World Intelligence MCP Server

Real-time global intelligence across 30+ domains with 109 MCP tools, a live ops-center dashboard, a CLI, and a Qdrant vector store for enterprise-grade semantic search across accumulated intelligence. All data comes from free, public APIs — no paid subscriptions required.

Built for AI agents that need world awareness: market conditions, geopolitical risk, military posture, supply chain disruptions, cyber threats, and more — all queryable via the Model Context Protocol. The vector store enables natural language queries like "military activity near Taiwan" or "cyber threats targeting healthcare" across all historical data.

What You Get

Total: 110 tools across 30+ intelligence domains.

Quick Start

Install

git clone https://github.com/marc-shade/world-intel-mcp.git
cd world-intel-mcp
pip install -e .

# Optional extras
pip install -e ".[dashboard]"  # Live ops-center dashboard
pip install -e ".[vector]"     # Qdrant vector store + FastEmbed
pip install -e ".[dev]"        # pytest, respx, coverage

Run as MCP Server

world-intel-mcp  # stdio mode for Claude Code, Cursor, etc.

Claude Code Configuration

Add to ~/.claude.json:

{
  "mcpServers": {
    "world-intel-mcp": {
      "command": "world-intel-mcp"
    }
  }
}

Dashboard

intel-dashboard              # http://localhost:8501
intel-dashboard --port 9000  # custom port

PDF/HTML Reports

pip install -e ".[pdf]"      # requires: brew install pango (macOS)
intel report                 # full PDF report → ~/.cache/world-intel-mcp/
intel report --format html   # HTML (no native deps needed)
intel report -o brief.pdf    # custom output path
intel report -s markets,cyber,earthquakes  # select sections

Map-first ops center: Leaflet map with toggle-able layers (quakes, military, conflict, fires, convergence, nuclear, infrastructure), 35+ live SSE feeds, HUD bar, glassmorphic panels, per-source circuit breaker health.

CLI

intel markets              # stock indices
intel earthquakes --min-mag 5.0
intel status               # cache + circuit breaker health

Architecture

server.py     (MCP stdio) ─┐                                               ┌─ VectorStore (Qdrant)
cli.py        (Click CLI)  ├─> sources/*.py ─> Fetcher ─> CircuitBreaker ─┤
dashboard.py  (SSE)        │    analysis/*.py                              └─ Cache (SQLite)
collector.py  (daemon)    ─┘

• Fetcher: Centralized async HTTP client (httpx). Retries, per-source rate limiting, stale-data fallback. Auto-stores results in vector store on fresh fetches.
• CircuitBreaker: Per-source tracking. 3 consecutive failures trips for 5 minutes. Each RSS feed gets its own breaker.
• Cache: SQLite WAL-mode TTL cache. get() returns live data, get_stale() returns expired data for fallback.
• VectorStore: Qdrant + FastEmbed (BAAI/bge-small-en-v1.5, 384-dim). Async background worker queue for non-blocking storage. Enables semantic search across all accumulated intelligence.
• Collector: Standalone daemon that fetches all 43 sources in parallel and populates the vector store. Run once or as a daemon (default: 5-minute interval).
• Sources (sources/*.py): 30+ modules, each exports async def fetch_*(fetcher, **kwargs) -> dict.
• Analysis (analysis/*.py): Cross-domain synthesis — signal aggregation, instability indexing, NLP, company enrichment, macro composite.
• Config (config/*.py): Curated datasets — 22 hotspots, 70+ bases, 40 ports, 24 pipelines, 24 nuclear facilities, 34 cables, 48 datacenters, 27 spaceports, 82 exchanges.

MCP Tools Reference

Financial Markets (7)

Forex & Currency (3)

Bonds & Yields (2)

Earnings (2)

SEC Filings (3)

Company Enrichment (1)

Macro Composite (1)

Economic (3)

Central Banks (1)

BTC Technicals (1)

Natural Disasters (2)

Environmental (2)

Conflict & Security (4)

Military & Defense (6)

Infrastructure (4)

Maritime (2)

Geospatial Datasets (10)

News & Media (3)

Intelligence Analysis (8)

NLP Intelligence (4)

Strategic Synthesis (4)

Climate (1)

Prediction Markets (1)

Elections (1)

Displacement (1)

Aviation (2)

Cyber Threats (1)

Space Weather (1)

AI & Technology (4)

Health (1)

Social & Sanctions (3)

Shipping & Trade (1)

Government (1)

Country Intelligence (3)

Extended Geospatial (1)

Traffic (2)

Cross-Domain Alerts (2)

Monitoring (2)

Vector Search (5)

Cross-Domain Analytics (3)

Reports (1)

Vector Store

The optional Qdrant vector store accumulates intelligence over time for semantic retrieval. All data fetched through the Fetcher is automatically embedded and stored.

Setup

# Install Qdrant (Docker)
docker run -p 6333:6333 qdrant/qdrant

# Install vector dependencies
pip install -e ".[vector]"

# Run the collector daemon (populates vector store 24/7)
intel-collector --daemon              # every 5 minutes
intel-collector --daemon --interval 120  # every 2 minutes
intel-collector --sources markets,cyber  # specific domains only
intel-collector                        # single collection cycle

Semantic Search Examples

Once data accumulates, AI agents can query across all domains:

• "military activity near Taiwan strait" — finds military flights, naval warnings, theater posture data
• "cyber threats targeting healthcare" — finds URLhaus, CISA KEV entries related to healthcare
• "economic indicators suggesting recession" — finds yield curve inversions, macro signals, FRED data

The vector store uses FastEmbed (ONNX-based, BAAI/bge-small-en-v1.5) for embeddings — no GPU required, ~3 second cold start.

Environment Variables

Everything else uses free, unauthenticated public APIs.

Development

pip install -e ".[dev]"
pytest                       # 186 tests
pytest --cov=world_intel_mcp # with coverage
pytest tests/test_forex.py -v # single module

Adding a New Source

1. Create sources/your_source.py with async def fetch_your_data(fetcher: Fetcher, **kwargs) -> dict
2. Use fetcher.get_json(url, source="your-source", cache_key=..., cache_ttl=300) — automatic caching, retries, circuit breaking, rate limiting
3. In server.py: add Tool(...) to TOOLS, add case to _dispatch() (use inline import)
4. Add tests using respx to mock HTTP (see tests/test_forex.py for pattern)
5. Optionally add to dashboard/app.py (SSE) and cli.py (Click)

License

MIT