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mcp-knowledge

by io.github.getgapup

africa_trade_preference_arbitrage

Read-onlyIdempotent

Analyzes AGOA (African Growth and Opportunity Act) and EBA (Everything But Arms) trade preference arbitrage opportunities for COOs evaluating export strategies. Compares tariff rates, trade volumes, and preference utilization across eligible African countries using WITS and OECD trade data. Returns structured analysis of potential duty savings, market access advantages, and compliance requirements. — pass async:true REQUIRED to avoid x402 timeout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoReference year for trade data
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_codeYes6-10 digit Harmonized System product code
exporting_countryYesISO 2-letter country code of African exporter
importing_countryNoISO 2-letter country code of target market (US/EU)
preference_schemeNoTrade preference scheme to analyze

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
duty_savings_pctNoEstimated duty savings percentage under preference scheme
trade_volume_usdNoAnnual trade volume in USD for given HS code
market_access_scoreNoComposite score of market access advantage (0-100)
compliance_requirementsNoList of compliance requirements for preference eligibility
preference_utilization_rateNoPercentage of eligible exports utilizing preference

TDQS

A3.7/5.0
Behavior4/5

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

The description adds value beyond annotations by revealing the async requirement and timeout risk, which are important behavioral traits. It also mentions data sources (WITS, OECD). Annotations already declare readOnly/openWorld/idempotent, so the burden is lower. 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.

Conciseness4/5

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

The description is concise with a clear purpose statement followed by technical notes. It is front-loaded and every sentence contributes. Slightly verbose in listing data sources but overall efficient.

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 (6 parameters, output schema exists), the description covers purpose, data sources, output type, and a critical async requirement. It could mention country eligibility or limitations, but is largely complete for the agent to understand usage.

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%, so the schema already describes all parameters. The description adds only a usage hint about async, which is not a parameter semantic. It does not provide additional meaning beyond what the schema offers.

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 analyzes AGOA/EBA trade preference arbitrage opportunities, specifies target audience (COOs), data sources (WITS, OECD), and output (structured analysis). It is a specific verb+resource with scope, and distinguishes from siblings by focusing on arbitrage opportunities.

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

Usage Guidelines2/5

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

The description provides a technical requirement (async:true to avoid timeout) but does not explain when to use this tool versus sibling tools like agoa_eba_intelligence or africa_trade_preference_optimizer. No guidance on prerequisites or exclusions.

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.

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