apifyforge/climate-financial-stranded-asset-mcp
Climate financial stranded asset analysis for AI agents — this MCP server gives any LLM client direct access to eight quantitative climate risk tools covering carbon asset stranding, portfolio climate VaR, physical hazard exposure, biodiversity-financial coupling, transition contagion, and full TCFD reporting.
Platform-specific configuration:
{
"mcpServers": {
"climate-financial-stranded-asset-mcp": {
"command": "npx",
"args": [
"-y",
"climate-financial-stranded-asset-mcp"
]
}
}
}Add the config above to .claude/settings.json under the mcpServers key.
> [View on ApifyForge](https://apifyforge.com/actors/mcp-servers/climate-financial-stranded-asset-mcp) | [Use on Apify Store](https://apify.com/ryanclinton/climate-financial-stranded-asset-mcp)
---
Add to your MCP client (Claude Desktop, Cursor, Windsurf):
{
"mcpServers": {
"climate-financial-stranded-asset-mcp": {
"url": "https://ryanclinton--climate-financial-stranded-asset-mcp.apify.actor/mcp"
}
}
}---
Climate financial stranded asset analysis for AI agents — this MCP server gives any LLM client direct access to eight quantitative climate risk tools covering carbon asset stranding, portfolio climate VaR, physical hazard exposure, biodiversity-financial coupling, transition contagion, and full TCFD reporting. It is built for investment analysts, risk teams, and sustainability professionals who need institutional-grade climate metrics without constructing the data infrastructure themselves.
The server orchestrates 14 live data sources — SEC EDGAR, FRED, World Bank, OECD, Eurostat, IMF, OpenAQ, IUCN Red List, GBIF, UN COMTRADE, OpenCorporates, GLEIF, Nominatim, and SEC insider filings — and applies six quantitative algorithms to produce TCFD-aligned output. Connect once via the Model Context Protocol and every supported AI client can call all eight tools without additional setup.
| Data Point | Source | Example | |---|---|---| | 📊 Carbon asset stranding probability | Markov regime-switching Monte Carlo | XOM: 73% stranding prob, $2.1B expected loss | | 📉 Portfolio climate VaR at 99% confidence | Monte Carlo (10,000 paths) + GPD tails | $485M VaR99, $612M Expected Shortfall | | 🏭 Physical hazard exposure by facility | Spatial lat/lng overlay + OpenAQ | Midland TX refinery: 34% composite risk score | | 🌿 Biodiversity-financial risk (TNFD) | IUCN Red List + GBIF occurrence trends | Agriculture co: "high" TNFD category, 12 VU s
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