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tariff_arbitrage_finder

Read-onlyIdempotent

As a COO, identify tariff reclassification opportunities to reduce import costs. Analyzes product HS codes against WTO TFA and USA Trade Online data to find lower-duty classifications. Inputs: product description, current HS code, country of origin, and annual import volume. Outputs: potential duty savings, alternative HS codes, and compliance considerations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
annualVolumeNo
currentHsCodeYes
countryOfOriginYes
currentDutyRateNo
productDescriptionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
opportunitiesNo

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds context by specifying data sources (WTO TFA and USA Trade Online) and listing outputs (duty savings, alternative HS codes, compliance considerations). However, it does not disclose potential limitations, data freshness, or async behavior, so it adds moderate value beyond annotations.

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

Conciseness5/5

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

The description is concise and well-structured: it opens with the primary purpose, then explains the method, and finally lists inputs and outputs. Every sentence earns its place, and it is appropriately front-loaded with the main use case.

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

Completeness3/5

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

The description covers the core purpose, method, inputs, and outputs, and the output schema likely handles return details. However, it omits the currentDutyRate parameter, which may be important for calculating savings, and does not address async behavior or distinguish from similar tools. Given the tool's moderate complexity, the description is adequate but has clear gaps.

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

Parameters2/5

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

Schema description coverage is only 17% (only async has a description), placing the burden on the description. The description lists four of the six parameters (product description, HS code, country of origin, annual import volume) but omits currentDutyRate and async. It provides only basic labels without explaining meaning, format, or relationships, so it only partially compensates for the low schema coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'identify tariff reclassification opportunities to reduce import costs' and specifies the method (analyzing HS codes against WTO TFA and USA Trade Online data). This is a specific verb+resource. However, it does not explicitly differentiate from sibling tools like tariff_impact_simulator, so it lacks explicit sibling differentiation.

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

Usage Guidelines3/5

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

The description implies usage context ('As a COO' and reducing import costs) and lists inputs/outputs, but it does not provide explicit when-to-use/when-not-to-use guidance or name alternatives. The usage is inferred from the purpose statement rather than clearly spelled out.

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

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TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.