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

With no annotations, the description must carry the full behavioral burden. It usefully discloses the fee model (Basic parts ¥0 feeder fee vs Extended parts +¥20/type) and that it emits optimization suggestions, but says nothing about permissions, failure modes when a part cannot be matched, or response shape.

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 tightly packed sentences with no filler; the primary purpose leads and the fee-model detail follows, so an agent gets the essential meaning from the first clause.

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

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

For a single-parameter compute tool with no output schema, the description conveys the pricing rules and the optimization-suggestion behavior, which is most of what an agent needs. It stops short of describing the returned breakdown structure, which would require an output schema or explicit mention.

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 a single well-documented `queries` array, so the schema already carries parameter meaning. The description adds no format or constraint detail beyond what the schema provides, making the 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 names a specific verb and resource ('Calculate PCBA component cost breakdown') plus a secondary function (JLCPCB SMT surcharge estimation), which is far more informative than the tool name alone. It does not explicitly contrast itself with `search_lcsc_parts`, so it stops short of full sibling differentiation.

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

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

Usage is only implied: an agent can infer this is a costing step after parts are identified, but there is no explicit when-to-use, when-not-to-use, or prerequisite statement (e.g., that component identifiers must first be resolved). No alternatives are named.

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

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

No annotations are provided, so the description carries the full behavioral burden. It adds useful context by naming the equations and the solver behavior, but it never states that this is a pure side-effect-free computation, whether any external lookup occurs, or what the response contains; the core mutability/safety profile is left to inference.

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 tight sentences with the capability and equation basis front-loaded followed by the design targets. No filler or restated title; every clause earns its place.

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

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

For a 6-parameter, no-output-schema tool, the definition omits what is actually returned. It also blurs whether it computes impedance from a given geometry or solves W/S from target_z, and with no annotations covering safety or side effects, some behavioral completeness is missing.

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

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

Schema description coverage is 100%, so the schema already documents all six parameters including defaults and the mode enum. The description's impedance examples echo the schema's target_z description rather than adding new syntax or format detail, so the baseline 3 applies.

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

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

States a specific verb (calculate) plus the exact resource (characteristic/differential impedance for PCB microstrip traces) and names the governing equations (IPC-2141/Wheeler). An agent can immediately distinguish this from synthesize_circuit, validate_netlist, or calculate_bom_cost.

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

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

Gives concrete application context (50Ω RF, 90Ω USB, 120Ω RS485/CAN) and the two supported modes, so the agent knows the domain in which to reach for it. It does not, however, state any exclusions or explicitly compare against 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 carries the full burden. It usefully discloses that the catalog is fixed at 10 pre-validated entries with no filtering or parameters, but says nothing about return shape, ordering, or whether the list can change between calls — a modest gap for a trivial read-only listing.

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

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

A single front-loaded sentence with no filler; the key fact (all 10 pre-validated blocks) is stated immediately and nothing else is needed.

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 zero-param, annotation-free listing tool this covers the essentials, but with no output schema the agent still doesn't know what a returned CircuitBlock looks like or how to use it downstream. Adequate but with a clear gap.

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 tool takes zero parameters, so the schema has nothing to document and the baseline of 4 applies. The description correctly implies a parameterless, unfiltered enumeration.

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 pairs a clear verb ('List') with a specific resource ('CircuitBlocks') and adds scope ('all 10 pre-validated... in the hardware DSL catalog'). It does not explicitly contrast itself with siblings like synthesize_circuit, but no confusion arises given the distinct name and catalog framing.

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

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

There is no statement of when to use this tool versus alternatives, nor any prerequisites or exclusions. At best the 'pre-validated catalog' framing implies you'd call it to discover available blocks before synthesizing, but nothing is stated explicitly.

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

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

With no annotations, the description carries the full burden and does disclose real behavioral traits: zero API keys required and a 24-hour disk cache, which warns the agent that stock/pricing could be up to a day stale, plus exponential backoff implying retry handling. It stops short of describing the return shape or failure modes.

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 tight sentences, front-loaded with the core capability and followed by operational caveats. Every clause earns its place with no redundancy.

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

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

For a stateless search tool with no annotations and no output schema, the definition covers capability, data freshness, and reliability posture adequately. It could go further by hinting at what a result record contains or how misses are reported, but nothing critical for invocation is missing.

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

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

Schema coverage is 100%, so both the query format (part number/model/description with examples) and the limit default are already documented structurally. The description adds no parameter-level detail beyond that, which is the expected baseline when the schema does the work.

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?

Specific verb 'Query' paired with a concrete resource: LCSC/JLCPCB inventory, stock, package, and pricing. The domain is distinct from the circuit-synthesis and validation siblings, though it never explicitly names or contrasts with calculate_bom_cost, the nearest neighbor.

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

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

No statement of when to reach for this tool versus calculate_bom_cost or the other siblings, and no prerequisites beyond the implied 'supply a query'. The cache/backoff notes describe behavior, not usage conditions.

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

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

With no annotations, the description carries the full burden, and 'deterministic' is a genuinely useful behavioral claim (same prompt yields the same netlist). However, it omits other traits an agent needs for a generative tool: expected latency/cost, whether output is persisted or returned inline, auth requirements, and failure modes for unsupported targets.

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 the core action and output, with the capability list last. Every sentence conveys information, though the long comma-separated enumerations are dense and could be compressed.

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 complex generative tool with no annotations and no output schema, the description covers input scope and output artifacts but leaves the return shape, downstream consumption, and error behavior unspecified. Adequate as a minimum, but not 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 coverage is 100% for the single 'prompt' parameter, setting the baseline at 3. The description adds meaning beyond the schema by enumerating the supported MCUs and peripheral categories, effectively defining what content a valid prompt may contain. It stops short of giving prompt syntax or format examples.

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

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

States a specific verb (synthesize) and concrete outputs (deterministic hardware netlist and component modules) plus the input source (natural language prompt). The enumeration of supported MCUs and peripherals further bounds what the tool produces, making it clearly distinguishable from siblings like validate_netlist or search_lcsc_parts.

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

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

No statement of when to use this tool versus its siblings, nor of prerequisites (e.g., that it is typically the first step before validate_netlist or calculate_bom_cost). Usage is only implied by the verb 'synthesize'. The only guidance-like content is the supported-target list, which constrains inputs rather than routing the agent.

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

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

No annotations are provided, so the description carries the full burden, yet it discloses nothing beyond the basic action. It does not say whether validation is strict or permissive, what happens on failure, or whether the call mutates anything — important for a tool operating on a nested object.

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

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

A single front-loaded sentence with no waste; the action and target lead immediately. It is efficient, though so terse that it leaves out behavior an agent would otherwise need.

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

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

With no output schema and no annotations, the description should ideally say what a validation call returns (validity flag, error list) and how failures surface. Given the simple one-parameter schema and nested-object input, it is minimally adequate but leaves the result-handling gap open.

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 a single, clearly documented 'netlist' property, so the schema does the heavy lifting and the baseline is 3. The description adds only that validation targets the formal CircuitAgent JSON Schema, but no format or shape detail beyond what the schema already states.

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

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

The description names a specific verb ('Validate') and resource ('hardware netlist dictionary') plus the validation standard (CircuitAgent JSON Schema), so the agent knows exactly what the tool does. It is naturally distinguishable from the unrelated siblings (synthesize_circuit, calculate_bom_cost), though it does not explicitly name an alternative.

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

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

There is no statement of when to call this versus other tools, no prerequisites, and no indication of what to do with a failing netlist. Usage is only implied by the verb 'Validate'.

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