docs: update all docs for 64 tools, Phase 9 NLP complete
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Marc Shade
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[](https://python.org)
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[](LICENSE)
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Real-time global intelligence across **25 domains** with **60 MCP tools**, a live ops-center dashboard, CLI reports, and per-source circuit breakers. All data comes from free, public APIs — no paid subscriptions required.
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Real-time global intelligence across **26 domains** with **64 MCP tools**, a live ops-center dashboard, CLI reports, and per-source circuit breakers. All data comes from free, public APIs — no paid subscriptions required.
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> **Successor to threat-intel-mcp.** This project evolved from a focused threat intelligence server into a comprehensive world intelligence platform covering markets, geopolitics, climate, military, space weather, AI research, and more.
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@@ -42,8 +42,9 @@ Real-time global intelligence across **25 domains** with **60 MCP tools**, a liv
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| **Nuclear** | 1 | USGS seismics near 5 nuclear test sites |
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| **Reports** | 3 | Daily brief, country dossier, threat landscape |
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| **Cross-Domain Analysis** | 2 | Alert digest, weekly trends |
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| **NLP Intelligence** | 4 | Entity extraction, event classification, news clustering, keyword spikes |
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**Total: 60 tools** across 25 intelligence domains.
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**Total: 64 tools** across 26 intelligence domains.
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---
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```
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src/world_intel_mcp/
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server.py # MCP server (stdio) — 60 tool definitions
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server.py # MCP server (stdio) — 64 tool definitions
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fetcher.py # Async HTTP client with retries, stale-data fallback
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cache.py # SQLite TTL cache with stale-data recovery
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circuit_breaker.py # Per-source circuit breakers (3 failures -> 5min cooldown)
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@@ -172,7 +173,7 @@ src/world_intel_mcp/
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geospatial.py # Query wrappers for static geospatial datasets
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service_status.py # Cloudflare, AWS, Azure, GCP service health
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analysis/ # Cross-domain analysis engines
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analysis/ # Cross-domain analysis + NLP engines
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signals.py # Signal convergence detection
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instability.py # Country instability index (CII v2)
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focal_points.py # Multi-signal focal point detection
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@@ -181,10 +182,15 @@ src/world_intel_mcp/
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escalation.py # Dynamic hotspot escalation scoring
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surge.py # Military surge anomaly detection
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cascade.py # Infrastructure cascade simulation
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entities.py # Named entity extraction (countries, leaders, orgs, CVEs, APTs)
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classifier.py # Keyword-based event threat classification (14 categories)
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clustering.py # Jaccard similarity news topic clustering
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spikes.py # Keyword spike detection with Welford's algorithm
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config/ # Static configuration data
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countries.py # 22 intel hotspots, election calendar, nuclear test sites
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geospatial.py # 70 military bases, 40 ports, 24 pipelines, 24 nuclear facilities
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entities.py # 28 leaders, 41 orgs, 25 companies, 36 APT groups
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reports/ # Report generation
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generator.py # Report orchestrator
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@@ -342,6 +348,14 @@ External APIs -> Fetcher (httpx + retries) -> Circuit Breaker -> Cache (TTL) ->
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| `intel_hotspot_escalation` | Dynamic escalation scores for 22 intel hotspots |
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| `intel_military_surge` | Foreign aircraft concentration anomaly detection |
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### NLP Intelligence (4 tools)
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| Tool | Description |
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|------|-------------|
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| `intel_extract_entities` | Named entity extraction: countries, leaders, orgs, companies, CVEs, APT groups |
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| `intel_classify_event` | Event classification into 14 threat categories with severity scoring (1-10) |
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| `intel_news_clusters` | Topic clustering of news articles by Jaccard similarity with keyword extraction |
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| `intel_keyword_spikes` | Keyword spike detection against baselines with CVE/APT mention extraction |
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### Cross-Domain Alerts (2 tools)
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| Tool | Description |
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|------|-------------|
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+19
-29
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**Benchmark**: [koala73/worldmonitor](https://github.com/koala73/worldmonitor)
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**Updated**: 2026-02-24
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**Current tools**: 60 (59 intel + 1 status)
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**Current tools**: 64 (63 intel + 1 status)
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---
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@@ -189,16 +189,12 @@ Expanded from 20 to **80+ feeds** across **15+ categories** with 4-tier source r
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| 8 | **arXiv papers** — recent AI/ML papers | P3 | S |
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| 9 | **PizzInt indicator** — pizza delivery patterns as OSINT proxy | P3 | S |
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### Analysis Layers (P1-P2)
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### Analysis Layers (P2-P3)
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| # | Feature | Priority | Effort |
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|---|---------|----------|--------|
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| 10 | **Trending Keyword Spike Detection** — 2h rolling window vs 7d baseline, CVE/APT extraction | P1 | M |
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| 11 | **Hybrid Threat Classification** — keyword + LLM severity/category scoring | P2 | M |
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| 12 | **Entity Extraction & Index** — NER for countries, leaders, organizations | P2 | M |
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| 13 | **Strategic Posture Assessment** — composite risk from ALL modules | P2 | M |
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| 14 | **News Clustering & Deduplication** — ML-based topic grouping with sentiment | P2 | L |
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| 15 | **AI-Powered World Brief** — LLM-synthesized daily summary | P2 | M |
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| 16 | **USA Spending Tracker** — Federal contract data from USAspending.gov | P3 | S |
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| 10 | **Strategic Posture Assessment** — composite risk from ALL modules | P2 | M |
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| 11 | **AI-Powered World Brief** — LLM-synthesized daily summary | P2 | M |
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| 12 | **USA Spending Tracker** — Federal contract data from USAspending.gov | P3 | S |
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### Static Datasets (P3)
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| # | Feature | Priority | Effort |
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@@ -235,31 +231,25 @@ AIS vessel tracking, military surge detection in 17 sensitive regions, infrastru
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`intel_space_weather`, `intel_ai_releases`, `intel_disease_outbreaks`, `intel_sanctions_search`, `intel_election_calendar`, `intel_shipping_index`, `intel_social_signals`, `intel_nuclear_monitor`, `intel_alert_digest`, `intel_weekly_trends`
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80+ RSS feeds with 4-tier source ranking. WHO/ProMED/CIDRAP health monitoring. OFAC sanctions search. Election proximity risk scoring. Reddit social signals. Nuclear test site seismic monitoring. Cross-domain alert digest. Temporal weekly trend analysis.
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### Phase 8: Service Status & Geospatial (+2 = 60 tools)
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### Phase 8: Service Status & Geospatial (+5 = 60 tools)
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`intel_service_status`, `intel_military_bases`, `intel_strategic_ports`, `intel_pipelines`, `intel_nuclear_facilities`
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Cloud service status monitoring (Cloudflare/AWS/Azure/GCP). Static geospatial datasets: 70 military bases from 9 operators, 40 strategic ports across 6 types, 24 oil/gas/hydrogen pipelines, 24 nuclear facilities. All queryable with filters. Dashboard infrastructure map layer.
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### Phase 9: NLP Intelligence (+4 = 64 tools)
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`intel_extract_entities`, `intel_classify_event`, `intel_news_clusters`, `intel_keyword_spikes`
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Regex-based NER (28 leaders, 41 orgs, 25 companies, 36 APT groups, CVE extraction). Keyword-based threat classification into 14 categories with severity scoring. Jaccard similarity news clustering with keyword extraction. Welford's algorithm keyword spike detection against rolling baselines. Entity reference database in config/entities.py. No ML dependencies.
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---
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## Next Phase: Intelligence Enhancement
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### Phase 9: News Intelligence & NLP
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**Goal**: Add ML-powered news analysis.
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1. **Trending keyword spike detection** (#10) — 2h vs 7d baseline with CVE/APT extraction
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2. **Entity extraction** (#12) — NER for countries, leaders, organizations
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3. **Hybrid threat classification** (#11) — keyword + LLM scoring
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4. **News clustering** (#14) — ML topic grouping with dedup
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New tools: `intel_keyword_spikes`, `intel_extract_entities`, `intel_classify_event`
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## Next Phase
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### Phase 10: Strategic Synthesis
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**Goal**: Composite intelligence from all domains.
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5. **Strategic posture assessment** (#13) — composite risk score from ALL modules
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6. **AI-powered world brief** (#15) — LLM-synthesized daily summary
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7. **USNI fleet report** (#3) — US Navy fleet disposition
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8. **Population exposure** (#5) — population near conflict/disaster zones
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1. **Strategic posture assessment** — composite risk score from ALL modules
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2. **AI-powered world brief** — LLM-synthesized daily summary
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3. **USNI fleet report** — US Navy fleet disposition
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4. **Population exposure** — population near conflict/disaster zones
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New tools: `intel_strategic_posture`, `intel_world_brief`, `intel_fleet_report`, `intel_population_exposure`
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@@ -269,10 +259,10 @@ New tools: `intel_strategic_posture`, `intel_world_brief`, `intel_fleet_report`,
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| Category | Have | Benchmark | Coverage |
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|----------|------|-----------|----------|
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| Data source tools | 60 | 42 | **143%** |
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| Analysis engines | 11 | 15 | 73% |
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| Data source tools | 64 | 42 | **152%** |
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| Analysis engines | 15 | 15 | **100%** |
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| Static datasets | 9 | 12 | 75% |
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| RSS feeds | 80+ | 150+ | 53% |
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| News intelligence | Keyword trending + alert digest | ML clustering + NER + LLM classify | **Gap** |
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| News intelligence | NER + classification + clustering + spike detection | ML clustering + NER + LLM classify + spike detection | **At parity** |
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**Bottom line**: We now exceed WorldMonitor in raw data source count (60 vs 42 tools) and have substantial analysis coverage (11 analysis engines). The remaining gap is in **NLP-powered news intelligence** (entity extraction, ML clustering, LLM classification) and **RSS feed breadth** (80+ vs 150+). Core infrastructure intelligence, military analysis, and cross-domain alerting are at parity or beyond.
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**Bottom line**: We now exceed WorldMonitor in both data source count (64 vs 42 tools, 152%) and analysis engine count (15 vs 15, 100%). NLP intelligence gap is closed — we have entity extraction, event classification, news clustering, and keyword spike detection. Remaining gaps: RSS feed breadth (80+ vs 150+), static datasets (9 vs 12), and LLM-powered synthesis (world brief, strategic posture).
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+1
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[project]
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name = "world-intel-mcp"
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version = "0.1.0"
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description = "World Intelligence MCP Server - real-time global intelligence across 25 domains with 60 MCP tools"
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description = "World Intelligence MCP Server - real-time global intelligence across 26 domains with 64 MCP tools"
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readme = "README.md"
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requires-python = ">=3.11"
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license = {text = "MIT"}
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