gapup-mcp

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

A3.7/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds useful behavioral context, such as using external data sources (UN Comtrade, WCO tariff schedules, African Union rules) and generating specific outputs (duty savings estimates, optimal trade routes, recommendations). This goes 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.

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the main action in the first sentence. It lists inputs, outputs, data sources, and use cases efficiently. The inclusion of keywords at the end is slightly redundant but does not detract significantly from conciseness.

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 presence of an output schema and annotations, the description provides adequate context: it explains the tool's purpose, inputs, outputs, and data sources. However, it does not mention the async behavior of the optional parameter, and there is no discussion of error conditions or data freshness, which would improve completeness for a data-dependent tool.

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?

The input schema has 100% description coverage, so all parameters are documented in the schema. The description mentions the three required parameters (originCountry, destinationCountry, hsCode) but does not add extra semantics beyond what the schema provides for optional parameters like async, quantity, and valueUsd. Therefore, the description adds minimal value in this dimension.

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 that the tool analyzes AGOA/EBA duty savings opportunities with HS code-level trade route optimization, specifying inputs and outputs. However, it does not explicitly differentiate from sibling tools like africa_trade_preference_arbitrage or agoa_eba_intelligence, which may have overlapping purposes.

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 mentions it is 'Ideal for export market evaluation, supply chain optimization, and trade agreement compliance analysis,' providing context for when to use the tool. However, it does not include explicit guidance on when not to use it or any comparisons to alternative tools, which is a gap given the many sibling tools in the same domain.

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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