IFC GEMs Risk MCP Server
Access emerging markets credit risk data through your AI assistant
🎯 What It Does
This Model Context Protocol (MCP) server provides real-time access to the World Bank's IFC Global Emerging Markets (GEMs) Risk Database—a comprehensive source of credit risk analytics for emerging market lending.
Query credit risk data across three economic sectors:
- Sovereign - National government debt and sovereign bonds (lowest risk: 0.77% default rate)
- Public Sector - Sub-sovereign entities and state-owned enterprises
- Private Sector - Corporate and private sector lending (largest dataset: 2,853 observations)
Analyze 7 global regions, 15 economic sectors, and 40+ years of credit risk data (1984-2024).
🔧 How to Connect
MCP Endpoint
https://mcp.akgunaylabs.io/api/gems-risk/mcpClaude Desktop Configuration
Add to your config file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"gems-risk": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://mcp.akgunaylabs.io/api/gems-risk/mcp"
]
}
}
}📊 Available Tools (9 total)
Query average default frequencies by region
Example: "What's the default rate in Latin America?"Retrieve recovery rates for defaulted loans
Example: "What's the recovery rate for sovereign debt in MENA?"Comprehensive credit risk profile with default, recovery, and expected loss
Example: "Show me complete credit risk for East Asia"Compare default and recovery rates across all regions
Example: "Compare default rates across all regions"Analyze credit performance by economic sector
Example: "Default rate for infrastructure in East Asia?"Compare credit risk across project types
Example: "Compare greenfield vs expansion projects"Track default and recovery rates over time
Example: "Show 10-year default rate trend for South Asia"Compare senior vs subordinated debt recovery rates
Example: "Senior vs subordinated debt recovery rates"Advanced queries across sector, project type, and seniority
Example: "Senior infrastructure loans in Latin America"🌍 Coverage
Geographic Regions (7)
- Global
- East Asia & Pacific
- Latin America & Caribbean
- Sub-Saharan Africa
- South Asia
- Middle East & North Africa
- Europe & Central Asia
Economic Sectors (15)
- Infrastructure
- Manufacturing
- Financial Institutions
- Oil & Gas
- Agriculture
- + 10 more sectors
Data Quality
- 40+ years (1984-2024)
- 8,000+ observations
- Real-time API
- Authoritative IFC data
💡 Use Cases
Portfolio risk assessment, regional comparison, sector analysis, country risk evaluation
Project evaluation, seniority analysis, infrastructure project finance, development bank lending
Emerging markets research, historical credit cycle analysis, risk modeling with real-world data
📈 Data Source
World Bank Data360 API - IFC_GEM (Global Emerging Markets Risk Database)
- Authority: International Finance Corporation (IFC) - World Bank Group
- Coverage: 40+ years of emerging market credit data (1984-2024)
- Data Points: 8,000+ default and recovery observations
- Access: Real-time API, no authentication required, publicly available
- Privacy: Aggregated regional/sectoral statistics, no personal data
🔍 Understanding the Metrics
Percentage of loans that default. Lower is better. Range: 0.5% - 5% for emerging markets.
Percentage recovered when defaults occur. Higher is better. Range: 50% - 95% for emerging markets.
Combined measure: Default Rate × (1 - Recovery Rate). Overall portfolio risk. Lower is better.
💬 Example Queries
Once connected, ask Claude natural questions like:
- "What's the default rate in Latin America?"
- "Compare sovereign vs corporate default risk in Sub-Saharan Africa"
- "Show me the 10-year default rate trend for global emerging markets"
- "What's the recovery rate for senior infrastructure debt in MENA?"
- "Which region has the highest recovery rates for private sector lending?"
- "Compare greenfield vs expansion projects for credit risk"
📖 Version Information
What's New in v2.0
- Three economic sectors (sovereign, public, private)
- Enhanced tool coverage (5 → 9 tools)
- Smart data source detection
- Time series analysis
- Seniority analysis
- Multidimensional queries