mcp-knowledge

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

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds value by disclosing external data sources (WTO TFA, US Census) and output structure (cost breakdown, duties, taxes). 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.

Conciseness5/5

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

Two sentences, no filler. Purpose stated first, followed by inputs and outputs. Highly efficient and front-loaded.

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?

With output schema present (though not shown), description lists expected outputs. Combined with annotations, it covers the tool's behavior adequately. Missing async handling details but not critical for basic use.

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% only for async), but the description names key parameters and explains their role in the model (e.g., 'product value, shipping cost'). This compensates for missing schema descriptions, though async parameter is omitted.

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

Purpose5/5

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

Description uses specific verb 'model' and resource 'tariff changes affect landed costs', clearly distinguishing it from siblings like tariff_arbitrage_finder. Inputs and outputs are enumerated, making purpose unambiguous.

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?

Description states role ('As a COO') and scenario but does not explicitly mention when to avoid or compare with alternatives. No exclusion criteria or sibling differentiation provided.

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

C2.8/5.0

Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

tariff_impact_simulator

Input Schema

Output Schema

TDQS

Glama MCP Gateway

TDQS

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