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Futuristic Risk Intelligence

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Futuristic Risk Intelligence — MCP Server & Data Feed

Geopolitical conflict risk data for AI agents via Model Context Protocol (MCP). Updated daily.

PyPI war-dashboard-data MCP server

MCP Tools

Tool

Description

get_conflict_risks

Risk probabilities for 6 major geopolitical conflicts (escalation, ceasefire, regime change) with 1d/7d/30d horizons

get_political_events

High-impact political, economic, and natural disaster events with probability estimates

get_maritime_traffic

⚠️ Suspended — AIS snapshot data does not meet reliability standards. Returns status: unavailable.

Related MCP server: TickerAPI

Install

pip install war-dashboard-data

Quick Start

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "futuristic-risk": {
      "command": "war-dashboard-data"
    }
  }
}

Then ask Claude: "What's the current escalation risk for Russia-Ukraine?"

Direct API (REST)

curl https://raw.githubusercontent.com/cct15/war-dashboard-data/main/conflicts.json

Coverage

6 conflict regions: Russia-Ukraine, Iran-Israel/US, Israel-Palestine, China-Taiwan, India-Pakistan, US-Latin America

5 event types with clear risk direction:

Event Type

Meaning

Direction

escalation

Military escalation (strikes, invasion, nuclear test)

risk_increase

ceasefire

Ceasefire or peace agreement reached

risk_decrease

ceasefire_cancel

Existing ceasefire breaks down

risk_increase

regime_change

Government falls or changes

risk_increase

diplomatic

Major diplomatic event (nuclear deal, treaty)

neutral

Data Schema

conflicts.json

Each conflict includes:

Field

Description

conflict_id

Region identifier (e.g. russia_ukraine, iran_israel_us)

importance

Editorial priority: high (active/major conflict, recommended for display) or low (low probability, included for data completeness)

risk_level

Overall risk: high / medium / low

probability_30d / 7d / 1d

P(event occurs within time horizon)

situation_briefing

Daily situation summary in Chinese, based on latest news

risk_impact

Structured impact analysis: industries[], assets[], channels[] (transmission paths)

risk_events[]

Breakdown by event type with per-type probabilities

risk_events[].direction

risk_increase (higher prob = more danger) or risk_decrease (higher prob = less danger)

risk_events[].change_vs_7d_ago

Probability delta vs. 7 days ago

data_points

Number of data sources (for confidence assessment)

anomaly_detected

Whether probability diverges from news intensity

political_events.json

Political, economic, and natural disaster events with probability estimates.

Field

Description

event_summary

Event description (Chinese)

event_summary_en

Event description (English)

category

political / economic / natural_disaster

probability

Estimated probability of occurrence

importance

Editorial priority: high (recommended for display) or low (data completeness)

deadline

Event deadline (YYYY-MM-DD), if applicable

data_confidence

high / medium / low (based on trading volume)

importance field

Both conflicts.json and political_events.json include an importance field:

  • high — Editorially recommended. Active conflicts, high-probability events, or events with significant recent changes. Matches the daily intelligence report's display filter.

  • low — Included for data completeness. Low probability, no active events, or not a current focus area. Agents may still find these useful for comprehensive monitoring.

maritime.json

⚠️ Suspended: Free AIS data (45-second snapshots) produces sporadic zero-vessel readings in busy straits, which could mislead agents into inferring blockades. Returns {"status": "unavailable", "zones": []}. Will resume when a reliable AIS source is found.

Use Cases

  • Trading agents: Adjust crypto/commodity positions based on geopolitical risk changes

  • Risk management: Monitor conflict escalation probabilities for portfolio hedging

  • DeFi protocols: Dynamic collateral ratios based on geopolitical risk

  • Research agents: Track probability trends across 6 conflict regions

  • News agents: Get structured risk data instead of parsing headlines

Technical Details

  • Zero dependencies: MCP server uses only Python stdlib (works with Python 3.9+)

  • Data source: Proprietary multi-source modeling

  • Update frequency: Daily

  • Latency: Public data has ~24h delay

License

Data is free for non-commercial use. Contact for commercial licensing.

Website & Research

futuristicrisks.com — Live risk dashboard, cascade impact analysis, daily verified intelligence, and API documentation.

Research articles:

Built by Futuristic Risk Intelligence.

Install Server
A
license - permissive license
A
quality
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
1Releases (12mo)
Commit activity

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