Civilizational Fragility MCP Server

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Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "civilizational-fragility-mcp": {
      "url": "https://ryanclinton--civilizational-fragility-mcp.apify.actor/mcp"
    }
  }
}

---

Civilizational fragility assessment for AI agents — this MCP server gives Claude, GPT-4, and any MCP-compatible agent access to cross-domain cascading collapse risk analysis backed by 17 live data sources and 10 mathematical frameworks. Built for researchers, national security analysts, institutional risk teams, and AI systems that need grounded, quantitative answers to questions about systemic collapse, tipping points, and cross-domain contagion.

The server runs as an always-on Apify Standby actor and exposes 8 specialist tools via the Model Context Protocol. Each tool call fires 17 actors in parallel — spanning FRED, IMF, World Bank, NOAA, FEMA, USGS, GDACS, OpenAQ, NVD, CISA KEV, WHO, ClinicalTrials.gov, Congress Bills, the Federal Register, and OFAC Sanctions — then runs the collected data through a stack of algorithms: sheaf cohomology, Kaneko coupled map lattice, Vietoris-Rips persistent homology, Conley index, Dec-POMDP intervention planning, Shapley decomposition, Leontief input-output analysis, mean-field games, Gaussian processes, and Moran evolutionary dynamics. The output is a structured fragility report with domain-level stress scores, risk grades, tipping-point proximity maps, and prioritized intervention recommendations.

What data can you access?

| Data Point | Source | Coverage |
|-----------|--------|----------|
| 📊 Economic time series | FRED Economic Data | 800K+ US/global series |
| 📉 Labor market indicators | BLS Economic Data | US employment, CPI, wages |
| 🌐 Global macro indicators | IMF Data | 190 countries |
| 🏦 Development & poverty metrics | World Bank Data | 200+ countries |
| 📋 OECD economic statistics | OECD Statistics | 38 member countries |
| 🌩 Weather & climate events | NOAA Weather | US and global |
| 🚨 Disaster declarations | FEMA Disaster Search | All US declared disasters |
| 🌍 Seismic events | USGS Earthquake Search | Global, real-time |
| ⚠️ Multi-hazard disaster alerts | GDACS | Worldwide, near-real-time |
| 🌫 Air quality readings | OpenAQ | Global monitoring stations |
| 🔓 CVE vulnerability database | NVD CVE Search | Full CVE history |
| 🛡 Actively exploited vulns | CISA KEV Catalog | Confirmed in-the-wild |
| 🏥 Global health indicators | WHO GHO | 1,000+ health series |
| 🧪 Clinical trial activity | ClinicalTrials.gov | Registered trials |
| 🏛 US federal legislation | Congress Bill Tracker | House + Senate bills |
| 📜 Regulatory actions | Federal Register Search | Federal rules & notices |
| 🚫 Sanctions & designations | OFAC Sanctions Search | SDN list + programs |

Why use this MCP server for civilizational fragility assessment?

Manual multi-domain risk assessment means pulling data from a dozen disparate APIs, normalizing incompatible schemas, choosing mathematical frameworks for cross-domain coupling, and spending weeks on analysis that is already stale by the time it is written up. Commercial risk intelligence platforms charge $15,000–$50,000 per year for static reports that cannot respond to live queries.

This MCP server automates the entire analytical pipeline. An AI agent issues a single tool call; the server fetches fresh data across all 17 sources in parallel, builds domain nodes and coupling edges from the raw indicators, and runs 10 algorithms in sequence to produce a structured risk report in minutes. No API keys to manage, no data pipelines to maintain, no model to retrain.

Platform benefits:
- Standby mode — the server is always warm; tool calls connect immediately with no cold-start latency
- Parallel data fetching — all 17 actor calls run concurrently; data collection takes 2–4 minutes, not hours
- API access — trigger tool calls from any MCP-compatible AI client: Claude Desktop, Cursor, Cline, or custom agents
- Pay-per-call pricing — no subscription; pay only for the tool calls you make
- Monitoring — configure Slack or email alerts if the server encounters errors via Apify's built-in run monitoring
- Integrations — connect to Zapier, Make, webhooks, or call the Apify API directly for programmatic access

Features

- 10 mathematical frameworks in one server — sheaf cohomology, Kaneko CML, Vietoris-Rips persistent homology, Conley index, Dec-POMDP, Shapley decomposition, Leontief I-O, mean-field game, Gaussian process, and Moran process are all implemented in the scoring engine
- Sheaf H^1 obstruction detection — identifies where local domain assessments fail to glue globally; high H^1 indicates inconsistent risk signals across domains that cannot be reconciled
- Kaneko coupled map lattice with configurable logistic r parameter (default 3.8, chaotic regime); tracks per-domain Lyapunov exponents, synchronization index, and cascade events where stress crosses critical threshold
- Vietoris-Rips persistent homology — computes Betti numbers beta_0 (independent risk clusters) and beta_1 (circular dependencies); long-lived intervals in the persistence diagram signal structural vulnerabilities vs. transient noise
- Conley index Morse decomposition — isolates attractors and repellers in the domain state space; repeller count is a direct fragility signal
- Dec-POMDP intervention planner — models each domain as an agent selecting from {do_nothing, monitor, mitigate, emergency_response} with belief-space value iteration; outputs optimal policy, total cost/benefit, and value of information per domain
- Shapley decomposition with interaction indices — computes each domain's marginal contribution to total fragility using the full Shapley formula; pairwise interaction indices reveal synergistic domain pairs
- Leontief nonlinear input-output analysis — maps resource flow bottlenecks and forward/backward linkages across all 6 domains; system multiplier quantifies amplification
- Mean-field game (coupled HJB + Fokker-Planck) — models epidemic-economic feedback loops; outputs Nash equilibrium status, epidemic peak, economic trough, and density evolution
- Gaussian process with Matern 5/2 kernel — spatial regression across domain stress values; hyperparameter optimization via log marginal likelihood; quantifies spatial correlation structure
- Moran evolutionary process — simulates 10,000 institutional actors over configurable generations; fixation probabilities and stationary distribution reveal long-run institutional dominance
- 6-domain architecture — economics, climate, health, cybersecurity, governance, and environment modeled as coupled DomainNode objects with stressLevel, fragility, and resilience attributes
- 17 data sources queried in parallel — FRED, BLS, IMF, World Bank, OECD, NOAA, FEMA, USGS, GDACS, OpenAQ, NVD, CISA KEV, WHO, ClinicalTrials, Congress Bills, Federal Register, OFAC
- Risk grade output — A through F letter grades derived from the composite overallFragility score for at-a-glance communication
- Structured recommendations — topRisks and recommendations arrays in every report, ready for agent reasoning chains

Use cases for civilizational fragility assessment

National security and strategic intelligence

Defense research teams and think tanks use assess_cascading_fragility to produce quarterly risk briefings that span economic, geopolitical, health, and environmental domains simultaneously. The Dec-POMDP tool output maps directly to resource allocation decisions: where to direct monitoring investment and when to escalate to emergency response posture.

Early warning system development

Teams building automated early-warning systems embed detect_tipping_proximity into daily or weekly scheduled pipelines. Lyapunov exponents above zero flag chaotic domain dynamics; sheaf cohomology obstructions above 0.5 indicate the risk landscape is no longer globally consistent. Both signals fire before conventional indicators move.

Academic and institutional research

Researchers studying systemic risk, complexity economics, and sociotechnical collapse use simulate_multiplex_cascade and track_persistent_homology to generate quantitative inputs for papers and models. The Vietoris-Rips filtration output and Betti number time series are directly interpretable within the topological data analysis literature.

AI agent augmentation for macro risk reasoning

AI coding assistants and research agents configured with this MCP server can answer questions like "which domain is closest to a tipping point?" or "what is the optimal intervention sequence given current fragility levels?" using live, grounded data rather than training-time knowledge. The structured JSON output is optimized for agent consumption.

Long-term institutional strategy

Strategy teams and scenario planners use forecast_civilizational_trajectory to understand which governance strategies are evolutionarily stable under current conditions. The Moran process fixation probabilities and mean-field game Nash equilibrium outputs quantify which institutional approaches dominate long-run.

Causal pathway and bottleneck analysis

Operations researchers and supply chain analysts use causal_cross_domain_query to trace resource flow bottlenecks between domains via Leontief I-O. The system multiplier reveals how much a unit shock in one domain amplifies across the network. The Gaussian process spatial correlation matrix shows which domains are statistically co-located in the stress landscape.

How to connect this MCP server

Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "civilizational-fragility": {
      "url": "https://civilizational-fragility-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN"
    }
  }
}

Cursor

Add this to your .cursor/mcp.json:

{
  "mcpServers": {
    "civilizational-fragility": {
      "url": "https://civilizational-fragility-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN"
    }
  }
}

Cline / VS Code

Add to your Cline MCP settings:

{
  "mcpServers": {
    "civilizational-fragility": {
      "url": "https://civilizational-fragility-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN"
    }
  }
}

Replace YOUR_APIFY_TOKEN with your token from Apify Console > Settings > Integrations.

MCP tools

| Tool | Best for | Estimated cost |
|------|----------|---------------|
| assess_cascading_fragility | Full report combining all 10 algorithms | $300–500 |
| detect_tipping_proximity | Early warning; identify domains nearest critical transition | $200–350 |
| simulate_multiplex_cascade | Shock propagation modeling; resilience scenario testing | $200–300 |
| compute_domain_shapley | Blame attribution; which domain drives fragility most | $200–300 |
| plan_intervention_decpomdp | Resource allocation; optimal intervention policy | $200–300 |
| causal_cross_domain_query | Bottleneck tracing; cross-domain feedback quantification | $200–350 |
| track_persistent_homology | Topological risk structure; persistent vs. transient features | $200–300 |
| forecast_civilizational_trajectory | Long-run institutional evolution and trajectory | $200–350 |

Tool parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| query | string | "global risk assessment" | Focus query for data collection across all 17 actors |
| cml_time_steps | number | 200 | Coupled map lattice simulation steps (all tools) |
| cml_logistic_r | number | 3.8 | Logistic map r parameter; >3.57 = chaotic regime |
| planning_horizon | number | 5 | Dec-POMDP planning horizon in steps |
| population_size | number | 10000 | Moran process population (trajectory forecast tool only) |
| generations | number | 500 | Moran evolutionary generations (trajectory forecast tool only) |

Input tips

- Use a specific query for focused data — "climate economic cascading risk 2026" pulls more relevant IMF, World Bank, and NOAA records than the generic default
- Leave logistic_r at 3.8 for standard analysis — values above 3.9 increase chaos sensitivity; values below 3.57 produce periodic (non-chaotic) CML behavior
- Start with detect_tipping_proximity before running assess_cascading_fragility — it is cheaper and identifies which domains warrant deeper investigation
- Increase planning_horizon to 10 for multi-year scenario planning; the default of 5 models near-term intervention sequences
- Run compute_domain_shapley first when you need to justify intervention spending; the Shapley values identify the highest-impact domain with precision

Output example

A representative response from assess_cascading_fragility:

{
  "overallFragility": 0.67,
  "riskGrade": "C",
  "topRisks": [
    "Elevated sheaf H1 obstructions (0.42) indicate inconsistent cross-domain risk signals — local assessments for cyber and economic domains cannot be reconciled globally",
    "Positive Lyapunov exponents in cyber (0.31) and governance (0.18) signal chaotic dynamics — small shocks can cascade non-linearly",
    "Leontief system multiplier 2.14 indicates each unit of domain stress amplifies 2.14x across the network"
  ],
  "recommendations": [
    "Priority 1: Mitigate cyber domain — Shapley value 0.28 makes it the dominant fragility contributor",
    "Priority 2: Monitor governance domain — positive Lyapunov exponent with second-highest Shapley value (0.21)",
    "Priority 3: Emergency response posture for health — Dec-POMDP optimal action given planning horizon 5"
  ],
  "domains": [
    { "id": "cyber", "name": "Cybersecurity", "stress": 0.74, "fragility": 0.71, "resilience": 0.29 },
    { "id": "economic", "name": "Economics", "stress": 0.58, "fragility": 0.61, "resilience": 0.42 },
    { "id": "governance", "name": "Governance", "stress": 0.63, "fragility": 0.65, "resilience": 0.35 },
    { "id": "health", "name": "Health", "stress": 0.51, "fragility": 0.55, "resilience": 0.48 },
    { "id": "climate", "name": "Climate", "stress": 0.47, "fragility": 0.49, "resilience": 0.54 },
    { "id": "environment", "name": "Environment", "stress": 0.44, "fragility": 0.46, "resilience": 0.58 }
  ],
  "sheafH1": 3,
  "globalConsistency": 0.62,
  "cmlSynchronization": 0.38,
  "cascadeEvents": 7,
  "betti0": 2,
  "betti1": 1,
  "attractors": 3,
  "repellers": 2,
  "shapleyDominant": "cyber",
  "leontiefMultiplier": 2.14,
  "nashEquilibrium": false,
  "moranDominant": "monitor",
  "report": {
    "overallFragility": 0.67,
    "riskGrade": "C",
    "sheafCohomology": { "h0": 1, "h1": 3, "globalConsistency": 0.62 },
    "persistentHomology": { "betti0": 2, "betti1": 1, "totalPersistence": 1.84, "stabilityScore": 0.51 },
    "conleyIndex": { "attractorCount": 3, "repellerCount": 2 },
    "shapley": { "dominantDomain": "cyber", "totalFragility": 0.67 },
    "leontief": { "systemMultiplier": 2.14, "bottlenecks": ["cyber", "governance"] },
    "meanField": { "nashEquilibrium": false, "epidemicPeak": 0.34, "economicTrough": -0.19 }
  }
}

Output fields

| Field | Type | Description |
|-------|------|-------------|
| overallFragility | number (0–1) | Composite fragility score across all domains and algorithms |
| riskGrade | string (A–F) | Letter grade derived from overallFragility |
| topRisks[] | string[] | Human-readable top risk narratives for agent reasoning |
| recommendations[] | string[] | Prioritized intervention recommendations |
| domains[].id | string | Domain identifier (cyber, economic, governance, health, climate, environment) |
| domains[].stress | number (0–1) | Current domain stress level derived from live data |
| domains[].fragility | number (0–1) | Structural fragility of the domain |
| domains[].resilience | number (0–1) | Domain capacity to absorb and recover from shocks |
| sheafH1 | number | Count of H^1 obstruction cycles — inconsistent cross-domain signals |
| globalConsistency | number (0–1) | 1 minus normalized H^1; higher = more globally consistent |
| cmlSynchronization | number (0–1) | CML synchronization index; low value = desynchronized cascade risk |
| cascadeEvents | number | Count of cascade events in the CML simulation |
| betti0 | number | Betti-0: independent risk cluster count at final filtration scale |
| betti1 | number | Betti-1: circular dependency count at final filtration scale |
| attractors | number | Conley index attractor count (stable equilibria) |
| repellers | number | Conley index repeller count (unstable equilibria — fragility signal) |
| shapleyDominant | string | Domain with highest Shapley fragility contribution |
| leontiefMultiplier | number | System-wide amplification factor from Leontief I-O |
| nashEquilibrium | boolean | Whether the mean-field game has reached Nash equilibrium |
| moranDominant | string | Dominant institutional strategy from Moran evolutionary process |
| lyapunovExponents | Record<string,number> | Per-domain CML Lyapunov exponents; positive = chaotic |
| tippingProximity | Record<string,number> | Per-domain tipping point proximity score (detect tool) |
| optimalPolicy[] | object[] | Dec-POMDP recommended action per domain with cost/benefit |
| valueOfInformation | Record<string,number> | Per-domain VOI guiding monitoring investment allocation |
| domainContributions | Record<string,number> | Shapley value per domain |
| interactionIndices | Record<string,Record<string,number>> | Pairwise domain synergy matrix |
| fixationProbabilities | Record<string,number> | Moran fixation probability per institutional strategy |
| stationaryDistribution | number[] | Long-run institutional strategy distribution |
| leontiefBottlenecks | string[] | Domains identified as resource flow bottlenecks |
| spatialCorrelation | number[][] | GP Matern 5/2 spatial correlation matrix |
| gpHyperparameters | object | GP length scale, signal variance, noise variance |

How much does it cost to run civilizational fragility assessments?

This MCP server uses pay-per-event pricing — you pay per tool call. Each tool call fetches data from all 17 actors in parallel; the cost reflects the underlying actor compute costs plus the MCP server's own platform costs.

| Scenario | Tool calls | Cost per call | Total cost |
|----------|-----------|---------------|------------|
| Quick test (detect_tipping_proximity) | 1 | ~$0.04 | ~$0.04 |
| Weekly monitoring run | 4 | ~$0.04 | ~$0.16 |
| Full fragility assessment (assess_cascading_fragility) | 1 | ~$0.04 | ~$0.04 |
| Monthly research workflow | 20 | ~$0.04 | ~$0.80 |
| Institutional daily monitoring | 90 | ~$0.04 | ~$3.60 |

Note: the per-event charge above covers the MCP server event fee. The underlying 17 actor calls each consume Apify platform credits separately — budget $5–30 per full assess_cascading_fragility run depending on data volumes returned. You can set a maximum spending limit per run to cap total costs. The actor stops when your budget is reached.

The Apify Free plan includes $5 of monthly platform credits — enough to run several full assessments at no cost before any charges apply. Compare this to institutional risk platforms at $15,000–$50,000/year for static, non-queryable reports.

Using the API

The MCP server runs in Apify Standby mode and is accessible via its public actor URL. You can also trigger it programmatically using the Apify API.

Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")