calc.engineer

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

Though no annotations exist, the description adequately conveys the tool's behavior: it computes resistor values for a symmetrical Pi-pad network. However, it could mention assumptions (e.g., symmetrical network) or limitations (e.g., attenuation range constraints).

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

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

Three sentences, efficient and front-loaded with the key purpose. No wasted words.

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

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

Given the output schema exists, the description adequately covers the tool's functionality for a simple calculator. It mentions resistor values and topology, and provides comparison context.

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

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

Schema coverage is 100%, so baseline is 3. Description adds meaning by naming resistors (R1, R3 shunt, R2 series) and mentioning default impedance, but doesn't provide additional format or constraints beyond schema.

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

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

Description clearly states it designs a Pi-topology resistive attenuator pad, computes three resistor values, and distinguishes from sibling tool attenuator_tee by mentioning T-pad topology.

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

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

Explicitly instructs to compare with attenuator_tee for T-pad topology, providing clear guidance on when to use this tool versus the alternative.

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 states the tool computes resistor values and is used in RF signal chains. It does not detail any side effects, constraints (e.g., valid attenuation range), or computational behavior beyond the basic calculation. This is adequate 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 composed of 4 concise sentences. It front-loads the purpose, then provides input details, and ends with a sibling comparison. Every sentence adds value without redundancy.

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

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

The tool has an output schema (context indicates true), so return values are covered there. The description covers inputs, topology, usage context (RF), and comparison with Pi attenuator. For a simple calculation tool, this is complete and sufficient.

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

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

Schema coverage is 100% with both parameters described. The description adds meaning by explaining the roles of the three resistors (series R1,R3 and shunt R2) in the Tee topology, relating the parameters to the physical design. This goes beyond the schema definitions.

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

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

The description clearly states the tool's purpose: 'Design a Tee-topology resistive attenuator pad.' It specifies the computation of three resistor values for a symmetrical T-pad network, given attenuation and impedance. It also distinguishes from the sibling tool 'attenuator_pi' for Pi-pad topology.

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: it computes resistor values for Tee attenuators, maintaining impedance match. It mentions comparing with attenuator_pi for alternative topologies. However, it does not explicitly state when not to use this tool or list exclusion criteria, missing some 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 provided, but the description discloses outputs (days/hours autonomy, usable capacity, daily deficit) and accounts for depth of discharge, efficiency, min SOC, and partial solar. It is transparent about assumptions and 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?

Three sentences with no wasted words. The main purpose is front-loaded. Every sentence adds distinct value (core calculation, parameters, usage advice, chaining 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 the presence of an output schema (not shown but signaled), the description adequately covers behavioral context, parameter guidance, and usage scenarios. It could mention that inputs must be positive (schema enforces exclusiveMinimum), but overall complete for a 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 100%, yet the description adds value by explaining default dod (0.8 for lithium), round-trip efficiency default (0.9 lithium, 0.8 lead-acid), and usage of avg_solar_contribution_pct. This goes beyond the schema's basic 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 begins with a specific verb ('Calculates how many days a battery bank can sustain loads without solar input') and clearly identifies the resource (battery bank autonomy). It distinguishes from sibling tools like battery_charge_time and battery_life by focusing on autonomy under no solar input.

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?

Description provides explicit advice on using avg_solar_contribution_pct (0 for worst-case, 20-30% for realistic) and mentions chaining from solar_sizing and solar_load_audit. It does not explicitly state when not to use, but the context is clear.

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

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

With no annotations, the description carries full burden. It explains that real-world charging losses (80-90% efficient) are accounted for, outputs include time in hours/minutes, energy in Wh, and C-rate for safety. It also mentions chaining from solar_sizing. It does not contradict annotations and provides useful behavioral context.

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

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

The description is efficient, with about 5 sentences front-loading the primary function, then outputs, use cases, and safety note. Every sentence adds value; no fluff. Slightly longer than necessary, but not wasteful.

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 presence of an output schema (from context signals), the description focuses on what matters: inputs, calculations, use cases, and safety. It covers all necessary aspects for a calculation tool of moderate complexity.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds context (e.g., 'typically 80-90% efficient' for efficiency parameter) but does not significantly enhance meaning beyond schema descriptions. It mentions outputs like C-rate which tie to parameters, but that's not direct 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 clearly states 'Estimates battery charge time given capacity in mAh, charge current in mA, charger efficiency, and initial state of charge.' It lists specific outputs (time, energy, C-rate) and distinguishes from sibling tools like battery_autonomy and mah_to_wh by mentioning specific use cases like solar sizing and EV 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 provides explicit use cases: 'solar charge controller sizing, USB charging time estimation, lead-acid float charging, and EV battery planning.' It also notes safety guidelines for C-rate. However, it does not explicitly state when not to use this tool or provide alternatives, which prevents 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?

No annotations provided, so description carries full burden. It discloses efficiency derating and outputs (effective capacity, energy, runtime). However, does not explain mathematical model limitations or assumption of constant current. Adequate but not thorough.

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

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

Three sentences, each earning its place: action/inputs, behavior/outputs, use cases. Front-loaded with verb 'Calculates'. 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 complexity (4 params, no nested objects, output schema exists), the description covers inputs, outputs, use cases, and downstream tools. It is complete for an agent to understand when and how to use the tool.

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

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

Schema coverage is 100% with descriptions for each parameter. The tool description adds value by explaining the purpose of efficiency, default voltage for Li-ion, and what outputs are produced. This contextualizes parameters beyond schema.

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

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

Description clearly states 'Calculates battery runtime and energy capacity' with specific inputs and outputs. It distinguishes from siblings by mentioning integration with solar_sizing and ups_runtime, but lacks explicit contrast with other battery tools like mah_to_wh or battery_autonomy.

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?

Provides use cases (IoT, portable devices, UPS, solar) and mentions downstream tools. However, no guidance on when not to use or alternatives (e.g., for simple conversion use mah_to_wh).

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, but the description fully explains the behavior: it is a read-only computation with no side effects. It specifies formula, unit handling, and output data. No destructive or auth concerns arise.

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, concise and well-structured. It front-loads the core purpose and adds helpful detail without verbosity.

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

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

Given the tool's simplicity and the presence of an output schema (context signal), the description covers all necessary information: inputs, units, formula, outputs, and use cases. It is fully adequate for an AI 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 100%, so baseline is 3. The description adds context about units and formula but does not provide significant additional semantics beyond the schema descriptions.

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

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

The description explicitly states it calculates BMI using WHO standard formula, supports metric/imperial units, and lists outputs (BMI value, classification, healthy weight range). This clearly distinguishes it from sibling tools, which are all other 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 mentions use cases: health screening, fitness planning, clinical intake forms. While it doesn't explicitly state when not to use or alternatives, the sibling list contains no other BMI calculator, so 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?

No annotations provided; description carries full burden. It discloses formulas, outputs, and optional target voltage calculation. No destructive behavior expected, so transparency 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?

Three sentences pack all essential information: purpose, outputs, optional behavior, and use cases. No fluff; every sentence earns its place.

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

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

Output schema exists, covering return values. Description covers inputs, purpose, and usage context completely for a calculation tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by summarizing formulas and relationships (τ=R×C, E=½CV², t= -τ ln(...)), providing context beyond individual parameter descriptions.

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

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

The description clearly states the tool calculates RC capacitor charge timing, energy storage, and transient voltages, listing specific outputs. It distinguishes from sibling tools by mentioning chaining from rc_filter and timer_555, providing a specific verb and resource.

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

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

It explicitly lists use cases (timing circuits, power-on reset delays, debounce networks, soft-start designs) and suggests chaining from rc_filter or timer_555, giving clear context. Missing explicit when-not-to-use, but adequate.

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

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

No annotations provided, so description carries full burden. It mentions estimation and returns, but does not explicitly state no side effects. However, as a calculator, this is acceptable.

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?

Five sentences efficiently convey purpose, inputs, output, usage chain, and context. No redundancy, well-structured.

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

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

Given output schema exists, description adequately covers return values and context. It explains the tool's role in deployment planning without missing critical information.

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 100% coverage, but description adds value by explaining where airtime_ms comes from and noting default duty cycle. This supplements schema descriptions.

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

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

Description clearly states the tool estimates channel utilization percentage for Meshtastic/LoRa mesh based on node count, message rate, and airtime. It distinguishes from sibling tools like lora_airtime by focusing on utilization calculation.

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?

Description advises chaining from lora_airtime for airtime_ms input and states it's essential for deployment planning. While it lacks explicit when-not or alternatives, the guidance is clear and helpful.

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

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

The description discloses the core formulas (C=2πr, A=πr²), the return fields (circumference, area, radius, diameter), and the precedence rule. No annotations are provided, so the description carries the full burden. It adds useful behavioral context but could mention validation of inputs (e.g., negative numbers).

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, using a few sentences without any fluff. The main purpose is front-loaded in the first sentence, 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 the tool's simplicity and the presence of an output schema (not provided but noted), the description is sufficiently complete. It covers the purpose, parameter usage, precedence, and example domains. Minor gaps exist (e.g., output units) but are not critical.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds context about radius precedence and application areas, but does not significantly extend beyond the schema's 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 the tool calculates circumference and area of a circle given radius or diameter. It uses specific verbs and resources, and is distinct from sibling tools which are various engineering 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 explains that users can provide one or both of radius/diameter, with radius taking precedence. It lists multiple use cases in engineering and everyday tasks. However, it does not explicitly state when not to use the tool or mention alternatives.

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

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

No annotations are provided, so the description must carry the full burden. It discloses the computation formula, returned values (future value, interest, APY, schedule), and support for monthly contributions. It does not mention side effects or permissions, but as a calculation tool, it is effectively read-only and non-destructive.

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: three sentences with no redundant information. It front-loads the formula and key output, then adds optional contribution detail and use cases. Every sentence earns its place.

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

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

Given the tool has an output schema, the description does not need to detail return values. It sufficiently covers parameters, use cases, and the optional contribution feature. The description is complete for a financial calculation tool of moderate complexity.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds context for compounds_per_year (listing allowed frequencies) and describes monthly_contribution as optional, but does not significantly extend the meaning beyond the schema's property 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 it calculates compound interest growth, names the formula, and lists the outputs (future value, total interest, APY, schedule). It distinguishes from sibling tools like loan_amortization by specifying use cases (savings, CDs, investment returns, retirement 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 provides clear use cases and mentions optional monthly contributions for savings projections. It does not explicitly state when not to use this tool or name alternatives, but the context signals a variety of sibling tools, and the description effectively guides usage for financial growth projections.

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 transparency. It discloses the algorithm ('Abramowitz & Stegun rational approximation for the inverse normal CDF') and the effect of finite population correction. It does not mention any side effects, but as a calculation tool, none are expected. The description is sufficiently 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 concise with two well-structured sentences. The first sentence states the core function and outputs, and the second adds important details about FPC and the algorithm. Every sentence earns its place with no wasted words.

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

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

Given the tool has 5 parameters and an output schema, the description provides enough context for correct invocation. It explains the purpose, inputs, special features (FPC), and expected outputs. It is complete for an AI agent to understand and use the tool correctly.

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

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

Although schema description coverage is 100%, the description adds meaning beyond the schema by explaining the role of finite population correction, the FPC parameter, and the approximation method. It also lists the computed outputs. This adds value beyond the basic 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 the tool's purpose: 'Calculate the confidence interval for a sample mean.' It specifies the inputs (sample mean, size, standard deviation, confidence level) and outputs (margin of error, bounds, critical z-score, standard error). This distinguishes it from sibling tools, which are various other 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 usage context: 'Common in survey analysis, A/B testing, and quality control.' This implies appropriate scenarios but does not explicitly state when not to use the tool or contrast with alternatives. It lacks explicit exclusions or sibling differentiation.

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 fully bears the burden of transparency. It explains the core principle ('All electrical power converts to heat'), details the calculation components (equipment heat, envelope gain, solar gain), and mentions output recommendations and rounding. This gives the agent a clear understanding of the tool's behavior and assumptions.

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 composed of two efficient sentences with no extraneous information. It is front-loaded with purpose and inputs, then explains the calculation and output, and ends with usage guidance. Every sentence is essential and well-organized.

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

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

Given the complexity (8 parameters, output schema exists), the description covers the tool's purpose, inputs, calculation logic, and usage order. It does not list output fields explicitly, but that is acceptable since an output schema is present. Minor gaps: no mention of limitations or accuracy expectations, but overall adequate for an informed 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 100%, so parameters are already documented. The description adds value beyond the schema by explaining the overall model and how parameters contribute to the calculation (e.g., 'equipment heat output, envelope heat gain through walls, and solar gain'). This contextualizes the parameters without repeating schema details.

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

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

The description clearly states the tool's purpose: estimating cooling load (BTU/hr) for a homelab or server closet. It specifies inputs (equipment wattage, room dimensions, insulation, solar exposure) and outputs (total BTU/hr, AC tonnage, mini-split sizing, exhaust fan CFM). It distinguishes from siblings by mentioning use after power_cost, indicating a specific role in a workflow.

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

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

The description suggests when to use the tool: 'Use after power_cost to size cooling for your homelab room.' This provides sequential context but does not explicitly state when not to use or list alternatives. However, for a specialized calculation tool, this is sufficient guidance.

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

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

With no annotations, the description carries full burden. It discloses key behaviors: absolute difference regardless of order, a future flag, and business days defined as Monday–Friday. This provides good transparency for a calculation tool.

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

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

The description is two sentences, front-loads the core action, and includes all necessary context without redundancy. Every sentence earns its place.

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

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

Given the low complexity (2 required parameters, no nested objects, output schema present), the description fully covers input format, output characteristics, and business day definition. It is complete enough for an agent to use correctly.

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

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

Schema coverage is 100% and both parameters have clear descriptions in the schema. The description adds value by explaining output behavior (absolute difference, future flag, business days) but does not add new parameter-level semantics beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description begins with a specific verb-resource combination ('Calculate the difference between two dates') and lists multiple units (days, weeks, months, years, business days). This clearly distinguishes it from sibling tools, which are almost all engineering/physics 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 explicitly provides use cases (project planning, deadline tracking, etc.) and notes that the order of dates doesn't matter. While no alternatives are mentioned, the tool's purpose is sufficiently scoped to avoid confusion among non-date siblings.

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

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

No annotations are provided, so the description carries full burden. It discloses default impedance (50 ohms) and mentions conversion to multiple units. However, it lacks details on precision, error handling, or any limitations, resulting in adequate but not comprehensive transparency.

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

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

The description is three sentences long, front-loaded with the main conversion action. Every sentence adds value: purpose, dBm definition, use case, and default. No redundant or extraneous 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?

Given the presence of an output schema and the simplicity of the tool (2 parameters, no nested objects), the description is largely complete. It covers purpose, usage context, and defaults. Could explicitly clarify it is a pure conversion tool versus related compliance tools, but overall 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 documentation coverage is 100%, so parameters are already well-described. The description adds context (default impedance matching most RF systems) but does not provide significant additional meaning beyond the schema.

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

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

The description clearly states the tool converts dBm to milliwatts, watts, dBW, and RMS voltage. It specifies the resource (power levels) and action (convert), and differentiates itself from sibling RF tools by focusing on a specific conversion need.

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 concrete use cases: interfacing datasheets with circuit analysis, verifying power amplifier output, receiver sensitivity, and regulatory EIRP limits. It does not explicitly mention when not to use or alternatives, but the context is clear enough.

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

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

No annotations provided, so description carries full burden. It discloses return units, gain characteristics, and relationship between half-wave and quarter-wave. For a simple calculator, this is adequate.

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

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

The description is concise, with the main purpose in the first sentence, and additional details in subsequent sentences. No unnecessary words.

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

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

Given output schema exists, description doesn't need to explain return values. It covers antenna types, usage, and units, which is sufficient for this tool.

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

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

Schema coverage is 100%, but description adds context by explaining what half-wave and quarter-wave antennas are and that freq_mhz is operating frequency, enhancing understanding beyond schema.

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

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

The description specifies the tool calculates physical dimensions of a dipole antenna for a given frequency, returning lengths in metres, feet, and inches. It also distinguishes between half-wave and quarter-wave types, which is clear and specific.

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

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

The description explicitly states to use this tool before building or purchasing an antenna to verify element length. While it doesn't mention when not to use, the context makes it clear it's for antenna dimension calculation, and siblings do not overlap.

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?

Despite no annotations, the description thoroughly explains the tool's behavior: it computes max messages per hour, min interval, effective data rate, and flags dwell-time-limited regions. It details region-specific constraints (ETSI 1%, FCC dwell time) without contradictions.

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

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

Two well-structured sentences, front-loaded with purpose, no fluff. Every sentence adds value.

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

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

Given the output schema exists, the description sufficiently covers inputs, processing logic, and outputs for a moderately complex tool. It provides region-specific rules and chaining guidance.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema's parameter descriptions; it only reiterates the region's duty cycle details and the airtime_ms source.

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 specifies the verb 'calculates' and the resource 'LoRa duty cycle budget', listing specific regulatory regions and computed outputs. It clearly distinguishes from sibling tools like lora_airtime and lora_param_optimizer.

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 advises chaining from lora_airtime to obtain the required airtime_ms input, providing clear usage context. However, it does not explicitly state when not to use this tool or mention alternatives.

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

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

No annotations are provided, so the description carries full burden. It discloses the return values: compliance status, margin in dB, and warning message. It explains the computation logic (computes EIRP from tx power, antenna gain, cable loss). There is no mention of destructive behavior, but as a computation tool, that 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 dense paragraph that efficiently conveys all needed information without redundancy. It is front-loaded with the main purpose. Minor improvement: could be broken into bullet points for readability, but current structure is effective and earns its sentences.

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 5 parameters, all described, and an output schema (though details not in provided context), the description covers the return values and usage context. It mentions chain validation with meshtastic_range, which provides operational context. It is sufficient for an agent to understand and 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 input schema has 100% coverage with descriptions for all parameters. The description adds extra meaning beyond the schema: it notes that tx_power and antenna_gain come from meshtastic_range for chain validation, and provides typical values (e.g., 'Stock Meshtastic antennas: 2-3 dBi'). This aids parameter selection.

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

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

The description clearly states the tool checks EIRP compliance against regional regulatory limits for LoRa and Meshtastic operation. It specifies computation of EIRP from transmit power, antenna gain, and cable loss, and comparison against multiple regional limits. This distinguishes it from sibling tools, which are all different 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 explicitly mentions it is 'Critical for Meshtastic deployments using aftermarket high-gain antennas' and that it accepts parameters from meshtastic_range for chain validation. This provides clear context on when to use the tool, though it does not explicitly state when not to use it.

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

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

Since no annotations are provided, the description fully carries the burden of disclosure. It explicitly states supported exponent types, return of numeric result and scientific notation string for extremes, leaving no 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 four sentences with no redundancy. It leads with the core operation, then covers capabilities, use cases, and relations to sibling tools, all 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?

Given the tool's simplicity (two parameters with full schema coverage) and presence of an output schema, the description is complete. It explains behavior, use cases, related tools, and output format, leaving no gaps.

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

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

Schema coverage is 100% with descriptions, so baseline is 3. The description adds value by specifying that fractional and zero exponents are handled, and that output includes scientific notation for extreme values, enhancing understanding beyond schema.

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

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

The description clearly states the tool computes base^exponent, specifying it handles positive, negative, fractional exponents and zero. It distinguishes itself from siblings like log_calc and scientific_notation, confirming a specific, well-defined 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 lists concrete use cases (compound interest, exponential growth/decay, physics power laws, combinatorics) and explicitly identifies log_calc as the inverse and scientific_notation as a chaining tool, providing clear guidance on when and with what to use this tool.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It transparently describes the computation and the 60% clearance threshold concept, explaining that obstructions within 60% cause significant signal degradation. It does not mention any destructive actions, side effects, or authentication needs, but as a pure calculation tool, none are expected. The description adds valuable context beyond a simple calculation 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 remarkably concise: two sentences plus a final relational sentence. The first sentence immediately states the core purpose ('Calculate the Fresnel zone radius at the midpoint of a radio link'), which is front-loaded. Every sentence adds value—inputs, outputs, practical significance, and relation to link_budget—without any 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 calculation tool with a well-defined input schema and an output schema (implied), the description provides complete context: it explains what the tool does, what inputs are needed, what outputs are returned (including the 60% threshold), and how it fits into a broader engineering workflow. It also briefly educates on the first Fresnel zone concept. Given the tool's complexity, this is comprehensive.

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

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

The input schema already provides comprehensive descriptions for all three parameters (freq_mhz, dist_km, zone_number) with constraints and defaults, achieving 100% coverage. The description adds context about the first Fresnel zone and the 60% threshold, but does not elaborate on individual parameter semantics beyond what the schema provides. According to the rubric, high schema coverage sets a baseline of 3, and the description does not significantly elevate this.

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

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

The description clearly states the action: 'Calculate the Fresnel zone radius at the midpoint of a radio link.' It specifies inputs (frequency, distance, zone number) and outputs (radius in metres and feet, plus 60% clearance threshold). The verb 'Calculate' paired with the specific resource 'Fresnel zone radius' makes the purpose unambiguous. While it doesn't explicitly differentiate from siblings like fspl or link_budget, the focus on Fresnel zone is unique and distinct.

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: it feeds into link_budget for path clearance analysis, suggesting it is a component in a larger workflow. However, it does not provide explicit guidance on when to use this tool versus alternatives (e.g., fspl for free-space path loss, or link_budget for overall analysis). There are no 'when not to use' statements or exclusions, leaving some ambiguity for the agent.

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

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

With no annotations provided, the description carries full burden. It discloses that the tool assumes ideal line-of-sight with no obstacles, reflections, or atmospheric absorption, and also computes RF wavelength. This provides clear behavioral context for the agent.

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, front-loaded with purpose, and every sentence adds value. No redundant or vague statements.

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 nature of the tool, the schema covers parameters, output schema exists, and the description explains the ideal conditions and connection to link_budget. It is complete for an agent to understand usage and expectations.

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 covers 100% of parameters with clear descriptions (distance in km, frequency in MHz, exclusiveMinimum: 0). The description adds marginal value by restating that frequency is carrier frequency and distance is between Tx and Rx, but does not significantly enhance parameter understanding 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 the tool calculates free-space path loss in dB using the ITU-R P.525 standard, given frequency in MHz and distance in km. It explicitly distinguishes from sibling tools by noting it feeds into link_budget for full end-to-end analysis.

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

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

The description provides explicit usage context: 'Use this to estimate baseline path loss before adding fade margins, antenna gains, or environmental corrections.' It also mentions it feeds into link_budget, but lacks explicit when-not-to-use guidance or limitations beyond ideal conditions.

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 responsibility. It thoroughly explains the tool's behavior: it calculates yearly/cumulative costs, accounts for multiple factors, and outputs specific metrics. No mention of auth or rate limits, but as a calculator, this is acceptable.

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 main purpose. It covers inputs, outputs, and use cases in a few sentences without redundancy. Slight room for tightening, but well-structured.

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

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

Given the presence of an output schema, the description need not detail return values. It fully covers the tool's domain, inputs, and applications, providing sufficient context for an AI agent to decide when to use it.

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

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

Schema coverage is 100% with each parameter described. The description adds value by summarizing the parameter groups (fuel cost, consumption, etc.) but does not provide additional insight beyond the schema. Baseline 3 is appropriate.

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

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

Description clearly states the tool compares total cost of ownership between a fuel generator and a solar+battery system over a configurable time horizon. It specifies calculated outputs and real-world applications, distinguishing it from sibling tools like solar_roi or battery_autonomy.

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 listing typical applications (off-grid site planning, remote telecom towers, etc.). However, it does not explicitly state when not to use the tool or compare it to alternative solar tools from the sibling list, which would improve guidance.

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

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

The description explains the correlation-based method and states it runs instantly, which is transparent. Without annotations, it carries the burden; it could be improved by noting assumptions (e.g., developing laminar flow) but is still adequate.

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

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

The description is concise (four sentences) with no unnecessary words. It front-loads the purpose and includes key details like method, outputs, and run-time behavior.

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 (10 parameters, no annotations, but has output schema), the description covers the purpose, method, outputs, and use case comprehensively. It is complete for a calculation tool.

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

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

Parameter descriptions are fully covered in the input schema, so the description adds minimal per-parameter detail. The formulas mentioned (e.g., fin efficiency) provide background but not additional semantics for each 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 analyzes a forced-convection fin-array heatsink using correlations, with specific outputs like thermal resistance and optimal spacing. It is distinctly different from sibling tools such as pcb_thermal or thermal_enclosure.

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

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

It specifies use for 'heatsink selection and first-pass optimization in electronics cooling', providing clear context. However, it does not explicitly mention when not to use or compare to alternatives like detailed CFD.

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 full burden. It discloses input formats, output details (including bit width, ASCII, signed interpretations), and support for negatives. Missing edge case handling but sufficiently 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 well-structured, starting with the main action, then details, then example. It is informative but could be slightly more 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 single-parameter tool with no annotations and an output schema (implied), the description fully explains input format and output contents, making it complete.

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

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

Schema coverage is 100% with parameter description. The tool description adds value by explaining prefix conventions and negative number support, going 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 the tool converts numbers between bases (hex, binary, decimal, octal) and lists additional outputs. It distinguishes itself from sibling calculator tools by specifying its unique function.

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 contexts (embedded programming, register debugging, etc.) but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria.

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

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

With no annotations provided, the description carries the full burden of behavioral transparency. It discloses that the tool computes shunt and series components, selects topology based on higher impedance side, and provides the Q factor formula. It does not cover limitations (e.g., only real impedances) or side effects, but the disclosed behavior is substantial.

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 well-structured, starting with the primary purpose and proceeding to details. Every sentence contributes value, including the Q factor note, topology selection, and application guidance. No filler.

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

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

Given the tool's complexity and the presence of an output schema (not shown but reported), the description is complete. It explains the design methodology, Q factor, topology selection, and usage context. No gaps evident.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by explaining the role of source and load impedances (real, resistive) and frequency, and notes constraints (must differ). This context enriches the 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 that the tool designs an L-network impedance matching circuit between two real impedances at a given frequency, specifying the computation of shunt and series reactive components and automatic topology selection. It distinguishes itself from siblings by mentioning chaining with lc_resonance and applications in antenna and amplifier matching.

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

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

The description provides explicit context for when to use the tool, such as for antenna matching, amplifier input/output matching, and maximizing power transfer. It suggests chaining from lc_resonance. However, it does not explicitly state when not to use 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 explains core behaviors: it computes, estimates, applies derating, selects standard size, calculates DC current. It does not explicitly state that it is a read-only computation (no mutation), but the nature of the tool implies it. No contradictions.

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

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

The description is a single paragraph that efficiently conveys all key aspects. It is front-loaded with the primary purpose. However, it could be broken into shorter sentences or bullet points for easier scanning.

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

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

The description is comprehensive given the tool's complexity (7 parameters, no annotations, output schema exists). It covers inputs, algorithm, outputs (though output structure not detailed), and chaining suggestions. An agent can fully understand what the tool does and how to use it.

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

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

Schema has 100% coverage. The description adds value by explaining how parameters interact (e.g., surge estimation uses largest_load_watts, derating_pct is NEC 20%, power factor affects VA). It clarifies default behaviors and chaining sources for continuous_watts and largest_load_watts.

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

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

The description starts with 'Size a DC-to-AC inverter' and explicitly lists the computations (VA rating, surge, derating, standard size, DC input current). It distinguishes from siblings by mentioning chaining from solar_load_audit and to wire_gauge/wire_ampacity, which are present in the sibling list.

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

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

Clearly states usage scenarios: off-grid solar, battery backup, mobile power systems. It provides chaining guidance (from solar_load_audit, to wire_gauge). However, it does not explicitly mention when not to use this tool or list direct 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?

No annotations provided, so description carries full burden. It explains that optional resistance yields Q factor and bandwidth, mentions the formula, and implies the behavior of the tool when resistance is omitted. However, it does not discuss edge cases like extreme values or precision.

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?

Four sentences covering purpose, formula, applications, and optional feature. Front-loaded with the main action. Could be slightly tighter, but no wasted words.

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

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

Given the presence of an output schema (assumed to define return values), the description adequately explains inputs and outputs. It also provides chaining context. For a calculation tool with moderate complexity, this is sufficiently complete.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining how each parameter contributes to the calculation (e.g., 'series resistance for Q factor and bandwidth') and provides formulas, going beyond schema descriptions.

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

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

The description clearly states the tool calculates resonant frequency, Q factor, and bandwidth for an LC circuit. It uses a specific verb ('calculates') and resource ('LC circuit'), and distinguishes itself from siblings by mentioning chaining with impedance_match.

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

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

The description provides explicit use cases (tank circuits, oscillators, filters, antenna matching) and suggests chaining to impedance_match. It lacks explicit 'when not to use' guidance, but the context is clear enough.

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

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

With no annotations, the description fully discloses behavior: computes exact resistance, selects nearest E24 standard, reports actual current, power dissipation, voltage across resistor, and validates voltage. No contradictions.

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

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

Four sentences, front-loaded with purpose, then computation, outputs, validation, and chaining. Every sentence adds value without redundancy.

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

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

Given the presence of an output schema, the description adequately covers inputs, validation, outputs, and related tools. It is complete for an electronics calculation tool.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value beyond schema by explaining the formula, standard resistor selection, and output reports, though it does not describe each parameter in 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 calculates the current-limiting resistor for LEDs in series from a DC supply, names the formula, and specifies outputs. It is distinct from sibling tools by focusing on this specific task.

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 usage context, including validation of supply voltage and suggestions to chain with ohms_law or trace_width. It does not explicitly list when not to use or compare to all alternatives, but the guidance 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?

No annotations provided, so description carries full burden. It specifies the underlying standard (ITU-R P.525) and explains how the optional rx_sensitivity_dbm parameter affects margin calculation. Does not mention side effects, but for a calculator tool this is adequate. The description is transparent about what it computes.

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

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

Two sentences, front-loaded with purpose and detailed outputs. Every word earns its place; no redundancy or fluff. Clear and efficient.

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

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

Given that an output schema exists (not shown but mentioned), the description does not need to detail return values. It covers main use case and workflow integration. Minor gap: does not state assumptions (e.g., free-space propagation, no obstacles), but overall complete for a calculator tool.

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

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

Schema description coverage is 100%—every parameter has a description. The description provides overall context (computing link budget) but does not add additional per-parameter details beyond what the schema already provides. Baseline 3 is appropriate for high 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 clearly states it computes a full RF link budget, listing specific outputs (EIRP, path loss, received power, link margin). It distinguishes from sibling tools like noise_figure_cascade and snr_margin by mentioning workflow integration (accepts output from noise_figure_cascade, feeds into snr_margin).

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

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

Explicitly states 'Use this to determine whether a wireless link closes with adequate margin.' It also provides workflow context (accepts output from noise_figure_cascade, feeds into snr_margin), helping the agent understand when to use this tool in a sequence. No explicit exclusion criteria, but clear 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 fully informs about tool behavior: computation, returns, support for extra payments with savings breakdown, and pagination. It discloses currency-agnostic nature and no destructive actions, meeting the full burden.

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

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

The description is four sentences, front-loaded with core purpose, and each sentence adds distinct value (outputs, extras, loan types, pagination). No fluff or redundancy.

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

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

Given 4 parameters and an output schema (confirmed by context), the description covers inputs, outputs, optional extra payment effects, loan types, and pagination. It is complete for an agent to decide and 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 100%, so the schema already documents all parameters. The description adds minimal extra semantics (e.g., currency-agnostic, pagination hint) but does not significantly enhance parameter understanding 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 the tool computes a full loan amortization schedule given principal, rate, and term, listing specific outputs. It distinguishes itself from sibling tools which are mostly electronic/engineering calculators, making its purpose unambiguous.

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

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

The description provides concrete usage context by listing applicable loan types (mortgage, auto, personal, student) and mentions pagination for schedule output. It does not explicitly state when not to use or name alternatives, but given the sibling list, this is sufficient for an agent.

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

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

With no annotations, description fully discloses behavior: computes four logarithms via change-of-base formula, requires positive input. No contradictions or hidden 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?

Three tightly written sentences: function, use cases, tool connections. Front-loaded with purpose, every sentence adds value.

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

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

Given low complexity and presence of output schema, description fully covers purpose, usage, behavior, and relations to siblings. No missing information for effective 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 already describes both parameters (100% coverage). Description adds context about outputs but no new parameter-specific details beyond 'positive number' which matches 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?

Description uses clear verb 'Compute logarithms' and specifies resource 'positive number in any base'. Distinguishes from siblings by mentioning inverse operation (exponent_calc) and order-of-magnitude (scientific_notation).

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?

Provides concrete use cases (decibel calculations, entropy, pH) and explicitly links to sibling tools for related operations. Lacks explicit 'when not to use' guidance, preventing a perfect score.

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

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

Since no annotations are provided, the description carries full burden. It discloses the underlying formula, computed intermediate values (symbol duration, preamble time, etc.), parameter influences, and integration with another tool. No contradictions.

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

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

Two well-structured sentences that front-load the main purpose and formula, then detail computed outputs and use cases. No extraneous words. Every sentence adds value.

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

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

Given the presence of an output schema (context signals), description does not need to explain return values. It covers all parameters, their typical values, usage context, and integration with sibling tools. Complete for a calculation tool.

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

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

Schema coverage is 100%, baseline is 3. Description adds significant value by explaining each parameter's role in the computation, providing typical values (e.g., 'LoRaWAN uses 8' for preamble_symbols), and specifying auto-calculation for low_data_rate_optimize.

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

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

Clearly states it calculates LoRa time-on-air using a specific formula (Semtech AN1200.13). Verb 'Calculates' and resource 'LoRa packet time-on-air' are specific. Lists computed outputs and use cases. Differentiates from siblings like lora_param_optimizer by mentioning capacity planning and integration with channel_utilization.

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

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

Explicitly states it is 'essential for capacity planning in LoRaWAN and Meshtastic mesh networks' and 'feeds airtime_ms to channel_utilization for mesh load analysis.' Provides clear context for when to use but does not explicitly mention when not to use 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 fully carries the burden. It discloses iterative search over all SF/BW combos, respect for regional TX power limits and duty cycle, and ranking by priority. No contradictions.

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

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

The description is a single paragraph of ~100 words, dense with information. It front-loads the main purpose and is efficient, though could be slightly more structured with bullet points.

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 (6 params, 3 enums), the description covers optimization logic, constraints, and outputs. An output schema exists (though not shown), so return values need not be detailed. Complete for effective 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 coverage is 100%, so baseline is 3. The description adds process context (iteration, ranking) but not additional per-parameter details beyond what the schema already provides with enums and defaults.

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

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

The description clearly states the tool 'Recommends optimal LoRa spreading factor, bandwidth, and TX power' and distinguishes it from sibling tools like lora_airtime and lora_range_estimate by specifying its iterative optimization over SF/BW combinations.

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 with 'Essential for tuning Meshtastic and LoRa deployments' but does not explicitly state when not to use or compare to alternatives like lora_range_estimate. It does mention the optimization priorities, aiding 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 discloses the calculation method, empirical nature of terrain corrections, and caveats about real-world variation. It adds behavioral context beyond what the input schema provides.

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 main purpose, and structured logically without redundant 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 full schema coverage, no required parameters, and the existence of an output schema, the description adequately explains the tool's functionality, inputs, and output (range, margin, loss) without needing further detail.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds overall context about the link budget equation but does not significantly enhance semantics beyond schema defaults.

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

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

The description clearly states the tool estimates LoRa maximum communication range by computing free-space path loss link budget with terrain corrections, distinguishing it from siblings like lora_sensitivity or lora_airtime.

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 mentions usefulness for Meshtastic deployment planning and coverage mapping, providing context. It does not explicitly state when not to use or compare with alternatives, but the context is clear enough for selection.

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

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

No annotations are provided, so the description carries full burden. It details the computation steps (noise floor calculation, adding SNR thresholds) and lists the return values (sensitivity, noise floor, required SNR, thermal noise). This is transparent for a pure calculation tool.

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

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

The description is concise (5 sentences) and well-structured: it starts with the core function, explains the computation, lists outputs, provides usage context, and mentions integration with other tools. Every sentence adds value.

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

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

Given the tool has 3 parameters, defaults, and an output schema (implied by context), the description covers the calculation logic, output fields, and practical use cases. It is complete for a physics-based calculator tool.

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

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

Input schema has 100% coverage with descriptions for each parameter. The tool description reiterates the inputs but does not add new per-parameter semantics beyond what the schema provides. With full schema coverage, a baseline score of 3 is appropriate.

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

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

The description clearly identifies the tool's function ('Calculates LoRa receiver sensitivity') and specifies the inputs (spreading factor, bandwidth, noise figure) and the underlying model (Semtech SX1276). It distinguishes itself from sibling tools by providing a precise 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 states that the tool is essential for link budget planning in LoRaWAN and Meshtastic networks and mentions it feeds into 'link_budget' and 'lora_range_estimate'. This provides clear usage context. However, it does not explicitly exclude alternative tools or scenarios, missing some 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 fully covers behavior: it converts capacity, echoes inputs for chaining, and produces multiple output units. It does not mention side effects or destructive actions, which is appropriate for a read-only conversion.

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

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

The description is four sentences, each contributing value: core function, common use, specific applications, and chaining capability. It is front-loaded and efficient 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?

Given the tool's simplicity and the presence of an output schema, the description provides thorough context: conversion types, use cases, and integration with downstream tools. No critical information is missing.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds context about the conversion purpose and outputs but does not significantly enhance parameter understanding 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 the tool converts mAh to Wh, kWh, and J given voltage, using specific verbs and resource. It distinguishes from siblings by calling it the most common battery unit conversion and listing related tools like battery_life, solar_sizing, ups_runtime.

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

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

The description explicitly mentions when to use (comparing mAh cells to energy budgets) and lists concrete use cases (airline compliance, solar sizing, UPS planning). It does not explicitly state when not to use, but the context and sibling list imply it's specialized for battery conversions.

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 convey behavioral traits. It discloses that the tool models sleep/RX/TX current draw, uses device profiles, and returns time distribution and daily Wh. As a calculator, it is implicitly read-only and non-destructive, but the description does not explicitly state that it does not modify state or require authorization.

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 totaling about 100 words. The first sentence immediately states the tool's purpose and scope. The second sentence elaborates on role behavior and outputs. No filler or redundant information; every sentence adds value.

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

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

Given the tool's 8 parameters, enums, and existence of an output schema, the description adequately covers core functionality and context (e.g., role duty cycles, solar_sizing integration). It does not delve into every parameter detail, but the schema covers those. Minor gap: it doesn't explain output schema fields explicitly, but that is covered by the output schema itself.

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

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

All 8 parameters have schema descriptions (100% coverage), so baseline is 3. The tool description adds meaningful context beyond enums: it explains that 'role' controls duty cycling (router vs. client behavior), mentions peripheral toggles (GPS, BLE, display) affect current draw, and notes that 'channel_preset' impacts TX airtime. This enriches the param 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 explicitly states the tool calculates 'Meshtastic node power consumption and battery runtime' using specific device profiles and duty cycling models. It lists five device types and four roles, clearly distinguishing this from sibling calculators like 'solar_sizing' and 'battery_life' by referencing Meshtastic-specific terminology.

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

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

The description provides clear context on when to use the tool (for power and runtime estimation) and how role selection affects duty cycling (e.g., routers stay in RX mode, clients sleep). It also mentions feeding output to 'solar_sizing'. However, it does not explicitly exclude scenarios where alternative tools would be more appropriate, leaving some ambiguity.

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 exist, so the description carries the full burden. It explains the combination of device-specific TX power/antenna gain, firmware presets, terrain correction, automatic parameter population, and the output (range, sensitivity, link budget, effective parameters). This is thorough behavior 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 two sentences long, front-loading the main purpose. It is efficient but could be slightly improved with structured lists for readability. Still, every sentence adds meaningful information without 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 complexity (8 parameters, output schema existence), the description covers tool purpose, input logic, output details, and use case. There are no gaps; it is complete for an AI agent to understand when and how to use this 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?

Input schema covers all 8 parameters (100% coverage). The description adds value by explaining that parameters are automatically populated from device/channel selection and that overrides are optional, which clarifies the interaction between 'device'/'channel_preset' and the custom 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 specifies the verb 'estimates', the resource 'Meshtastic node communication range', and details the combination of hardware profiles and channel presets. It clearly distinguishes from sibling tools like 'lora_range_estimate' by being Meshtastic-specific.

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

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

The description states 'Essential for Meshtastic deployment planning and node placement', providing clear context for use. However, it does not explicitly mention when not to use or list alternatives, though the purpose inherently differentiates from generic LoRa 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?

Discloses use of closed-form equations and optional wavelength calculation. No annotations are provided, so the description bears full burden. However, it does not discuss limitations like frequency range, accuracy, or edge effects, 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?

Three sentences, well-structured and front-loaded. Every sentence adds value without redundancy. Efficient and clear.

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?

Covers inputs, equations, outputs, and references IPC-2141 with an example. Complex tool with output schema present. Could mention applicability domain (e.g., frequency range) but overall complete enough for most use cases.

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

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

Schema coverage is 100%, so the schema already documents parameters. The description adds a little context (e.g., 'default 4.6 for FR-4') but does not significantly enhance understanding 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 the tool calculates PCB microstrip trace width for target impedance using Hammerstad-Jensen equations. It specifies input parameters and outputs, and distinguishes from sibling tools by focusing on microstrip geometry.

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?

Provides context that it's essential for RF PCB layouts to achieve controlled impedance, with an example (50 ohm for SMA). Does not explicitly exclude cases like stripline or give alternatives, but the context is clear enough.

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

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

No annotations are provided, so the description carries full burden. It exhaustively discloses behaviors: calculates effective bandwidth after TCP/IP overhead, total required bandwidth, utilization percentage, remaining headroom, flags saturation at 80%, identifies network vs. storage bottleneck, provides 1TB transfer reference, and recommends next upgrade. This is highly 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 concise paragraph that front-loads the purpose and follows with key details. Every sentence adds value, though a slightly more structured format (e.g., bullet points) could improve readability. Still, it is efficient for an AI agent.

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

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

Given the tool has 5 parameters and an output schema (not shown), the description covers all essential aspects: what inputs are used, what calculations are performed, and what outputs are provided (e.g., utilization, headroom, bottleneck detection). It leaves no major 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 100% (all parameters have descriptions). The tool description adds context beyond the schema, such as explaining that utilization flags at 80% and that the tool recommends upgrades. This enhances understanding of parameter roles, warranting a score above baseline.

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: 'Plan and validate network link capacity for homelab workloads.' It specifies the verb (plan/validate) and resource (network link capacity), and distinguishes itself from sibling calculators by focusing on network throughput 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 explains when to use the tool (e.g., 'Determine whether a 1G, 2.5G, 10G, or faster link can handle your concurrent streams'). While it does not explicitly state when not to use it or name alternatives, the context is clear enough for an AI agent to select it appropriately.

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 explains the calculation method (Friis formula) and why first stage dominates, disclosing the mathematical behavior. It does not mention any side effects or destructive actions, 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?

Four sentences, front-loaded with purpose, each sentence adds value: purpose, formula mention, educational context, integration hint. No unnecessary words.

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

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

Given one parameter and output schema, the description covers what it does, how, why it works (first stage dominance), and how it fits into a larger workflow (link budget). This is complete and actionable.

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

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

Schema coverage is 100%, so baseline is 3. The description reiterates that noise figure and gain are in dB but adds no new semantics beyond what the schema already documents for each 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 cascaded noise figure using Friis formula, specifies the resource (multi-stage receiver chain), and distinguishes from sibling tool link_budget by stating it feeds into that tool for complete sensitivity analysis.

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

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

The description explains when to use this tool (as a precursor to link_budget) and provides context about LNA placement. However, it lacks explicit when-not or alternative tool guidance, which would make it even clearer.

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 equations used (V=IR, P=VI, etc.) but does not mention error handling behavior (e.g., what happens if fewer than two parameters are provided, or if the two parameters are inconsistent). It does not describe output format, precision, or units beyond the input schema. For a simple calculation tool, this is adequate but not exceptional.

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 extremely concise with only three sentences. The first sentence immediately states the tool's core function, the second adds equations and applications, and the third provides chaining guidance. No words are wasted, and the structure is front-loaded with essential information.

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

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

Given that the tool only performs a simple calculation based on two parameters, the description covers its purpose, equations, applications, and integration with related tools. The input schema fully documents the parameters, and the presence of an output schema (though not shown) likely covers return format. All necessary context for correct use 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 coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining the relationships between parameters (the equations) and the real-world applications (sizing resistors, current draw). This helps the agent understand how the tool works and when to use specific parameter combinations, earning a higher score.

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

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

Description clearly states it solves Ohm's Law and power equation given any two of four electrical quantities. It lists the four quantities (V, I, R, P) and the standard equations used (V=IR, P=VI, etc.). It also distinguishes from sibling tools by mentioning chaining into led_resistor or voltage_divider, establishing a unique 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?

Description explicitly says it is 'fundamental for every circuit design task' and lists practical use cases (sizing resistors, estimating current draw, etc.). It also suggests chaining into led_resistor or voltage_divider for specific applications. However, it does not explicitly state when NOT to use this tool or provide alternative tool names for cases that require different physics.

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 fully discloses behavior: it lists output items (gain in linear/dB, output voltage, input impedance, phase inversion), details formulas per topology, and states ideal op-amp assumptions. This gives the agent a complete understanding of the tool's results and limitations.

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 well-structured: it starts with the core purpose, gives formulas for each topology, lists computed outputs, notes assumptions, and ends with chaining suggestions. No superfluous words; every sentence adds value.

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

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

Given the tool's complexity (5 parameters, 3 topologies, multiple outputs) and that an output schema exists (though not shown), the description covers all essential aspects: formulas, outputs, assumptions, and usage context. It is complete for an agent to correctly 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?

The input schema already has descriptive parameter descriptions (100% coverage). The tool description adds further value by explaining how parameters like ri_ohm are interpreted differently across topologies (e.g., inverting: input resistor, non-inverting: ground resistor). This enriches the agent's understanding 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 the tool calculates voltage gain and output for op-amp circuits, specifying three topologies (inverting, non-inverting, differential). It distinguishes itself from sibling tools, which are diverse engineering calculators, by being the dedicated op-amp gain 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 guidance on chaining with related tools (ohms_law, resistor_color_code) for power budgeting and component selection. It also notes ideal op-amp assumptions, implying limitations in non-ideal scenarios. However, it does not explicitly state when not to use this tool (e.g., for non-ideal or complex op-amp models).

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 fully discloses behavioral traits: it performs DRC and ERC checks, returns 'honest unrouted-net list', a 'manufacturable' flag, SVG layers, and optional Gerber. It also warns that the auto-router is a first-pass attempt, managing expectations.

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 thorough yet efficient, front-loading the main purpose and then detailing key features. Every sentence adds value—no redundancy or filler. It 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?

Given the presence of an output schema (mentioned but not shown), the description covers all necessary aspects: input, process, output, and usage guidance. It explains the full workflow from netlist to manufacturing files, making it complete for a complex tool.

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

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

Schema coverage is 100% with descriptions, but the tool description adds extra context for several parameters: e.g., 'board' is auto-sized if omitted, 'gnd_pour' treats GND as bottom-layer pour, 'placement' is optional and overrides auto-placement. This enhances understanding beyond the schema.

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

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

The description uses a specific verb ('Turn a SPICE netlist into a fab-ready 2-layer PCB') and lists key actions (assign footprints, auto-place, route, verify with DRC/ERC). It clearly distinguishes from siblings such as spice_simulate by noting the same netlist is used for layout after simulation.

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

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

Explicitly states when to use manual placement ('Supply a 'placement' array for production-quality boards') versus auto-placement ('the auto-router is a first-pass best-of-N-seeds'). Also connects to sibling tool spice_simulate ('Same netlist you simulate with spice_simulate'), guiding the agent on context.

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