mcp-knowledge

africa_trade_preference_optimizer

Read-onlyIdempotent

As a COO, analyze AGOA/EBA duty savings opportunities with HS code-level trade route optimization. Input origin country, destination country, and HS code to receive duty savings estimates, optimal trade routes, and preference utilization recommendations. Uses UN Comtrade trade flow data, WCO tariff schedules, and African Union trade agreement rules. Ideal for export market evaluation, supply chain optimization, and trade agreement compliance analysis. Keywords: AGOA, EBA, duty savings, trade optimization, HS code, African trade, export strategy.

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	6-10 digit Harmonized System code (e.g., '010121' for live horses)
`quantity`	No	Estimated annual export quantity in units
`valueUsd`	No	Estimated annual export value in USD
`originCountry`	Yes	ISO 3166-1 alpha-3 country code of export origin (e.g., 'KEN' for Kenya)
`destinationCountry`	Yes	ISO 3166-1 alpha-3 country code of import destination (e.g., 'USA' for United States)

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes
`sources`	No
`warnings`	No
`dutySavings`	No	Estimated annual duty savings in USD under optimal preference program
`optimalRoute`	No
`alternativeRoutes`	No
`complianceWarnings`	No	Potential compliance risks or documentation requirements

TDQS

A3.9/5.0

Behavior4/5

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

The description adds context beyond annotations by specifying data sources (UN Comtrade, WCO, African Union rules) and output types (duty savings estimates, optimal trade routes, recommendations). It does not contradict the readOnlyHint, openWorldHint, or idempotentHint annotations.

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

Conciseness4/5

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

The description is concise (about 80 words) and front-loaded with the main verb. It includes keywords and ideal use cases but is a single paragraph; a more structured format could improve scannability.

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?

The description covers the tool's purpose, data sources, and output types. With a full output schema and good annotations, the description is adequate. No major gaps are present.

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

Parameters3/5

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

Schema coverage is 100% with good schema descriptions. The description adds context that the tool expects originCountry, destinationCountry, and hsCode but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

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?

The description clearly states the tool's purpose: analyze AGOA/EBA duty savings opportunities with HS code-level optimization. It distinguishes itself from siblings like africa_trade_barrier_breaker and agoa_eba_intelligence by focusing on duty savings and trade route optimization.

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 scenarios (export market evaluation, supply chain optimization) but does not explicitly state when not to use it or compare directly with sibling tools. No exclusion criteria or alternative recommendations are 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.

africa_trade_preference_optimizer

Input Schema

Output Schema

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