China Sourcing Audit

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

With no annotations provided, the description carries the burden of behavioral disclosure. It communicates that this is an informational checklist and adds provenance ('Based on 20 years of China manufacturing experience'). It does not explicitly disclose limitations, such as the fact that it performs no live supplier verification or that results are general guidance rather than supplier-specific.

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

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

The description is concise and front-loaded: the main action and audience appear in the first sentence, followed by a scannable list of checklist topics and a single usage directive. Every sentence contributes value; the '20 years' line is slightly promotional but supports credibility without bloating the description.

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

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

The tool is simple, has an output schema, and the description covers purpose, content, and timing, so a lot is already in place. However, the unexplained use_case parameter and the lack of any differentiation from sibling audit/verification tools leave meaningful gaps for an agent trying to select and invoke this tool with full confidence.

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 single parameter, use_case, has 0% schema description coverage and is not explained in the tool description. The phrase 'first-time importers' loosely aligns with the default value 'first_order', but an agent gets no guidance on what values are accepted or how use_case affects the checklist. The optional default softens this, but the semantics remain undocumented.

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

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

The description clearly states the tool's action and resource: it retrieves a 'complete 7-point China sourcing audit checklist' for first-time importers, and it enumerates the topics covered. It is specific and easy to understand, though it does not explicitly contrast itself with siblings like full_supplier_audit or verify_supplier.

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 a clear usage context: 'Use before sending any deposit to a new supplier.' This tells an agent when the tool is appropriate. However, it does not mention when not to use it or point to alternative tools, so it stops short of full routing 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 the transparency burden. It discloses the operational method ('issuer website lookup of cert numbers') and makes clear the tool provides verification guidance rather than claiming to perform a live authentication. It could mention limitations or data-source reliability, but the core behavior is transparent.

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

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

Two tight sentences with no filler: the main action is front-loaded and the motivating use case follows immediately. 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?

Given one optional parameter, no annotations, and an output schema, the description supplies enough context: purpose, trigger, and verification method. It could be slightly more complete by explaining its relationship to verify_supplier or explicitly noting that it gives verification guidance rather than a guarantee, but these are minor 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 description coverage is 0%, so the description must compensate for the sole parameter. It connects 'product type' to the certification lookup, but it never explicitly documents acceptable values, examples, or the behavior when the default empty string is used. The agent can infer the parameter's role, but the semantics are not fully specified.

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

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

The description opens with a specific action ('Check which certifications...') and names the exact resource: CE/FCC/FDA requirements for EU/US import plus certificate authenticity verification. This clearly distinguishes it from broader siblings like verify_supplier or audit_checklist.

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?

'Use when unsure if a supplier's certificate is genuine' provides an explicit trigger, and the warning about fake CE certificates and customs seizures adds practical context. It stops short of naming sibling alternatives or stating when not to use the tool, so it doesn't earn a 5.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly reveals that one call executes five sequential stages, returns a consolidated verdict, and is cheaper than separate calls. It does not mention failure modes or side effects, but for a read-style audit this is reasonably transparent.

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

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

The description is front-loaded with the core value proposition and the five stages are listed compactly. Some marketing phrasing like 'AI-commerce one-stop gate' and redundancy with 'complete decision' add slight noise, but overall it is efficient and well organized.

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

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

For a tool with eight parameters, no schema descriptions, and no annotations, the description is incomplete for practical use. It explains what the tool does at a high level but leaves the agent to guess how parameters like dimensions, deposit percentage, goods value, and shipping mode affect the audit stages and final verdict.

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

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

Schema description coverage is 0%, so the description must compensate by explaining parameters, but it does not mention width_cm, weight_kg, shipping_mode, deposit_pct, goods_value_usd, or any input semantics. Only company_name is implied by the audit context, leaving the agent without essential guidance for correct invocation.

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

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

The description identifies a specific verb and resource: one call runs the complete 5-stage pre-purchase decision and returns a unified risk verdict. It clearly distinguishes itself from the sibling single-purpose tools by framing it as the complete decision gate, not a single lookup.

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 states this tool is for agents that need a complete decision, not a single lookup, and contrasts it with calling five tools separately. This gives clear when-to-use guidance and names the alternative approach, even if the sibling tool names are not enumerated.

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

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

With no annotations provided, the description carries full responsibility for behavioral transparency. It discloses that the tool only provides estimates, not official customs quotes, and exposes underlying assumptions and risk factors such as the 12% HS-code misclassification rate and 18-month retroactive exposure. This gives the agent realistic expectations about output reliability.

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 dense sentences with no wasted words. It front-loads the core purpose and method, then gives usage timing, then lists inputs and the key limitation. Every sentence earns its place.

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

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

Given the tool's moderate complexity, no annotations, and the presence of an output schema, the description covers all critical external context: geographic scope, cost components, calculator assumptions, units, shipping modes, when to use it, and the estimate-only caveat. Nothing essential is missing for an agent to decide when to invoke it and how to supply inputs.

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

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

Schema description coverage is 0%, so the description must compensate. It does so meaningfully by specifying units for weight (kg), dimensions (cm), goods value (USD), and shipping mode options (air/sea). It also clarifies the volumetric weight calculation basis (air /6000). It doesn't describe each parameter individually, but the names plus unit hints are sufficient for correct invocation.

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

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

The description opens with a specific verb and object — 'Calculate true landed cost' — and scopes it to China-to-US/EU imports with specific cost components (volumetric weight, port charges, customs duty risk). It clearly distinguishes itself from the sibling supplier audit and payment verification tools, which address different tasks entirely.

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

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

The description explicitly states when to use the tool: 'Use before budgeting any import order'. It also provides a clear limitation: 'Estimates only, not a customs quote', which functions as a when-not-to-use boundary. This is strong usage guidance for an agent deciding between this and unrelated sibling tools.

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

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

With no annotations, the description carries the full burden, and it does well by disclosing that the tool flags specific red flags, returns go/no-go signals, and provides safe-payment rules. It clearly implies a read-only screening behavior rather than an action like sending a wire. It does not disclose limitations such as reliance on self-reported inputs, but the core behavioral traits are 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?

Three sentences cover purpose, timing, flag criteria, output, and usage trigger with no wasted words. The most important constraint, using it before sending a deposit, is 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?

For a straightforward screening tool with an output schema and five optional inputs, the description gives enough to call it correctly: the trigger condition, key input semantics, and return shape. Minor gaps are the exact role of contract_ok and the precise safe-payment rules, but these are partially addressed by the defaults and 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?

The schema provides no parameter descriptions, but the description maps four of the five parameters to named red flags: pay_to_personal, payee_matches_contract, urgent_account_change, and deposit_pct with the '>30%' threshold. The contract_ok parameter is not explained, and the 'industry norm' reference is not quantified, so the compensation is strong but not complete.

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

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

The description opens with a specific verb ('Screen') and resource ('Chinese supplier payment arrangement') and narrows scope to wire-fraud red flags before sending a deposit. It enumerates concrete flags and says it returns go/no-go signals, making it clearly distinct from sibling tools like verify_supplier or landed_cost_calc.

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 an explicit trigger: 'Use whenever a buyer is about to wire money to China.' It also states timing with 'BEFORE sending a deposit.' It does not explicitly mention when not to use the tool or compare it to alternatives, so it stops short of a 5.

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

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

No annotations are provided, so the description carries the full burden, and it does so well by listing concrete public-registry signals checked: shell-company traits, missing manufacturing scope, registered capital threshold (100万+), and lack of factory evidence. It also states it returns red flags and verification steps. It does not add caveats about data freshness or false positives, but the behavior is largely 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 five sentences with no filler: purpose, trigger, checks, exclusion, and expected input/output all appear in order. The most important scope constraints are front-loaded, and every sentence earns its place.

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

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

The description fully covers what the tool does, when to use it, what signals it checks, what input to provide, and what it returns, with the output schema covering result shape. The main gap is that it doesn't explicitly route the agent to sibling tools for broader or adjacent processes, such as full_supplier_audit.

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

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

The schema has 0% property descriptions, but the description explicitly mentions 'Provide company name and any known details (business scope, registered capital),' mapping directly to the three schema parameters. It also gives a meaningful threshold example with '100万+'. It stops short of a 5 because it doesn't state exact formats or value representations for the fields.

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

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

The description uses a specific verb+resource: 'Verify whether a Chinese company is a REAL factory or a trading company/middleman.' It names specific signals like shell-company traits, missing manufacturing scope, and low registered capital, and explicitly excludes US/EU jurisdictions, making the tool clearly distinguishable from broader audit or certification siblings.

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

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

The description gives a clear 'Use when' trigger: a buyer provides a Chinese company name, USCI, or a 1688/Alibaba vendor and needs hard fraud flags. It also provides an explicit 'NOT for' exclusion for US/EU companies. It does not name a sibling tool like full_supplier_audit as the alternative for broader cases, which keeps it just below perfect.

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