gapup-mcp

tariff_impact_simulator

Read-onlyIdempotent

As a COO, model how proposed tariff changes affect landed costs for imported goods. Inputs: HS code, current tariff rate, proposed tariff rate, product value, shipping cost, and country of origin. Outputs: detailed cost breakdown including new duties, taxes, and total landed cost impact. Sources include WTO TFA and US Census trade data.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If 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.
`hsCode`	Yes
`productValue`	Yes
`shippingCost`	No
`countryOfOrigin`	Yes
`currentTariffRate`	Yes
`proposedTariffRate`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`costImpact`	No
`currentDuty`	No
`proposedDuty`	No
`dutyDifference`	No
`currentLandedCost`	No
`proposedLandedCost`	No
`costImpactPercentage`	No

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is clear. The description adds value by stating the tool models cost impacts and cites sources (WTO TFA and US Census trade data), providing context beyond the 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 two concise sentences with no fluff. It front-loads the primary purpose and efficiently covers inputs and outputs.

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

Completeness4/5

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

Given the tool's complexity (7 parameters, vague schema, output schema exists), the description adequately summarizes the main functionality. It covers essential inputs and outputs but omits details on the optional async parameter and explicit limitations.

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

Parameters4/5

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

Schema description coverage is low (14%), but the description lists all key inputs (HS code, tariff rates, product value, shipping cost, country of origin) and explains their role in the simulation. This compensates for the sparse schema and adds meaningful context.

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: 'model how proposed tariff changes affect landed costs for imported goods.' It specifies verb and resource, and lists inputs and outputs. However, it does not explicitly differentiate from sibling tools like 'tariff_arbitrage_finder', which could be confused as similar.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It mentions the intended user role ('As a COO') but does not specify prerequisites, exclusions, or compare to other tariff-related tools in the sibling list.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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