calc.engineer

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

No annotations exist, so the description carries the burden. It explains the computation of three resistor values and the symmetrical nature. However, it does not detail the output format, though an output schema is present (not shown). Overall, it sufficiently discloses the tool's behavior.

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

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

The description is concise with two substantial sentences and a comparison. Every sentence adds value without waste.

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

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

Given the simplicity (2 parameters, output schema exists), the description is complete enough. It covers the purpose, typical use, and topology. Minor missing details like output format are compensated by the output schema.

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

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

Schema coverage is 100%, baseline 3. The description adds context by explaining that attenuation_db is in decibels, z0_ohm defaults to 50 ohms, and describes the roles of R1, R2, R3. This goes beyond the bare schema.

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

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

The description clearly states it designs a Tee-topology resistive attenuator pad, computes resistor values, and is used in RF signal chains. It distinguishes itself from the sibling tool attenuator_pi by explicitly mentioning the 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 explicit guidance on when to use this tool (for Tee topology) and compares it with an alternative (attenuator_pi for Pi-pad topology), aiding tool 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 provided, so description carries full burden. Discloses outputs (autonomy days/hours, usable capacity, daily deficit) and accounts for multiple factors. Lacks mention of error handling or potential side effects, but as a pure calculation tool, behavior is well-covered.

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

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

Four sentences: purpose, factors, outputs, usage. Front-loaded with critical info, every sentence adds value. 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 6 parameters and existence of output schema, description is complete. Covers inputs, outputs, usage, and chaining context. No missing information for a calculation tool of this complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds significant value beyond schema: explains how to set avg_solar_contribution_pct, provides defaults for round_trip_efficiency (lithium vs lead-acid) and dod for lithium. This enriches understanding of 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?

Clearly states the tool calculates battery autonomy in days, specific verb 'calculates', and resource 'battery bank autonomy'. Distinguishes from siblings by mentioning it chains from solar_sizing and solar_load_audit, implying differentiation from similar tools like battery_charge_time or battery_life.

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 explicit usage guidance: use avg_solar_contribution_pct = 0 for worst-case, 20-30% for cloudy-day modeling. Mentions it chains from other tools, giving context. Does not explicitly exclude alternative tools like battery_life for lifespan calculations, which would strengthen 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 a 'Peukert-adjacent derating' efficiency factor and lists outputs (effective capacity, Wh, runtime). No mention of destructive behavior or auth needs, but the tool is read-only and adequately described.

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

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

Three sentences with no wasted words. The first sentence immediately conveys the core function, followed by efficiency accounting and use cases. Front-loaded 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 4 parameters, no annotations, and an output schema, the description fully covers inputs, behavioral nuance (efficiency), and outputs (capacity, energy, runtime). It also links to sibling tools for modeling completeness.

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

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

Schema coverage is 100% with all parameters described. The description restates key terms (capacity, voltage, current, efficiency) but adds minimal insight beyond the schema, such as clarifying that voltage is nominal and current is average. Baseline 3 is appropriate.

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

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

The description clearly states the tool calculates battery runtime and energy capacity from cell capacity, voltage, and current draw. It distinguishes from siblings by mentioning outputs feed into solar_sizing and ups_runtime, differentiating it from related tools like battery_charge_time.

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?

Lists specific use cases (IoT sensor planning, portable device design, UPS estimation, solar cycling) and mentions downstream integration with other tools. Lacks explicit when-not-to-use or alternative selections, but context is clear.

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

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

Without annotations, the description carries the full burden. It discloses unit system support, formula, and expected outputs (BMI, classification, healthy weight range). No contradictions with annotations (none present). A score of 4 reflects strong transparency with minor room for details like error handling.

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

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

The description is efficient, using two substantive sentences. It front-loads the core purpose and formula, then adds use cases. Every sentence contributes, though the second sentence could be slightly more concise. Still, 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 simple tool (3 parameters, all documented, output schema exists), the description fully enables correct use. It explains the formula, unit support, and return fields. No gaps in understanding are likely.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds marginal value by stating unit system options and formula units, but the schema already describes each parameter. No additional constraints or formatting details are provided 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 explicitly states 'Calculate Body Mass Index (BMI) from weight and height using the WHO standard formula.' It clearly identifies the verb (calculate), resource (BMI), and scope (WHO formula), and distinguishes itself from sibling tools by specifying a unique health-focused 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 mentions use cases ('health screening, fitness planning, and clinical intake forms') but does not provide explicit guidance on when not to use it or compare it to sibling tools. The implied usage is clear but lacks exclusions or alternatives.

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

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

No annotations exist, but the description thoroughly explains the tool's behavior: formulas (RC time constant, target voltage calculation), outputs (time, voltage, energy, current), and parameter constraints (v_target bounds). This fully compensates for missing annotations.

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

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

The description is a single focused paragraph that front-loads the main purpose and lists outputs and use cases efficiently. Slight improvement possible with structured bullet points, but no waste.

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

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

Given an output schema exists (not shown) and the description enumerates all computed quantities (time constant, thresholds, voltages, energy, peak current), the tool is completely specified for a physics calculator with clear parameter constraints and chaining hints.

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

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

Schema coverage is 100% with clear descriptions. The description adds value by explaining the RC time constant formula, the optional target voltage calculation, and example scientific notation, going beyond the schema alone.

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

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

The description clearly states it 'Calculates RC capacitor charge timing, energy storage, and transient voltages' using specific verbs. It distinguishes from siblings by naming rc_filter and timer_555 as tools to chain from.

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 identifies specific applications (timing circuits, power-on reset delays, debounce networks, soft-start designs) and suggests chaining context, but no explicit when-not-to-use guidance is provided.

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

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

No annotations are provided, so the description fully carries the burden. It discloses outputs (utilization percentage, headroom, total packet count) and input requirements. For a pure calculation tool, this is transparent, though it could mention assumptions about the model.

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

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

Description is well-structured: purpose, function, outputs, chaining advice, and importance. Every sentence contributes. Could slightly tighten the first sentence, but overall efficient.

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

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

Given moderate complexity (4 parameters, no enums) and existence of output schema, the description is complete. It covers purpose, inputs, outputs, chaining, and practical significance. No other contextual gaps are apparent.

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 explicitly mapping 'message rate' to messages_per_hour_per_node and 'per-packet airtime' to airtime_ms, and by advising to obtain airtime_ms from lora_airtime. This enriches 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 estimates channel utilization percentage for Meshtastic/LoRa mesh networks, with specific verbs ('Estimates', 'Determines', 'Returns'). It distinguishes from sibling lora_airtime by indicating chaining, and positions itself as essential for deployment 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?

Explicitly advises chaining from lora_airtime to obtain airtime_ms input, providing clear usage context. Does not explicitly state when not to use or list alternatives, but the purpose is well-defined enough to infer appropriate use cases.

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 explains calculations and return values but lacks details on edge cases, limitations, or error handling. It is functionally transparent but not comprehensive.

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, front-loaded with purpose and usage hint. The domain list adds context without being excessive, but one could argue it's slightly verbose. Overall efficient.

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

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

For a simple two-parameter tool with output schema, the description covers usage, precedence, and domains. It does not need to detail return values. 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% and already describes precedence. The description adds 'provide one or both' which is helpful but not substantial. Baseline 3 appropriate.

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

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

The description clearly states the tool calculates circumference and area of a circle given radius or diameter, with precedence rules. It distinguishes from sibling tools which are for other engineering calculations.

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

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

The description mentions providing one or both parameters with precedence, and lists application domains, but does not explicitly state when not to use this tool or compare with 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 details the formula, inputs, and outputs (future value, total interest, APY, schedule). It is transparent about optional contributions and currency-agnostic nature. No hidden behaviors or side effects.

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

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

The description is concise (3 sentences), front-loaded with the formula, and provides essential information without redundancy or 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?

The description covers the formula, all inputs, optional monthly contributions, return values, and use cases. With 5 parameters and full schema coverage, plus an output schema, the description is comprehensive and sufficient for agent usage.

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

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

Schema description coverage is 100%, so the schema already documents each parameter. The description adds context like 'currency-agnostic' and explains the formula, but does not significantly enhance parameter meanings 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 compound interest growth, specifies the formula, and lists inputs. It is distinct from sibling calculators like 'loan_amortization' and 'savings' related tools.

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

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

The description mentions appropriate use cases (savings accounts, CDs, investment returns, retirement planning). It does not explicitly state when not to use, but the specificity is adequate for 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?

The description discloses the algorithm (Abramowitz & Stegun rational approximation for inverse normal CDF) and explains the finite population correction feature. This adds value beyond the input schema, though it could be more explicit about assumptions like normal distribution.

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 a clear first sentence stating purpose, followed by outputs, features, algorithm, and use cases. Every sentence adds value 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 output schema exists, the description need not detail return values but mentions them anyway. It covers algorithm, FPC, and use cases, but could more explicitly state the normality assumption.

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

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

With 100% schema coverage, baseline is 3. The description adds semantic context: sample mean as x-bar, sample size as n, and explains FPC for population_size. It clarifies the default confidence level, enhancing 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 a confidence interval for a sample mean, listing specific outputs. It distinguishes itself from statistical siblings like p_value by focusing on interval estimation.

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 common use cases such as survey analysis, A/B testing, and quality control, providing context for when to use. However, it does not explicitly exclude alternatives or specify when not to use.

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

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

No annotations are provided, so the description carries full burden. It discloses that all electrical power converts to heat, calculates equipment heat output, envelope and solar gain, and recommends AC tonnage, mini-split sizing (rounded to 6K BTU increments), and exhaust fan CFM, giving a comprehensive view of behavior.

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

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

The description is four sentences, front-loaded with purpose and key outputs, and includes essential details without unnecessary repetition. It is well-structured 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?

With 8 parameters and an output schema (not shown but referenced), the description explains inputs and outputs including tonnage, mini-split sizing, and CFM, and provides usage context (after power_cost). It is sufficiently complete for a specialized 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%, so the schema already describes each parameter. The description adds overall context but does not provide additional details beyond what the schema offers for individual parameters, justifying a baseline score of 3.

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

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

The description clearly states it estimates cooling load (BTU/hr) for a homelab or server closet, specifying inputs like equipment wattage, room dimensions, insulation, and solar exposure. It distinguishes itself from sibling tools like power_cost by suggesting use after that tool, making its purpose 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?

Explicitly says 'Use after power_cost to size cooling for your homelab room,' providing clear context for when to use. Does not explicitly state when not to use, but given the narrow domain, it is 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?

With no annotations, the description fully discloses behavioral traits: absolute difference (order irrelevant), a flag indicating if end date is in future, and business days defined as Monday–Friday. No destructive actions or auth requirements are needed for this calculator tool, so coverage is complete.

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 concise sentences, each adding unique information: core functionality, format and behavior, and use cases. No redundancy or fluff. Front-loaded with the main verb and resource.

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

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

The description covers input format, behavioral details, and use cases. Since an output schema exists (not shown but indicated), explanation of return values is unnecessary. The tool is simple and the description is sufficient for an agent to use it correctly.

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

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

Schema coverage is 100%, describing both parameters with patterns and descriptions. The description adds context beyond the schema: it notes that order doesn't matter and explains the future flag. This adds modest but valuable behavioral clarity.

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

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

The description clearly states the tool's function: calculating the difference between two dates in multiple units (days, weeks, months, years, business days). It specifies input format and behavioral nuances (absolute difference, future flag). No sibling tools perform similar date difference calculations, so it stands out.

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 lists relevant use cases (project planning, deadline tracking, contract duration, age computations), providing clear guidance on when to use it. However, it does not mention when not to use or alternatives, though given the sibling tools list, no direct competitors exist.

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

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

With no annotations provided, the description carries the full burden. It discloses handling of various exponent types and that results include numeric and scientific notation. However, it does not mention edge cases like complex results or overflow behavior, which would make it more 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 three sentences long, front-loaded with the core operation, followed by details and use cases. 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 tool's simplicity (two parameters) and presence of an output schema, the description sufficiently explains the tool's behavior and return values. It is complete for the intended use.

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

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

The schema describes both parameters with 100% coverage. The description adds value by explaining the supported exponent types and the output format, going 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?

The description clearly states the tool computes base^exponent and lists supported exponent types (positive, negative, fractional, zero). It also distinguishes itself from siblings like log_calc and scientific_notation by mentioning their relationships.

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 (compound interest, exponential growth/decay, physics power laws, combinatorics) and notes the inverse relationship with log_calc and chaining with scientific_notation, guiding when to use this tool versus 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 discloses that the tool returns radius in metres and feet plus clearance threshold, and explains the significance of the first Fresnel zone. Could be improved by noting input constraints (e.g., positive values), but sufficient for a straightforward calculation.

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

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

Three sentences: purpose, inputs/outputs, practical context. No wasted words; every sentence adds value. Front-loaded with action verb and resource.

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

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

For a tool with 3 parameters, existing output schema, and no annotation gaps, the description explains inputs, outputs, and practical relevance (60% clearance, link_budget integration). No missing information for agent to invoke correctly.

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

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

Schema coverage is 100% with all parameters described. Description repeats parameter info (frequency in MHz, distance in km, zone number 1-5) and adds context about outputs but not new semantic detail beyond schema. Baseline of 3 is appropriate.

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

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

Clearly states 'Calculate the Fresnel zone radius at the midpoint of a radio link.' Specifies inputs (frequency, distance, zone number) and outputs (radius in m/ft, 60% threshold). Distinguishes from sibling 'link_budget' by noting it feeds into that tool.

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

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

Provides clear context for use: calculation of Fresnel zone for path clearance analysis. Mentions practical relevance (60% threshold for obstructions) and relationship to link_budget. Does not explicitly state when not to use, but overall guidance is strong.

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 key behaviors: it calculates yearly and cumulative costs for three scenarios, accounts for multiple cost factors, and outputs specific metrics. It does not disclose assumptions or limitations, but provides sufficient transparency for an 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?

Two sentences, front-loaded with the core purpose, no redundancy. Each sentence adds value: one defines the tool, the second lists outputs and use cases.

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 9 parameters, no annotations, and an output schema, the description covers the tool's purpose, inputs (indirectly through parameter list), and outputs. It could mention that results are structured (e.g., JSON), but the output schema exists to handle that.

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

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

Schema coverage is 100% with detailed parameter descriptions. The description adds context by grouping parameters (e.g., 'fuel cost, generator consumption rate') and clarifying that the tool accounts for various costs. However, this does not significantly surpass the schema's own 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 compares TCO between a fuel generator and solar+battery system over a configurable horizon. It lists specific outputs (yearly costs, savings, breakeven year) and use cases. This distinguishes it from all sibling tools, which are unrelated.

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 lists application scenarios: off-grid site planning, remote telecom towers, construction sites, rural electrification. It does not mention when not to use, but given no direct sibling alternative, this is 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?

With no annotations, the description fully discloses behavioral traits: runs instantly in-worker, uses correlations, lists computed outputs, and notes limitations regarding mesh-level CFD.

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 concise sentences, front-loaded with purpose, then outputs, then usage guidance. Every sentence adds value 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 10 parameters with 100% schema coverage and an output schema, the description adequately explains the tool's purpose, outputs, and limitations, making it complete 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 coverage is 100% with parameter descriptions, so baseline is 3. The tool description does not add significant meaning beyond what the property descriptions already provide.

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

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

The description clearly states the verb 'Analyze', the specific resource 'forced-convection fin-array heatsink', and the method 'channel-flow correlations', distinguishing it from sibling tools like 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?

Explicitly states usage for 'heatsink selection and first-pass optimization in electronics cooling'. Implicitly distinguishes from full CFD by noting mesh-level CFD is planned but not yet available.

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

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

The description discloses that it returns all four representations, bit width, byte count, ASCII character, and signed interpretations. As a read-only converter tool, no side effects are expected, but the description could explicitly state it does not modify any data.

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

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

The description is informative and front-loaded with purpose, but slightly verbose. Every sentence adds value, but it could be trimmed without loss.

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 does not need to explain return values. It covers all necessary aspects for a simple conversion tool with one parameter.

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

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

The description adds significant meaning to the 'value' parameter beyond the schema, explaining the use of prefixes (0x, 0b, 0o) and that plain numbers are decimal, supports negatives. The schema coverage is 100%.

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 hexadecimal, binary, decimal, and octal bases. It provides specific examples and usage contexts, distinguishing it from sibling tools like unit_convert.

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 use cases (embedded programming, register debugging, etc.) and gives an example, implying when to use it. However, it does not explicitly state when not to use it or mention alternatives.

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

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

No annotations are provided, so the description carries full burden. It explains computations, assumptions (e.g., surge estimation as 3x largest load), and outputs (DC input current for cable sizing). While it lacks details on error handling or input validation, the schema covers constraints, making the behavior 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 yet comprehensive, starting with purpose, then detailing computational steps, chaining, and use cases. Every sentence is informative with no redundancy. It fits in a single paragraph and is efficiently front-loaded.

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

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

Given 7 parameters (1 required), an output schema (not shown but present), and no nested objects, the description covers purpose, usage, chaining, and high-level process. It provides enough context for an agent to invoke the tool correctly without ambiguity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining how each parameter is used in calculations (e.g., power factor effect, surge estimation from largest_load_watts, derating for NEC). This goes beyond basic schema descriptions, aiding the agent in proper invocation.

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

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

The description clearly states it sizes a DC-to-AC inverter, specifying the computation of continuous VA rating, surge capacity, derating, and selection of standard size. It distinguishes from siblings like solar_sizing (panel sizing) and battery_autonomy by mentioning chaining from solar_load_audit and to wire_gauge.

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 lists use cases: off-grid solar, battery backup, mobile power systems. It provides chaining guidance (from solar_load_audit, to wire_gauge). It does not explicitly state when not to use, but the context and sibling tools imply clear boundaries. Slightly more specific exclusions would improve clarity.

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 formulas used, the optional nature of resistance, and what Q factor and bandwidth are computed. It could mention that it assumes ideal components, but the information is sufficient for a calculator tool.

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

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

Three sentences total: first states the main purpose, second gives formulas, third lists use cases and chaining option. No wasted words, front-loaded with the primary function.

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 (which covers return values), the description fully explains what the tool does, its formula, use cases, and optional parameters. No gaps remain.

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 parameters are already described. The description adds meaning by explaining the role of each parameter (inductive element, capacitive element, optional series resistance) and how they are used in the computations, providing context beyond the schema's plain 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 uses a specific verb 'Calculates' and clearly identifies the resource: 'resonant frequency of an LC circuit'. It also distinguishes itself from sibling tools by mentioning chaining into '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 such as tank circuits, oscillators, bandpass filters, and antenna matching networks. It does not explicitly state when not to use, but the context is clear. It also hints at related tools via chaining.

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 carries behavioral disclosure. It details computation steps, standard resistor selection, validation, and reported outputs (current, dissipation, voltage). No hidden behaviors.

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?

Well-structured, front-loaded with purpose. Every sentence adds value: computation, standard selection, reports, validation, chaining. No redundancy or 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?

Description covers inputs, validation, output values, and chaining to related tools. With output schema present, return values are fully addressed. Highly complete for a resistor 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 good descriptions. Description adds formula context, E24 selection, and validation logic beyond schema. The mention of series strings adds extra meaning to led_count 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?

Description clearly states tool calculates current-limiting resistor for LEDs, uses formula R=(Vsupply-n*Vf)/I, selects E24 standard resistor, and reports additional values. It distinguishes from siblings (ohms_law, trace_width) by mentioning chaining.

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

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

Explicitly says when to use: driving one or more LEDs in series from DC supply. Mentions validation (supply voltage > total forward voltage). Chaining into ohms_law or trace_width provides alternative context.

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

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

No annotations are provided, so the description carries full burden. It specifies the calculations (EIRP, ITU-R P.525 free-space path loss, received power, link margin) and the condition for margin calculation (optional receiver sensitivity). It does not describe side effects, but as a calculator, none are expected.

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 the main action, and contains no fluff. Every sentence adds meaningful information about purpose, calculations, or usage 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 that an output schema exists, the description need not detail return values, but it still lists key outputs (EIRP, path loss, received power, margin). It also addresses the tool's place in a larger analysis chain, which enriches completeness 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% with clear property descriptions. The tool description adds context about defaults (e.g., antenna gains default to 2.15 dBi) and the role of rx_sensitivity (optional for margin). This provides extra value beyond the schema.

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

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

The description starts with a specific verb+resource: 'Compute a full RF link budget'. It lists what it calculates (EIRP, path loss, received power, link margin) and distinguishes from siblings by mentioning it accepts output from noise_figure_cascade and feeds into snr_margin, differentiating it from fspl and 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?

It states 'Use this to determine whether a wireless link closes with adequate margin', giving clear usage context. It also explains the tool's role in a workflow with sibling tools, though it does not explicitly list when not to use it 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?

No annotations provided, so description carries full burden. Discloses pagination behavior, support for extra payments, and currency-agnostic nature. For a read-only computation, behavior is well-documented.

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, no fluff. First sentence core function, then returns, then extra payment feature, then loan types and pagination. Well-structured 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 output schema exists, description need not explain returns. Covers core compute, optional feature, applicable loans, pagination. Complete for the tool's 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 covers 100% of params, but description adds value: notes extra payment shows months saved/interest saved, mentions common term values (360, 180, 60). Adds beyond 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?

Description clearly states it computes a full amortization schedule with specific outputs (monthly payment, total interest, schedule breakdown). Distinguishes from sibling tools which are unrelated calculators. Verb 'computes' plus resource 'loan amortization schedule' is 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?

Explicitly lists applicable loan types (mortgage, auto, personal, student) and provides pagination usage for schedule array. Does not specify when not to use, but context is clear given no similar sibling tools.

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

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

No annotations are provided, so the description bears the full burden. It reveals the return values (four logarithms), the change-of-base formula used, and implies no destructive side effects. However, it does not explicitly handle error cases (e.g., invalid input) or confirm atomicity, though the schema constraints provide some implicit guarantees.

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

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

The description is three sentences, each adding distinct value: purpose, use cases, and relationships. It is front-loaded with the core function and avoids fluff. Every sentence earns its place, making it highly efficient for an agent to parse.

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

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

Given the low complexity (two parameters) and presence of output schema (though not provided), the description sufficiently covers inputs, outputs, and usage context. It lists the four return types and the formula. Without the output schema text, the agent can infer the structure from the description, making it complete enough for selection and invocation.

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

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

Schema coverage is 100% with detailed parameter descriptions (default base, exclusive minima). The description adds meaning by explaining the tool supports 'any base' and mentions the underlying formula, which complements the schema without redundancy. The use-case examples further clarify parameter intent.

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 logarithms of positive numbers in any base and lists the specific log types (custom-base, ln, log10, log2). It also provides concrete use cases (signal processing, information theory, etc.) and explicitly mentions relationships with sibling tools exponent_calc and scientific_notation, distinguishing its role as a logarithm 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 offers context for when to use the tool (e.g., signal processing, pH chemistry) but does not explicitly state when not to use it or provide alternative tools for other mathematical operations. It mentions related siblings but frames them as complementary rather than alternatives, leaving room for ambiguity about tool 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 provided, so description carries full burden. It details the computation steps: noise floor from thermal noise density, bandwidth, noise figure, then adding SNR thresholds. It lists return values (sensitivity, noise floor, required SNR, thermal noise reference).

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

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

The description is moderately concise with 3 substantive sentences plus a usage line. It front-loads the main purpose. Could be slightly tighter but remains 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 the presence of an output schema (implied by context signals), the description explains the purpose, computation, and usage context fully. No additional behavioral details are needed for a calculator tool.

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

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

Schema coverage is 100%, and each parameter has a description in the input schema. The description adds context about the formula and datasheet source but does not provide new semantic details beyond what's in the schema.

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

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

Description clearly states the tool calculates LoRa receiver sensitivity using spreading factor, bandwidth, and noise figure based on Semtech SX1276 datasheet. It distinguishes from sibling tools by noting it feeds into link_budget and lora_range_estimate.

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 the tool is essential for link budget planning in LoRaWAN and Meshtastic networks and feeds output to other tools. It does not state when not to use it, but context is clear.

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

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

No annotations exist, so description carries full burden. It discloses echoing back input values for chaining. Does not mention destructive behavior (none relevant). Output schema exists, so return values need not be described. Minor lack: no mention of handling edge cases, but schema provides 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?

Description is a single paragraph that front-loads purpose, then gives applications, then mentions chaining. Every sentence adds value, though slightly verbose with multiple application examples. Could be tightened without losing content.

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

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

With full output schema and description covering typical use cases, applications, and chaining behavior, the description is complete. No obvious gaps for a conversion 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 context (nominal voltage, typical battery types) but doesn't elaborate on parameter format or constraints beyond schema. Adequate but not extraordinary given high schema 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?

Clearly states it converts mAh to Wh/kWh/J given voltage, specifying the resource (battery capacity) and action (convert). It distinguishes from sibling tools by mentioning specific applications (airline compliance, solar sizing, UPS) and chaining into 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?

Explicitly tells when to use the tool: for comparing cells rated in mAh against energy budgets in Wh. Lists concrete use cases (airline 100 Wh limit, solar bank sizing, UPS planning) and mentions chaining to other tools, implying when not to use 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 states the calculation method (Hammerstad-Jensen) and mentions optional frequency for wavelength, but lacks details on assumptions, accuracy, or limitations. Adequate but could disclose more behavioral traits.

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 cover purpose, equations, optional feature, and application. No wasted words; front-loaded with key 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?

Description explains return values (width, effective dielectric constant, optional wavelength), references IPC-2141, and provides RF layout context. With an output schema present (context signals), completeness is high. Could add parameter constraints or units, but 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%, baseline 3. Description adds value by explaining parameters in context (target impedance, substrate height, copper weight) and specifying outputs (trace width, effective dielectric constant). Also references equation type and application.

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 microstrip trace width for target impedance using Hammerstad-Jensen equations, specifies outputs (width, effective dielectric constant), and optionally wavelength. It distinguishes itself from sibling tools like 'trace_width' and 'impedance_match' by being microstrip-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?

Description provides context: 'Essential for RF PCB layout... controlled impedance traces (e.g., 50 ohm for SMA connectors).' It implies use cases but does not explicitly state when not to use or compare with alternatives like 'trace_width' or 'impedance_match'.

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 carries full burden. It discloses behavioral assumptions ('Ideal op-amp assumptions'), lists computed outputs (gain in linear/dB, output voltage, input impedance, phase inversion), and explains topology-specific input impedance. Lacks mention of edge cases or constraints but is otherwise 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?

Description is a single paragraph of 6 sentences, front-loaded with purpose, followed by essential formulas, results, assumptions, and chaining suggestions. Every sentence adds value 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 output schema exists, description need not detail return values. It adequately covers all topologies, parameters, and output types. Includes chaining context, making the tool self-contained for the intended use case.

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. Description adds meaning by clarifying resistor roles per topology (e.g., 'For inverting: input resistor Ri. For non-inverting: ground resistor Rg.'), explains optional vin_v and differential vin2_v, and interprets topology enum values.

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 'Calculate voltage gain and output for operational amplifier circuits in inverting, non-inverting, and differential configurations.' It specifies the exact resource (op-amp circuits) and action (calculate gain/output), and distinguishes by providing topology-specific formulas and results (gain in linear/dB, output voltage, input impedance, phase inversion).

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 context for each topology and mentions chaining from ohms_law and resistor_color_code, implying usage after component selection. It does not explicitly state when-not-to-use or compare to siblings, but the context is sufficient for typical scenarios.

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 key behaviors: it is an async container job (submit and poll), models a flat board, computes temperature map and junction temperatures, flags violations. No contradictions. Does not mention rate limits or auth, but sufficient for MCP 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?

Description is 5 sentences, well-structured: first sentence states action, then model details, outputs, usage, and async nature. No superfluous content, but could be slightly tighter. Still efficient.

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

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

Given the tool's moderate complexity (CFD simulation, multiple parameters, async), the description covers all necessary aspects: what it does, inputs (board dimensions, components, airflow), outputs (temperature map, junction temps, violations), and operational note (async). Output schema exists, so return values are documented elsewhere. Complete for selection and invocation.

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

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

All 7 parameters have descriptions in the input schema (100% coverage). The description summarizes parameters as 'board dimensions, component list, and airflow conditions' but adds no further meaning beyond the schema. Baseline 3 is appropriate.

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

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

Description clearly states 'Run a CFD thermal simulation of a PCB with discrete heat sources using OpenFOAM', specifying the action (simulate) and resource (PCB with heat sources). It also distinguishes from sibling tools like heatsink_cfd and thermal_enclosure by focusing on PCB thermal validation and component placement optimization.

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 intended use 'for PCB thermal validation and component placement optimization', giving clear context. It mentions 'Async container job — submit and poll' but does not explicitly state when not to use or list alternative tools. Still helpful 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?

With no annotations, the description fully explains the IPC-2221 formula, assumptions (copper resistivity, plating thickness), and output (parallel vias for target current). It is transparent about the underlying calculations and physical principles.

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 but well-structured: it opens with the main purpose, explains the math, describes outputs, and gives usage context. It is informative without being verbose, though bullet points could improve readability slightly.

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, IPC standard, physics formulas), the description covers all necessary aspects: equations, parameter meanings, practical applications, and integration with sibling tools. It is complete and self-contained.

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%, and the description adds significant value by explaining the physical meaning of each parameter, common values, and how they factor into the IPC-2221 formula. This goes well beyond the schema's defaults and ranges.

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 via current-carrying capacity using the IPC-2221 standard, with specific formulas and use cases like power planes and thermal via arrays. It distinguishes itself from siblings like trace_width by mentioning chaining.

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

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

The description provides clear context for use ('Essential for power planes, high-current paths, and thermal via arrays') and suggests chaining with trace_width. Lacks explicit when-not-to-use scenarios, but the positive guidance is strong.

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 communicate behavior. It specifies the return values (percentage, decimal, fraction) and gives an example. However, it does not mention error handling (e.g., total=0) or edge cases, which slightly reduces transparency.

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

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

The description is concise (two main sentences plus a list) and front-loaded with the core purpose. 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 tool's simplicity, the description covers all necessary aspects: purpose, parameters, return values, examples, and relationships to sibling tools. The presence of an output schema further reduces the need for additional 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 coverage is 100%, so baseline is 3. The description adds value by explaining the roles of 'value' and 'total' as part/whole and providing an example, enhancing 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 'Calculate what percentage one number is of another', using a specific verb and resource. It explicitly distinguishes itself from sibling tools like percentage_increase and test_grade, making the tool's unique 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 explicit guidance on when to use the tool, listing common use cases such as test scores, survey results, and financial ratios. It also suggests chaining with percentage_increase or using with test_grade, giving clear alternatives and context for usage.

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

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

With no annotations, the description carries full burden. It discloses that division by zero is handled gracefully and explains the sign meaning (positive for growth, negative for decline). This provides adequate behavioral insight for a simple 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 three sentences long, front-loaded with the main action, and every sentence adds value—no filler or repetition.

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

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

Given the low complexity (2 simple params) and presence of an output schema, the description covers error handling, sign explanation, and typical use cases. It is fully sufficient for an agent to invoke correctly.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds context (e.g., original/starting, new/ending) but does not significantly extend beyond the schema. Baseline 3 is appropriate.

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

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

The description uses a specific verb 'Calculate the percentage change' and clearly identifies the resource (old and new values). It specifies what is determined (increase/decrease, absolute change, percentage) and lists use cases, distinguishing it from generic percentage tools.

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

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

The description explicitly provides usage contexts such as financial analysis, scientific measurements, performance benchmarks, and population statistics. It does not mention when not to use or directly compare with sibling tools like percentage_calc, but the examples are clear enough for an agent to infer appropriate scenarios.

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 describes behavioral traits: computes target impedance, recommends capacitor types, calculates self-resonant frequency, and checks ESR ripple. No contradictions, but could mention any assumptions or limitations (e.g., ideal lead inductance).

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?

Concise, well-structured, and front-loaded: first sentence states purpose, subsequent sentences detail computation and recommendations. Every sentence adds necessary information 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 need not explain return values. It thoroughly covers input parameters, tool purpose, usage context, and chaining suggestions, making it complete for an agent to invoke correctly.

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

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

Schema coverage is 100%, baseline 3. Description adds significant value by explaining the Z_target formula, providing typical ripple values (50 mV digital, 10 mV analog), and clarifying parameter roles beyond schema documentation.

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

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

Description begins with 'Select bypass and bulk decoupling capacitors for IC power supply pins,' clearly stating the verb (select) and resource (capacitors). It explains the computation and differentiates from siblings by suggesting chaining with lc_resonance and trace_width.

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?

States 'Essential for digital, analog, and mixed-signal PCB design' and provides chain recommendations, offering clear context on when to use. Lacks explicit 'when not to use' or alternatives, but the sibling list implicitly covers other 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?

Despite no annotations, the description fully discloses behavior: it computes the missing side, returns area, perimeter, and confirms right triangle. It emphasizes that side c is always the hypotenuse. 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 concise (~100 words) and well-structured: core purpose, input/output, use cases, and chaining suggestions. Every sentence provides 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 tool's simplicity and the presence of an output schema, the description fully covers what the tool does, how to use it, what it returns, and how to combine with related tools. 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 for each parameter. The description adds meaningful context: only two sides are needed, and c must be the hypotenuse, which is not explicit in the schema.

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

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

The description clearly states the tool's function: 'Solve for any side of a right triangle using the Pythagorean theorem.' It specifies the verb and resource, and differentiates from siblings like slope_calc and square_root by focusing on right triangles and suggesting chaining.

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 says to provide any two sides, and the third is computed. It lists domains (surveying, construction, etc.) and suggests chaining with slope_calc for coordinate geometry or square_root for simplified radicals, providing clear when-to-use and 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, but the description fully discloses behavior: it is a read-only calculator, explains NEC 80% rule, cable management overhead estimation, and output fields. 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 well-structured and front-loaded with the tool's main purpose. While slightly long, each sentence adds value—no filler. Could be slightly more concise but appropriately detailed.

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 an output schema exists, the description adequately lists all key outputs (total_watts, utilization, free space, weights, PDU count) and mentions chaining. For a calculator tool, it provides complete context for 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 3. The description adds meaningful context beyond schema: explains cable management overhead, PDU count logic, and that rack_units defaults to 42. Provides how parameters are used in calculations.

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's a 'quick rack space and weight sizing calculator' for homelab and small data center racks, listing specific inputs (height, weight, wattage) and outputs (utilization, free space, weights, PDU count). It differentiates from siblings by specifying the use case and chainable outputs.

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

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

The description implies when to use (homelab, small data center) and mentions chaining outputs into power_cost and cooling_btu, guiding downstream usage. It does not explicitly exclude alternatives, 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?

Without annotations, the description carries the full burden. It explains the mathematical operations (f=1/(2piRC), tau=RC) and their physical meaning (3dB attenuation, 63.2% transient). It does not mention state modification or authorization needs, which are irrelevant for a computation tool.

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

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

The description is a focused paragraph of 5 sentences, front-loading the core function. It efficiently conveys formulas and usage. The mention of voltage_divider at the end adds context but is slightly tangential; overall it is well-structured without excess.

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