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mcp-knowledge

by io.github.getgapup

agoa_eba_intelligence

Read-only

Intelligence préférentielle AGOA (US→Africa) et EBA/GSP (EU→Africa). Vérifie l'éligibilité d'un pays africain aux programmes tarifaires préférentiels, l'éligibilité d'un produit par code HS, identifie les meilleures opportunités d'export Afrique→US/EU, et fournit les règles de conformité (rules of origin, valeur ajoutée, docs). Différenciateur Africa diaspora : 39 pays AGOA + 47 LDCs EBA encodés. Sources : AGOA.info · EU EBA · EU GSP+ · WTO Tariff · UN Comtrade.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesMode d'analyse : 'country_eligibility' (statut AGOA/EBA/GSP d'un pays africain) | 'product_eligibility' (éligibilité d'un produit par code HS) | 'trade_opportunity' (top opportunités export Afrique→US/EU) | 'compliance_check' (rules of origin, seuils valeur ajoutée, documentation)
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.
hs_codeNoCode HS (Harmonized System) 6+ chiffres (requis pour product_eligibility). Exemple : '620342' = pantalons coton homme, '090111' = café arabica non torréfié, '060310' = fleurs fraîches.
country_isoNoCode ISO 2-lettres du pays africain (requis pour country_eligibility). Exemples : KE=Kenya, NG=Nigeria, ZA=Afrique du Sud, ET=Éthiopie, LS=Lesotho, GH=Ghana.
destinationNoMarché de destination pour trade_opportunity : 'US', 'EU', ou 'both' (défaut). Ignoré pour les autres modes.

TDQS

A4/5.0
Behavior4/5

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

The description adds behavioral context beyond annotations by detailing data sources (AGOA.info, EU EBA, etc.) and the scope of countries (39 AGOA, 47 LDCs). It aligns with readOnlyHint=true, indicating no destructive actions. However, it does not mention the async behavior or that results might be delayed for some modes.

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, front-loaded with the tool's main purpose, and every sentence contributes essential information. No wasted words 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?

Given the tool's complexity (four modes, multiple countries, two programs), the description covers the main functionality, data sources, and regional scope. It lacks details on output format or error handling, but is still fairly complete for a query tool with good annotations.

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%, and the description provides a cohesive narrative tying the modes to the tool's functions. However, the schema already includes detailed parameter descriptions and examples (e.g., HS codes), so the description adds limited new semantic value beyond restating the modes.

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: checking eligibility of African countries and products for preferential tariff programs (AGOA, EBA/GSP), identifying export opportunities, and providing compliance rules. It uses specific verbs and resources, and differentiates from sibling tools by covering both US and EU programs and including compliance details.

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 by listing the four modes, but it does not explicitly state when to use this tool versus alternatives like africa_trade_preference_arbitrage. It lacks when-not-to-use guidance and comparisons with siblings.

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.

Resources