Skip to main content
Glama

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
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.
hsCodeYes6-10 digit Harmonized System code (e.g., '010121' for live horses)
quantityNoEstimated annual export quantity in units
valueUsdNoEstimated annual export value in USD
originCountryYesISO 3166-1 alpha-3 country code of export origin (e.g., 'KEN' for Kenya)
destinationCountryYesISO 3166-1 alpha-3 country code of import destination (e.g., 'USA' for United States)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
dutySavingsNoEstimated annual duty savings in USD under optimal preference program
optimalRouteNo
alternativeRoutesNo
complianceWarningsNoPotential compliance risks or documentation requirements

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by disclosing data sources (UN Comtrade, WCO tariff schedules, AU trade rules) and output types, providing useful context on expected results and scope. It doesn't mention side effects or limitations, but with strong annotations, this is sufficient.

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 front-loaded: first sentence states purpose, second describes inputs/outputs, third cites data sources, fourth lists use cases, followed by keywords. Every sentence serves a clear function with no fluff or redundancy.

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 purpose, inputs, outputs, data sources, and target use cases. The presence of an output schema and thorough parameter descriptions offsets the need for return-value details. Minor omissions like async behavior are already addressed in the schema, so the description is sufficiently complete for tool selection.

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 provides detailed descriptions for all six parameters, including examples for hsCode and country codes. The description's mention of 'origin country, destination country, and HS code' merely echoes the schema without adding syntax, constraints, or behavior. With 100% schema coverage, the baseline of 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 identifies the tool as an analyzer of AGOA/EBA duty savings with HS code-level trade route optimization, specifying distinct outputs (duty savings estimates, optimal trade routes, preference utilization recommendations). This specific combination differentiates it from siblings like 'tariff_arbitrage_finder' or 'agoa_eba_intelligence' by emphasizing route optimization and actionable recommendations.

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

Usage Guidelines4/5

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

The description states 'Ideal for export market evaluation, supply chain optimization, and trade agreement compliance analysis,' giving clear use context. However, it does not explicitly mention when not to use this tool or name any alternatives, which keeps it from a perfect score.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.