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mcp-knowledge

by io.github.getgapup

africa_trade_finance_esg_rater

Read-onlyIdempotent

As a COO, evaluate ESG compliance of African trade finance providers using World Bank WITS trade statistics and CDP climate disclosure data. Input the financial institution's name or identifier, and receive an ESG rating with breakdown across environmental, social, and governance dimensions. Ideal for due diligence on trade partners or portfolio risk assessment. Pass async:true to avoid timeout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoAssessment year (2018-2023)
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.
countryCodeNoISO 2-letter country code (e.g., 'ZA' for South Africa)
institutionNameYesFull name of the trade finance provider (e.g., 'Standard Bank Group')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
warningsYes
esgRatingYes
socialScoreNo
tradeVolumeNoAnnual trade finance volume (USD)
carbonIntensityNoCO2 emissions per million USD financed (tons)
governanceScoreNo
environmentalScoreNo

TDQS

A4.4/5.0
Behavior4/5

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

Description aligns with annotations (readOnlyHint, idempotentHint). Adds context about data sources and timeout handling via async parameter, which aids agent understanding.

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?

Two sentences plus a short instruction. Front-loaded with purpose and data sources. No fluff.

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?

Covers main use case, data sources, async option. Output schema exists. Could mention optional parameter effects but sufficient for an AI agent.

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

Parameters4/5

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

Schema coverage is 100%, but description adds value by explaining async in context of timeout and providing example values for institutionName and countryCode.

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?

Clearly states the tool evaluates ESG compliance of African trade finance providers using specific data sources (World Bank WITS, CDP). Distinguishes from siblings by specifying African trade finance focus.

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?

Explicitly states ideal usage for due diligence on trade partners or portfolio risk assessment. Mentions async option to avoid timeout. Does not explicitly state when not to use it.

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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