what a fine-tuning run costs'), clearly distinguishing it from sibling calculators by specifying the comparison between self-managed GPU-hours and managed API per-token pricing, plus wall-clock time and the cheaper path.

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: it is for estimating fine-tuning costs and comparing two pricing models. However, it does not explicitly mention when not to use it or name alternative tools, stopping short of a 5.

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

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

With no annotations, the description must carry the full burden of behavioral disclosure. It only mentions that it calculates a FIRE number and years to retirement, but fails to explain how inputs like withdrawalRatePct and expectedReturnPct are used, what the output format is, or whether assumptions like inflation or taxes are considered.

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, front-loaded with the purpose, and efficiently conveys the core function. The minor redundancy of repeating the tool's title verbatim detracts slightly but does not significantly hurt clarity.

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 six required parameters, no annotations, and no output schema, the description is incomplete. It does not define the FIRE number, explain the calculation methodology, specify output format, or list all required inputs, leaving significant gaps for an agent to infer.

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

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

Schema description coverage is 0%, so the description should compensate. It only mentions current savings, annual expenses, and expected return rate, omitting currentAge, annualSavings, and withdrawalRatePct. It adds minimal meaning beyond the parameter names and does not explain how the inputs interact to produce the FIRE number.

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 ('Calculate') and resource ('your FIRE number and years to early retirement'), which distinguishes it from other calculators like coast_fire_calculator. However, it does not explicitly name sibling tools or edge cases, so it lacks strong 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?

No guidance is given on when to use this tool versus alternatives such as coast_fire_calculator or retirement_monte_carlo_calculator. There are no prerequisites, exclusions, or scenario-based instructions. The phrase 'Enter current savings...' implies usage but does not provide clear 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?

No annotations are provided, so the description carries the full burden. It states it produces an 'estimate' but does not disclose whether it rounds up to whole boxes, how cost is calculated, or any limitations. The title hints at boxes and cost, but the description lacks behavioral specifics.

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 states the purpose and then lists the required inputs. Every word earns its place; no 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 covers the main purpose and inputs, but it omits output details (e.g., rounded box count, total cost calculation) and does not differentiate usage from similar tools. With no output schema or annotations, a bit more context would be needed for full 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 property descriptions are empty (0% coverage), so the description must compensate. It lists all five inputs in natural language, which helps mapping, but adds no units or clarifications beyond what the parameter names imply. For example, it doesn't explain that wastePct is a percentage or that boxCoverageSqFt is square feet per box.

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: to figure out how many boxes of flooring to buy and the cost. It names the specific resource (flooring boxes and cost) and lists the inputs, distinguishing it from other generic 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 intended use is implied by the calculator name and purpose, but there is no explicit guidance on when to use this tool versus alternatives like tile_calculator. No exclusions or alternative references 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?

With no annotations, the description must carry the burden. It explains the tool accounts for taxes, expenses, and profit margin, but does not detail the formula, assumptions, or output format. It leaves ambiguity about how taxes are applied (e.g., gross-up vs. net) or what 'exactly' means.

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 core action. The second sentence reinforces the outcome without adding fluff. Every phrase contributes to understanding 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 tool is a straightforward calculator with six numeric inputs and no output schema. The description conveys the input categories and goal, which is mostly sufficient. It falls short of full completeness by not describing the output formula or key assumptions, but given the simple domain, it is close to adequate.

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. It mentions taxes, business expenses, profit margin, and income goal, which maps to the main parameters. However, it does not explain the role of billableHoursPerWeek and workWeeksPerYear explicitly, nor how profit margin is 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 tool calculates a freelance hourly rate, specifically accounting for taxes, business expenses, and profit margin. This distinguishes it from sibling calculators like salary_to_hourly_calculator, which converts salary to hourly without those additional factors.

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 freelancers who want to set a rate to reach an income goal, naming key inputs (taxes, expenses, profit margin). It does not explicitly mention alternatives or exclusions, but the context is clear enough for typical 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?

With no annotations, the description carries the full burden of behavioral disclosure. It states the comparison and outputs, but does not mention limitations (e.g., ignoring maintenance, insurance, or total cost of ownership) or the role of the optional evPricePremium parameter. This is adequate but not deeply 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 only two sentences and front-loaded with the tool's purpose. However, the first clause simply repeats the title, which is redundant and could have been omitted. It remains efficient 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 lists the key outputs (annual savings, break-even years, 5-year totals) and some inputs, but fails to mention the optional evPricePremium parameter or any assumptions of the calculation. Without an output schema, the description could do more to clarify result structure and edge cases, though it is serviceable for a straightforward 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?

The schema has 0% description coverage, so the description must compensate. It mentions 'miles, MPG, and rates' but does not explicitly map to all six parameters. Importantly, kWhPerMile (electric car efficiency) and evPricePremium are omitted, leaving the agent to infer their purpose from bare parameter names. The added value over the schema is minimal.

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 yearly fuel cost between gas and electric cars, with specific outputs like annual savings, break-even years, and 5-year totals. This distinctly differentiates it from sibling calculators such as ev_charging_cost_calculator or electricity_cost_calculator.

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

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

The description provides clear context for when to use the tool: when comparing gas vs electric car fuel costs. It implies the inputs needed (miles, MPG, rates) and outputs produced, but stops short of explicitly mentioning alternative tools or exclusions, so it doesn't fully meet the top bar.

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 must carry the full burden. It discloses the scope of the calculation (employer taxes, benefits, platform fees), which is useful. However, it does not mention assumptions, limitations, or that this is a read-only estimator. It also fails to describe what the result represents or how it should be interpreted.

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. It front-loads the tool's name and purpose, then uses a dash to expand on the comparison components. There is no redundancy or filler, making it appropriately sized for the information it conveys.

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 (8 required parameters, no output schema, no annotations), the description is insufficiently complete. It does not explain what output the agent should expect, how the comparison is presented, or any key assumptions. The agent is left without guidance on how to interpret the result, making it hard to invoke confidently for a non-trivial 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 0%, and the description only vaguely references 'employer taxes, benefits, and platform fees.' It does not explain the meaning or units of parameters such as usSalary, intlContractorMonthly, eorEmployerBurdenPct, or eorFeeMo. The parameter names are somewhat self-explanatory, but the description fails to compensate for the lack of schema descriptions, leaving the agent to infer how to populate all 8 required fields.

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 'Compare' and clearly identifies the resource: annual cost of a US W-2 employee against an international contractor or EOR hire. This specificity distinguishes it from sibling tools like contractor_vs_employee_calculator, which likely focuses on domestic comparison. The mention of 'global' and 'EOR' further differentiates it.

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 context: comparing three hiring options for annual cost. However, it does not explicitly state when to use this tool over alternatives or provide exclusions. For example, it doesn't say 'use this when analyzing international hires' or 'for domestic comparisons use contractor_vs_employee_calculator.' This is clear context but lacks explicit when/when-not guidance.

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