Threat Intelligence MCP Server

World Intelligence MCP Server
Echtzeit-Intelligenz für die ganze Welt über 30+ Domänen mit 120 MCP-Tools, einem Live-Ops-Center-Dashboard, einer CLI und einem Qdrant-Vektorstore für unternehmensweite semantische Suche über gesammelte Intelligenzdaten. Alle Daten stammen aus kostenlosen, öffentlichen APIs: keine kostenpflichtigen Abonnements erforderlich.
Entwickelt für KI-Agenten, die Weltwissen benötigen: Marktbedingungen, geopolitische Risiken, militärische Lage, Störungen in Lieferketten, Cyber-Bedrohungen und mehr – alles abfragbar über das Model Context Protocol. Der Vektorstore ermöglicht natürlichsprachliche Abfragen wie "militärische Aktivität nahe Taiwan" oder "Cyber-Bedrohungen gegen das Gesundheitswesen" über alle historischen Daten.
Was Sie erhalten
Domäne | Tools | Datenquellen |
Finanzmärkte | 7 | Yahoo Finance, CoinGecko, Alternative.me, Mempool |
Forex & Währungen | 3 | ECB/Frankfurter (8 Hauptwährungspaare, Zeitreihen, Kreuzkurse) |
Anleihen & Renditen | 2 | FRED, Yahoo Finance (Zinskurve, Anleihen-ETFs, Spread-Analyse) |
Unternehmensgewinne | 2 | Yahoo Finance (Mega-Cap-Kalender, Überraschungshistorie) |
SEC-Einreichungen | 3 | SEC EDGAR (Volltextsuche, Unternehmenseinreichungen, 8-K-Wesentliche Ereignisse) |
Unternehmensanreicherung | 1 | Yahoo Finance + GDELT + SEC + GitHub (zusammengesetztes Profil) |
Makro-Komposit | 1 | Gewichtetes 6-Signal-Markturteil (Fear&Greed, VIX, Sektoren, DXY, BTC, Renditen) |
Wirtschaftsindikatoren | 6 | AAA-Kraftstoffpreise, EIA-Energie, FRED-Makro, Weltbank |
Zentralbanken | 1 | 15 Zentralbank-Leitzinsen |
BTC-Technik | 1 | SMA 50/200, Golden/Death Cross, Mayer-Multiple |
Naturkatastrophen | 2 | USGS-Erdbeben, NASA FIRMS-Waldbrände |
Umwelt | 2 | NASA EONET, GDACS-Katastrophenwarnungen |
Klima | 1 | Open-Meteo-Temperatur-/Niederschlagsanomalien |
Konflikt & Sicherheit | 4 | ACLED-Ereignisse, UCDP, Unruheerkennung, humanitäre Daten |
Militär & Verteidigung | 6 | adsb.lol, OpenSky, hexdb.io, Anstiegserkennung, Theater-Posture, Flugzeug-Batch |
Infrastruktur | 4 | Cloudflare Radar, Unterseekabel, Kaskadenanalyse, Cloud-Status |
Maritim | 2 | NGA-Schifffahrtswarnungen, Schiffs-Snapshots |
Luftfahrt | 2 | FAA-Flughafenverspätungen, Inlandsflug-Snapshot |
Nachrichten & Medien | 3 | 119 RSS-Feeds (4-stufig), GDELT, Trend-Keywords |
Intelligenzanalyse | 8 | Signal-Konvergenz, Brennpunkte, Instabilitätsindex, Risikowerte, Eskalation |
NLP-Intelligenz | 4 | Entitätsextraktion, Ereignisklassifikation, Nachrichten-Clustering, Keyword-Spitzen |
Strategische Synthese | 4 | Strategische Lage, Welt-Briefing, Flottenbericht, Bevölkerungs-Exposition |
Geodaten | 11 | Militärstützpunkte, Häfen, Pipelines, Nuklearanlagen, Kabel, Rechenzentren, Raumhäfen, Mineralien, Börsen, Handelsrouten, Cloud-Regionen |
KI & Technologie | 4 | arXiv-Papiere, HuggingFace-Modelle, Hacker News, GitHub-Trends |
Cyber-Bedrohungen | 1 | URLhaus, Feodotracker, CISA KEV, SANS |
Gesundheit | 1 | WHO DON, ProMED, CIDRAP-Krankheitsausbrüche |
Weltraumwetter | 1 | NOAA SWPC (Kp-Index, Sonneneruptionen, Warnungen) |
Soziales & Sanktionen | 3 | Reddit-Velocity, OFAC-SDN-Liste, Überwachung von Atomtestgeländen |
Länder-Intelligenz | 3 | Länder-Briefing, Länderaktien, Finanzzentren |
Prognosemärkte | 1 | Polymarket-Ereigniskontrakte |
Wahlen | 1 | Globaler Wahlkalender mit Risikobewertung |
Vertreibung | 1 | UNHCR-Flüchtlings-/Binnenvertriebenendaten |
Schifffahrt | 1 | Trockenschüttgut-Schifffahrtsstressindex |
Regierung | 1 | USAspending.gov-Bundesaufträge |
Verkehr | 2 | Straßenverkehrsfluss, Echtzeit-Vorfälle |
Domänenübergreifende Warnungen | 2 | Warnungs-Digest, Wochentrends |
Überwachung | 2 | Webcams, Serverzustand/Status |
Vektorsuche | 5 | Qdrant-semantische Suche, Ähnlichkeit, Zeitachse, Statistiken |
Domänenübergreifende Analytik | 3 | Korrelation, Domänen-Zusammenfassung, Trend-Erkennung |
Berichte | 1 | PDF/HTML-Mehrdomänen-Intelligenzberichte |
Täglicher Digest | 1 | Zitierter Markdown-Morgenbrief: Top-Ereignisse, Schlagzeilen, Trends und Zeitachse |
AOI-Geofences | 5 | Benutzerdefinierte Interessensgebiete: definieren/auflisten/löschen, ein zitierter Mehrdomänen-Brief und Hotspot-Eskalationsbewertung für das eigene Gebiet |
Lagebrief | 1 | Zitierter Lagebewusstseins-Brief über MCP: begrenzte serverseitige Übersicht, synthetisiert über lokales Ollama, mit mechanisch zitiertem Fallback |
Gesamt: 120 Tools in über 30 Intelligenz-Domänen.
Related MCP server: MCP Threat Intel Server
Schnellstart
Installation
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, coverageAls MCP-Server ausführen
world-intel-mcp # stdio mode for Claude Code, Cursor, etc.Claude-Code-Konfiguration
Zu ~/.claude.json hinzufügen:
{
"mcpServers": {
"world-intel-mcp": {
"command": "world-intel-mcp"
}
}
}Dashboard
intel-dashboard # http://localhost:8501
intel-dashboard --port 9000 # custom portPDF/HTML-Berichte
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 sectionsKartenbasiertes Ops-Center: Leaflet-Karte mit umschaltbaren Ebenen (Erdbeben, Militär, Konflikte, Brände, Konvergenz, Nuklear, Infrastruktur), 47 Live-SSE-Feeds, HUD-Leiste, glasmorphische Panels, Circuit-Breaker-Zustand pro Quelle.
CLI
intel markets # stock indices
intel earthquakes --min-mag 5.0
intel status # cache + circuit breaker healthArchitektur
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: Zentralisierter asynchroner HTTP-Client (httpx). Wiederholungsversuche, Ratenbegrenzung pro Quelle, Fallback bei veralteten Daten. Speichert Ergebnisse bei neuen Abrufen automatisch im Vektorstore.
CircuitBreaker: Verfolgung pro Quelle. 3 aufeinanderfolgende Fehlschläge lösen für 5 Minuten aus. Jeder RSS-Feed erhält seinen eigenen Breaker.
Cache: SQLite-WAL-Modus-TTL-Cache.
get()liefert Live-Daten,get_stale()liefert abgelaufene Daten als Fallback.VectorStore: Qdrant + FastEmbed (BAAI/bge-small-en-v1.5, 384-dim). Asynchrone Hintergrund-Warteschlange für nicht blockierende Speicherung. Ermöglicht semantische Suche über alle gesammelten Intelligenzdaten.
Collector: Eigenständiger Daemon, der alle 46 Quellen parallel abruft und den Vektorstore befüllt. Einmalig oder als Daemon ausführbar (Standard: 5-Minuten-Intervall).
Quellen (
sources/*.py): 30+ Module, jedes exportiertasync def fetch_*(fetcher, **kwargs) -> dict.Analyse (
analysis/*.py): Domänenübergreifende Synthese – Signalaggregation, Instabilitätsindexierung, NLP, Unternehmensanreicherung, Makro-Komposit.Konfiguration (
config/*.py): Kuratierte Datensätze – 22 Hotspots, 70+ Stützpunkte, 40 Häfen, 24 Pipelines, 24 Nuklearanlagen, 34 Kabel, 48 Rechenzentren, 27 Raumhäfen, 82 Börsen.
MCP-Tools-Referenz
Finanzmärkte (7)
Tool | Beschreibung |
| Aktienindexkurse (S&P 500, Dow, Nasdaq, FTSE, Nikkei) |
| Top-Kryptopreise und Marktkapitalisierungen von CoinGecko |
| Stablecoin-Peg-Gesundheit (USDT, USDC, DAI, FDUSD) |
| Bitcoin-Spot-ETF-Preise und -Volumina |
| Performance der US-Aktiensektoren (11 SPDR-ETFs) |
| 7 Makroindikatoren (Fear & Greed, VIX, DXY, Gold, 10Y, BTC) |
| Rohstoff-Futures (Gold, Silber, Rohöl, Erdgas, Getreide) |
Forex & Währungen (3)
Tool | Beschreibung |
| Aktuelle FX-Kurse von der EZB. Nach Basis-/Zielwährungen filtern |
| Historische FX-Kurse mit Trendanalyse (konfigurierbare Tage) |
| Alle 8 Hauptpaare + Kreuzkurse + DXY-Stellvertreter |
Anleihen & Renditen (2)
Tool | Beschreibung |
| US-Treasury-Zinskurve (2Y-30Y), 2s10s/3m10y-Spreads, Inversionsflagge |
| Anleihen-ETFs: AGG, TLT, HYG, LQD, TIP mit Kurs/Änderung |
Gewinne (2)
Tool | Beschreibung |
| Anstehende Gewinne für 20 Mega-Cap-Aktien mit EPS-Schätzungen |
| Historische Gewinnüberraschungen (tatsächlich vs. Schätzung, Trend) |
SEC-Einreichungen (3)
Tool | Beschreibung |
| Volltextsuche über alle EDGAR-Einreichungen |
| Unternehmenseinreichungen nach Ticker (10-K, 10-Q, 8-K) mit CIK-Auflösung |
| Aktuelle 8-K-wesentliche Ereignisse (M&A, Führungswechsel, Gewinne) |
Unternehmensanreicherung (1)
Tool | Beschreibung |
| Zusammengesetztes Profil: Aktienkurs + Finanzdaten + Nachrichten + SEC + GitHub |
Makro-Verbund (1)
Tool | Beschreibung |
| Gewichteter Markt-Score (0-100) mit Urteil: RISK_ON bis STRONG_CAUTION |
Wirtschaft (6)
Tool | Beschreibung |
| Tägliche US-Benzin-, Diesel- und E85-Preise von AAA |
| US-Erdgaspreise für Privathaushalte von der EIA |
| US-Stromtarife nach Sektor/Bundesstaat von der EIA |
| Brent/WTI-Rohöl und Erdgas von der EIA |
| FRED-Wirtschaftsdaten (BIP, VPI, Arbeitslosigkeit, Zinsen) |
| Entwicklungsindikatoren der Weltbank nach Ländern |
Zentralbanken (1)
Tool | Beschreibung |
| Leitzinsen für 15 große Zentralbanken |
BTC-Technik (1)
Tool | Beschreibung |
| Bitcoin SMA 50/200, Golden/Death Cross, Mayer-Multiple |
Naturkatastrophen (2)
Tool | Beschreibung |
| USGS-Erdbeben (konfigurierbare Magnitude/Zeit/Limit) |
| NASA-FIRMS-Satellitenfeuer-Hotspots (9 globale Regionen) |
Umwelt (2)
Tool | Beschreibung |
| NASA-EONET-Naturereignisse |
| GDACS-Katastrophenwarnungen mit Schweregradbewertung |
Konflikt & Sicherheit (4)
Tool | Beschreibung |
| ACLED-Bewaffnete-Konflikt-Ereignisse |
| Ereignisse des Uppsala Conflict Data Program |
| Soziale Unruhen mit Haversine-Deduplizierung |
| HDX-Datensätze zu humanitären Krisen |
Militär & Verteidigung (6)
Tool | Beschreibung |
| Militärflugzeuge über adsb.lol (OpenSky-Fallback) |
| Aktivität in 5 Einsatzgebieten (EU, Indopazifik, Naher Osten, Arktis, Korea) |
| Flugzeugsuche nach ICAO24-Hex (hexdb.io) |
| Stapel-Flugzeugsuche (mehrere Hex-Codes) |
| Erkennung von Anomalien bei Konzentration ausländischer Flugzeuge |
| USNI-News-Tracker für Marineflotten |
Infrastruktur (4)
Tool | Beschreibung |
| Cloudflare-Radar-Internetstörungen |
| Zustand der Unterseekabel-Korridore |
| Kaskadensimulation der Infrastruktur |
| Cloud-Plattform-Status (AWS, Azure, GCP, Cloudflare, GitHub) |
Schifffahrt (2)
Tool | Beschreibung |
| NGA-Seenavigationswarnungen |
| Marineaktivität an 9 strategischen Wasserstraßen |
Geodaten-Datensätze (10)
Tool | Beschreibung |
| 70 Militärstützpunkte von 9 Betreibern |
| 40 strategische Häfen in 6 Typen |
| 24 Öl-/Gas-/Wasserstoffpipelines |
| 24 Kernkraft-/Anreicherungs-/Forschungseinrichtungen |
| 34 Unterseekommunikationskabel |
| 48 KI-/HPC-Rechenzentren weltweit |
| 27 globale Weltraumbahnhöfe |
| 27 strategische Mineralvorkommen |
| 82 Börsen weltweit |
| Wichtige Handelsrouten und Engpässe |
Nachrichten & Medien (3)
Tool | Beschreibung |
| 119 globale RSS-Feeds mit 4-stufigem Quellenranking |
| Trendbegriffe mit Spike-Erkennung |
| GDELT-2.0-Weltnachrichtensuche |
Intelligence-Analyse (8)
Tool | Beschreibung |
| Geografische Konvergenz von Multidomänen-Signalen |
| Erkennung von Brennpunkten bei mehreren Signalen |
| Signalaggregation auf Länderebene |
| Abweichungen der Aktivität von Basiswerten |
| Länder-Instabilitätsindex v2 (0-100) |
| ACLED-basierte Konfliktrisikobewertung |
| Eskalationswerte für 22 Intel-Hotspots |
| Umfassendes Länder-Intelligence-Dossier |
NLP-Intelligence (4)
Tool | Beschreibung |
| Extraktion benannter Entitäten (Länder, Führungspersönlichkeiten, Organisationen, CVEs, APTs) |
| Ereignisklassifizierung in 14 Bedrohungskategorien |
| Themen-Clustering nach Jaccard-Ähnlichkeit |
| Keyword-Spike-Erkennung mit dem Welford-Algorithmus |
Strategische Synthese (4)
Tool | Beschreibung |
| Zusammengesetztes globales Risiko aus 9 gewichteten Domänen |
| Strukturierte tägliche Intelligence-Zusammenfassung |
| Bericht über Marineflottenaktivitäten mit Bereitschaftsbewertung |
| Bevölkerung in Gefahr in der Nähe aktiver Ereignisse (Datensatz mit 105 Städten) |
Klima (1)
Tool | Beschreibung |
| Open-Meteo-Temperatur-/Niederschlagsanomalien |
Prognosemärkte (1)
Tool | Beschreibung |
| Polymarket-Prognosekontrakte |
Wahlen (1)
Tool | Beschreibung |
| Globaler Wahlkalender mit Risikobewertung |
Vertreibung (1)
Tool | Beschreibung |
| UNHCR-Flüchtlings-/Binnenvertriebenen-Statistiken |
Luftfahrt (2)
Tool | Beschreibung |
| FAA-Flughafenverspätungsstatus |
| Globaler Luftverkehrs-Schnappschuss von OpenSky |
Cyber-Bedrohungen (1)
Tool | Beschreibung |
| Aggregierte Cyber-Intelligence (URLhaus, CISA KEV, SANS) |
Weltraumwetter (1)
Tool | Beschreibung |
| Sonnenaktivität (Kp-Index, Röntgenfluss, SWPC-Warnungen) |
KI & Technologie (4)
Tool | Beschreibung |
| arXiv-KI-Papiere, HuggingFace-Modelle |
| Hacker News Top-Storys |
| GitHub-Trending-Repositories |
| arXiv-Papiersuche |
Gesundheit (1)
Tool | Beschreibung |
| WHO DON, ProMED, CIDRAP-Ausbrüche |
Soziales & Sanktionen (3)
Tool | Beschreibung |
| Geschwindigkeit geopolitischer Diskussionen auf Reddit |
| Suche in der OFAC-SDN-Liste |
| Seismische Überwachung in der Nähe von Atomtestgeländen |
Schifffahrt & Handel (1)
Tool | Beschreibung |
| Trockenmassengut-Schifffahrtsstressindex |
Regierung (1)
Tool | Beschreibung |
| USAspending.gov-Bundesverträge |
Länderinformationen (3)
Tool | Beschreibung |
| Kurze Länderlageübersicht |
| Börsen und Listings nach Ländern |
| Ranking globaler Finanzzentren |
Erweiterte Geodaten (1)
Tool | Beschreibung |
| Cloud-Anbieterregionen weltweit |
Verkehr (2)
Tool | Beschreibung |
| Straßenverkehrsflussdaten |
| Echtzeit-Verkehrsvorfälle |
Domänenübergreifende Warnungen (2)
Tool | Beschreibung |
| Zusammenfassung domänenübergreifender Warnungen |
| Wöchentliche Trendanalyse |
Überwachung (2)
Tool | Beschreibung |
| Öffentliche Webcam-Standorte und Live-Vorschauen |
| Serverzustand, Cache-Statistiken, Circuit-Breaker-Status |
Vektorsuche (5)
Tool | Beschreibung |
| Suche in natürlicher Sprache über alle gesammelten Informationen |
| Ähnliche Ereignisse zu einem bestimmten Datenpunkt finden |
| Chronologische Ansicht der Informationen für eine Domäne/Kategorie |
| Statistiken zur Vektor-Store-Sammlung |
| Einen bedarfsgesteuerten Sammelzyklus auslösen |
Domänenübergreifende Analytik (3)
Tool | Beschreibung |
| Korrelierte Signale über alle Domänen für ein bestimmtes Thema finden |
| Zusammenfassung der gespeicherten Informationen pro Kategorie (Anzahl, Quellen, Aktualität) |
| Aktivitätsspitzen/-abfälle erkennen durch Vergleich aktueller mit Basisperioden |
Berichte (1)
Tool | Beschreibung |
| Erzeugt einen PDF- oder HTML-Intelligenzbericht, der 18 Domänen parallel abdeckt |
AOI-Geofences (5)
Tool | Beschreibung |
| Definiert einen benannten Interessensbereich: Punkt + Radius in km (1-2000) |
| Listet alle benutzerdefinierten AOIs auf |
| Löscht einen benutzerdefinierten AOI nach Namen |
| Zitierter Kurzbericht für einen AOI: Erdbeben, Militärflüge, Waldbrände, Konfliktereignisse, Luftfahrt, nahe Infrastruktur und Nachrichtenerwähnungen, alle auf den Radius des AOI gefiltert |
| Hotspot-Eskalationsbewertung (gleiche Engine wie die 22 integrierten Hotspots), angewendet auf einen Benutzer-AOI |
Situationsbericht (1)
Tool | Beschreibung |
| Zitierter Lagebewusstseinsbericht, auf Abruf über MCP generiert: eine begrenzte serverseitige Übersicht (Erdbeben, Militärflüge, ACLED-Konfliktereignisse, Waldbrände, Cyber-Bedrohungen, Krankheitsausbrüche, Nachrichten, Weltraumwetter, strategische Haltung, Warnungsübersicht), synthetisiert über lokales Ollama oder einen mechanisch zitierten Fallback, wenn Ollama nicht erreichbar ist |
Beobachten Sie Ihr eigenes Gebiet (Geofences/AOIs)
Statische Infrastrukturergebnisse (Stützpunkte, Häfen, Nuklearanlagen, Kabel, Rechenzentren, Raumhäfen) stützen sich auf die kuratierten strategischen Datensätze dieses Repos, die global und bewusst spärlich sind, nicht auf erschöpfende lokale Register. Ein ruhiger AOI-Bericht bedeutet, dass nichts aus diesen kuratierten Sätzen in Reichweite ist, nicht dass Ihr Gebiet keine Infrastruktur hat.
28 der 120 Tools akzeptieren einen geografischen Parameter, aber vor der AOI-Familie akzeptierte nur intel_signal_convergence einen echten Punkt-plus-Radius, intel_military_flights nahm eine Bbox, und die Hotspot-Eskalationsbewertung war auf die 22 fest codierten INTEL_HOTSPOTS beschränkt. Die intel_aoi_*-Tools ermöglichen es Ihnen, Ihr eigenes Gebiet zu benennen (eine Stadt, eine Grenzregion, eine Einrichtung) und dieselbe zitierte, domänenübergreifende Behandlung zu erhalten.
Definieren Sie einen AOI einmal, und erstellen Sie dann auf Abruf einen Bericht und eine Bewertung:
intel_aoi_define(name="Pittsburgh", lat=40.4406, lon=-79.9959, radius_km=50)
intel_aoi_brief(name="Pittsburgh")
intel_aoi_escalation(name="Pittsburgh")intel_aoi_brief filtert jede geofähige Domäne auf den 50-km-Radius um Pittsburgh: Erdbeben, Militärflüge (Bbox aus dem Radius abgeleitet), Waldbrände (regionbezogen, da NASA FIRMS keine Punkt+Radius-Abfrage hat), ACLED-Konfliktereignisse, eine Stichprobe des nahen Luftverkehrs, nahe statische Infrastruktur (Militärstützpunkte, Häfen, Pipelines, Nuklearanlagen, Unterseekabel, Rechenzentren, Raumhäfen) mit Entfernungen in km und Nachrichtenschlagzeilen, die "Pittsburgh" erwähnen. Jedes Element in der Antwort trägt eine [n]-Zitation in eine nummerierte sources-Liste, und data_gaps nennt jede Domäne, die nicht auf den AOI eingegrenzt werden konnte (z. B. Waldbrände, wenn der AOI außerhalb der Abdeckungsregionen von NASA FIRMS liegt, oder Konfliktereignisse, wenn ACLED-Anmeldeinformationen nicht konfiguriert sind), anstatt sie stillschweigend wegzulassen.
intel_aoi_escalation verwendet dieselbe Basis-/Militär-/Konflikt-/soziale Unruhen-Bewertungsengine, die intel_hotspot_escalation für die 22 integrierten Hotspots antreibt, jedoch auf den eigenen Radius Ihres AOI beschränkt statt auf ein festes 2-Grad-Fenster.
AOIs werden in einer eigenen Tabelle in derselben SQLite-Cache-Datenbank gespeichert, die der Server bereits verwendet (standardmäßig ~/.cache/world-intel-mcp/cache.db oder $WORLD_INTEL_CACHE_DB), sodass ein geplanter Agent jedes benannte Gebiet über Neustarts hinweg mit intel_aoi_list / intel_aoi_delete überwachen und verwalten kann.
Vektor-Store
Der optionale Qdrant-Vektor-Store sammelt im Laufe der Zeit Informationen für die semantische Abfrage. Alle über den Fetcher abgerufenen Daten werden automatisch eingebettet und gespeichert.
Einrichtung
# 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 cycleAls macOS-launchd-Dienst ausführen
scripts/collector-daemon.sh verwaltet den Collector als launchd-Agent, sodass er Neustarts übersteht. Es füllt com.agentic.intel-collector.plist.template mit dem eigenen Pfad dieses Checkouts aus (aufgelöst aus dem eigenen Speicherort des Skripts, sodass es aus jedem Klon funktioniert) und installiert das Ergebnis in ~/Library/LaunchAgents/.
scripts/collector-daemon.sh start # install + load the launchd job
scripts/collector-daemon.sh status # check state and log info
scripts/collector-daemon.sh logs # tail stdout (logs err for stderr)
scripts/collector-daemon.sh stop # unload the launchd job
scripts/collector-daemon.sh restart
scripts/collector-daemon.sh render # print the filled-in plist without installing itBeispiele für die semantische Suche
Sobald Daten gesammelt wurden, können KI-Agenten domänenübergreifend abfragen:
"militärische Aktivität in der Nähe der Taiwanstraße" — findet Militärflüge, Marinewarnungen, Daten zur Lage im Einsatzgebiet
"Cyber-Bedrohungen gegen das Gesundheitswesen" — findet URLhaus-, CISA-KEV-Einträge im Zusammenhang mit dem Gesundheitswesen
"Wirtschaftsindikatoren, die auf eine Rezession hindeuten" — findet Zinskurveninversionen, Makrosignale, FRED-Daten
Der Vektor-Store verwendet FastEmbed (ONNX-basiert, BAAI/bge-small-en-v1.5) für Einbettungen — keine GPU erforderlich, ~3 Sekunden Kaltstart.
Umgebungsvariablen
Variable | Erforderlich | Beschreibung |
| Nein | ACLED-Konfliktereignisse |
| Nein | Satelliten-Waldbranddaten |
| Nein | Energiepreisdaten |
| Nein | Internetausfalldaten |
| Nein | Makroökonomische Daten (auch für Zinskurve verwendet) |
| Nein | Fallback für Militärflüge |
| Nein | Fallback für Militärflüge |
| Nein | Ollama-Server für KI-generierte Berichte (Standard: |
| Nein | Ollama-Modell für KI-generierte Berichte (Standard: |
| Nein | Protokollierungsgrad (Standard: INFO) |
Alles andere verwendet kostenlose, nicht authentifizierte öffentliche APIs.
Entwicklung
pip install -e ".[dev]"
pytest # 251 tests (269 total, 18 live-network smoke tests deselected by default)
pytest --cov=world_intel_mcp # with coverage
pytest tests/test_forex.py -v # single moduleHinzufügen einer neuen Quelle
Erstellen Sie
sources/your_source.pymitasync def fetch_your_data(fetcher: Fetcher, **kwargs) -> dictVerwenden Sie
fetcher.get_json(url, source="your-source", cache_key=..., cache_ttl=300)— automatisches Caching, Wiederholungen, Circuit Breaking, Rate LimitingIn
server.py: fügen SieTool(...)zuTOOLShinzu, fügen Siecasezu_dispatch()hinzu (verwenden Sie Inline-Import)Fügen Sie Tests mit
respxhinzu, um HTTP zu mocken (siehetests/test_forex.pyfür das Muster)Optional zu
dashboard/app.py(SSE) undcli.py(Click) hinzufügen
Lizenz
MIT
Available Tools
11 toolscheck_bulk_ipsC
Check multiple IP addresses against threat feeds in bulk.
Args: ips: JSON array of IP addresses or comma-separated list
Returns: JSON with reputation results for all IPs
| Name | Required | Description | Default |
|---|---|---|---|
| ips | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions bulk checking against threat feeds but lacks critical behavioral details: it doesn't specify rate limits, authentication needs, data sources, or what happens on errors. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections add structure without redundancy. However, the 'Returns' section could be more concise, as the output schema exists, making some details unnecessary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (bulk IP checking), no annotations, and an output schema present, the description is partially complete. It covers the basic purpose and parameter format but lacks usage guidelines, behavioral context, and error handling details. The output schema reduces the need to explain return values, but overall completeness is adequate with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds value by explaining that 'ips' accepts a 'JSON array of IP addresses or comma-separated list', which clarifies the input format beyond the schema's 'type: string'. However, it doesn't detail validation rules, IP format requirements, or size limits, leaving some semantics unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check multiple IP addresses against threat feeds in bulk.' It specifies the verb ('check'), resource ('IP addresses'), and scope ('bulk'), distinguishing it from single-IP tools like 'check_ip_reputation'. However, it doesn't explicitly differentiate from other bulk tools like 'check_network_against_threats', keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over 'check_ip_reputation' for single IPs or how it differs from 'check_network_against_threats' for bulk checks. No exclusions or prerequisites are stated, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_hash_reputationA
Check a file hash (MD5/SHA1/SHA256) against threat intelligence.
Args: file_hash: File hash to check
Returns: JSON with reputation data
| Name | Required | Description | Default |
|---|---|---|---|
| file_hash | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool checks against threat intelligence, but does not disclose behavioral traits such as rate limits, authentication needs, data sources, or error handling. This leaves significant gaps for a tool that likely queries external services.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose, followed by structured sections for args and returns. It avoids unnecessary details, though the 'Args' and 'Returns' headings could be integrated more seamlessly into the flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (returns JSON with reputation data), the description does not need to explain return values. It covers the basic purpose and parameter semantics adequately, but could improve by adding more behavioral context (e.g., rate limits) to compensate for the lack of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds meaning by specifying the parameter as a 'file hash' and listing supported hash types (MD5/SHA1/SHA256). However, it does not detail format constraints (e.g., length, case sensitivity) or provide examples, leaving some ambiguity beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('check') and resource ('file hash') against a target ('threat intelligence'). It distinguishes from siblings by specifying hash checking (vs. IPs, networks, feeds, etc.) and mentions supported hash types (MD5/SHA1/SHA256), making it unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking file hashes against threats, but does not explicitly state when to use this tool versus alternatives like check_ip_reputation or check_bulk_ips. It provides some context (e.g., hash types) but lacks explicit guidance on exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_ip_reputationC
Check an IP address against multiple threat intelligence sources.
Args: ip: IP address to check
Returns: JSON with reputation data from multiple sources
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'multiple threat intelligence sources' but doesn't specify which sources, latency, rate limits, authentication needs, or error handling. For a tool that likely queries external APIs, this leaves critical operational details unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by structured 'Args' and 'Returns' sections. It's efficient with minimal waste, though the 'Returns' section could be more specific about the JSON structure instead of just stating 'JSON with reputation data'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, threat intelligence query), the description covers the basics but lacks depth. The output schema exists, so return values needn't be detailed, but behavioral aspects like source reliability or rate limits are missing, making it adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, but the description explicitly documents the single parameter ('ip: IP address to check'), adding essential meaning beyond the bare schema. However, it doesn't provide format details (e.g., IPv4 vs. IPv6) or validation rules, so it only partially compensates for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check an IP address against multiple threat intelligence sources.' It specifies the verb ('check') and resource ('IP address'), though it doesn't explicitly differentiate from sibling tools like 'check_bulk_ips' or 'check_hash_reputation' beyond the IP focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'check_bulk_ips' for multiple IPs or 'check_hash_reputation' for non-IP checks. It lacks context on prerequisites, limitations, or exclusions, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_network_against_threatsC
Check network scan results against threat intelligence.
Args: scan_results: JSON string from network scanner with device IPs
Returns: JSON with any matched threats
| Name | Required | Description | Default |
|---|---|---|---|
| scan_results | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool checks against threat intelligence and returns JSON with matches, but lacks critical details: whether this is a read-only operation, if it requires authentication, rate limits, what happens on errors, or if it modifies any state (e.g., updates a cache). For a security tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by structured 'Args' and 'Returns' sections. Each sentence earns its place by providing essential information without redundancy. Minor improvements could include integrating the sections more fluidly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (security analysis), no annotations, and an output schema exists (implied by 'Returns: JSON'), the description is moderately complete. It covers the basic operation and parameter semantics but lacks behavioral context (e.g., safety, performance) and usage guidelines. The output schema reduces the need to explain return values, but more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that 'scan_results' is a 'JSON string from network scanner with device IPs', which clarifies the parameter's format and content beyond the schema's generic 'string' type. However, it doesn't detail the exact JSON structure or provide examples, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check network scan results against threat intelligence.' It specifies the verb ('check') and resource ('network scan results'), and distinguishes it from siblings like check_ip_reputation by focusing on bulk scan results rather than individual IPs. However, it doesn't explicitly differentiate from check_bulk_ips, which might be a similar sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like check_bulk_ips or check_ip_reputation. It mentions 'scan results' but doesn't clarify prerequisites (e.g., requires prior network scanning) or exclusions (e.g., not for single IPs). This leaves the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clear_threat_cacheB
Clear the threat intelligence cache to force fresh data fetch.
Returns: JSON confirmation
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It states the action ('clear cache') and outcome ('force fresh data fetch'), but lacks critical behavioral details: it doesn't specify permissions required, whether this is destructive (e.g., deletes cached data), rate limits, or side effects on other tools. The mention of 'JSON confirmation' is vague about response structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: two brief sentences that front-load the core action and mention the return type without redundancy. Every sentence adds value, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, output schema exists), the description is moderately complete. It covers the basic purpose and return format, but as a mutation tool with no annotations, it should ideally include more behavioral context (e.g., safety, permissions) to be fully helpful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add param details, which is appropriate, earning a baseline score of 4 for not introducing unnecessary information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Clear') and resource ('threat intelligence cache'), and distinguishes it from siblings by focusing on cache management rather than threat checking or data retrieval. However, it doesn't explicitly differentiate from all siblings (e.g., 'fetch_threat_feed' also involves data fetching).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance: it implies usage when fresh data is needed, but offers no explicit when/when-not rules, prerequisites, or alternatives. It doesn't compare with siblings like 'fetch_threat_feed' or 'get_threat_feeds' that might overlap in data freshness contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetch_threat_feedB
Fetch and parse a specific threat intelligence feed.
Args: feed_name: Name of the feed (feodo_tracker, urlhaus_recent, etc.)
Returns: JSON with IOCs from the feed
| Name | Required | Description | Default |
|---|---|---|---|
| feed_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool fetches and parses a feed, implying a read operation, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or whether it caches results. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections are structured clearly, though they could be integrated more seamlessly. There's minimal waste, but it could be slightly more polished in flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (returns JSON with IOCs), the description doesn't need to explain return values in detail. It covers the basic purpose and parameter semantics adequately. However, with no annotations and incomplete behavioral transparency, it could do more to address gaps like error cases or performance considerations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, but the description compensates by explaining the 'feed_name' parameter: 'Name of the feed (feodo_tracker, urlhaus_recent, etc.)'. This adds meaning beyond the bare schema, providing examples and context. However, it doesn't detail all possible feed names or constraints, so it partially addresses the coverage gap but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Fetch and parse a specific threat intelligence feed.' It specifies the verb ('fetch and parse') and resource ('threat intelligence feed'), distinguishing it from siblings like 'check_ip_reputation' or 'get_recent_iocs' that focus on reputation checks or recent IOCs rather than fetching feeds. However, it doesn't explicitly differentiate from 'get_threat_feeds', which might be similar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'get_threat_feeds' (which might list available feeds) or 'get_recent_iocs' (which might fetch recent IOCs without specifying a feed), leaving the agent to infer usage context. There's no explicit when/when-not or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cisa_kevA
Get CISA Known Exploited Vulnerabilities.
Args: days: Get vulnerabilities added in last N days (default: 30) vendor: Filter by vendor name (optional)
Returns: JSON with recent KEVs
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| vendor | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'gets' data and returns JSON, but fails to describe critical behaviors such as whether this is a read-only operation (implied but not stated), any rate limits, authentication requirements, or what happens with invalid inputs (e.g., negative days). For a tool with no annotation coverage, this leaves significant gaps in understanding its operational traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by clear sections for arguments and returns. Every sentence earns its place: the first states what the tool does, the next two explain parameters succinctly, and the last specifies the return format. There is zero waste, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no nested objects) and the presence of an output schema (which handles return values), the description is largely complete. It covers the purpose, parameters, and return format adequately. However, it lacks details on behavioral aspects like error handling or data freshness, which would be helpful since no annotations are provided to fill those gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate by explaining parameters, which it does effectively. It clarifies that 'days' retrieves vulnerabilities added in the last N days with a default of 30, and 'vendor' is an optional filter by vendor name. This adds meaningful context beyond the bare schema, covering both parameters' purposes and defaults, though it could benefit from examples or format details (e.g., vendor name casing).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('CISA Known Exploited Vulnerabilities'), making it immediately understandable. It distinguishes itself from sibling tools like 'get_recent_iocs' or 'get_threat_feeds' by focusing specifically on CISA's KEV database, which is a distinct dataset of known exploited vulnerabilities rather than general indicators or feeds.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the mention of filtering by days and vendor, suggesting it's for retrieving recent or vendor-specific vulnerabilities. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_recent_iocs' (which might overlap in recency) or 'check_network_against_threats' (which could involve KEV data), leaving the agent to infer context without clear exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_dashboard_summaryB
Get a summary of all threat intelligence for dashboard display.
Returns: JSON with aggregated threat data for visualization
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns aggregated threat data for visualization, but doesn't cover critical aspects such as whether it's a read-only operation, potential rate limits, authentication requirements, data freshness, or any side effects. For a tool with no annotation coverage, this leaves key behavioral traits unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: two sentences that directly state the purpose and return format without any fluff. The first sentence explains what the tool does, and the second clarifies the output, making it front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description doesn't need to detail inputs or return values. However, it lacks context on usage scenarios, behavioral traits, and differentiation from siblings, which are important for a tool in a server with multiple threat intelligence tools. The description is minimally adequate but has clear gaps in guidance and transparency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter-specific information, which is appropriate here. A baseline of 4 is applied since there are no parameters to document, and the description doesn't introduce any confusion or redundancy regarding inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a summary of all threat intelligence for dashboard display.' It specifies the verb ('Get') and resource ('summary of all threat intelligence'), and the context ('for dashboard display') provides additional clarity. However, it doesn't explicitly differentiate from sibling tools like 'get_threat_stats' or 'get_recent_iocs', which might also provide aggregated data, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'dashboard display' as a context, but doesn't specify scenarios, prerequisites, or exclusions. With sibling tools like 'get_threat_stats' and 'get_recent_iocs' that might overlap, the lack of comparative guidance is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_recent_iocsB
Get recent IOCs (Indicators of Compromise) from ThreatFox.
Args: ioc_type: Filter by type (ip:port, domain, url, md5, sha256) limit: Maximum IOCs to return (default: 100, max: 500)
Returns: JSON with recent IOCs
| Name | Required | Description | Default |
|---|---|---|---|
| ioc_type | No | ||
| limit | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'JSON with recent IOCs' but doesn't specify details like pagination, rate limits, authentication requirements, or error handling. For a tool with potential security implications (IOCs), this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by clear sections for 'Args' and 'Returns'. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is somewhat complete but has gaps. It covers parameters well and notes the return format, but lacks behavioral context (e.g., auth, rate limits) and doesn't leverage the output schema to detail the JSON structure, leaving room for improvement in overall completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It effectively explains both parameters: 'ioc_type' with its filter options (e.g., 'ip:port', 'domain') and 'limit' with its default and max values. This adds crucial meaning beyond the bare schema, though it could benefit from more detail on format constraints (e.g., URL encoding).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('recent IOCs from ThreatFox'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'fetch_threat_feed' or 'get_threat_feeds', which might also retrieve threat data, leaving some ambiguity about when to choose this specific tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'fetch_threat_feed' or 'get_threat_feeds'. The description lacks context about prerequisites, such as whether authentication is needed, or any explicit exclusions, leaving the agent to infer usage based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_threat_feedsB
Get list of all available threat intelligence feeds.
Returns: JSON with available feeds and their descriptions
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format ('JSON with available feeds and their descriptions'), which adds some context, but lacks details on permissions, rate limits, caching behavior, or whether this is a read-only operation. For a tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded, stating the purpose in the first sentence and the return format in the second. Both sentences add value, with no wasted words. However, it could be slightly more structured by explicitly separating usage context from output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description doesn't need to explain return values in detail, which it acknowledges. However, with no annotations and multiple sibling tools, the description lacks context on behavioral traits and usage differentiation. It's minimally adequate but has clear gaps in guiding the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate given the schema's completeness. A baseline of 4 is applied since there are no parameters to document.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'list of all available threat intelligence feeds', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'fetch_threat_feed' or 'get_recent_iocs', which might have overlapping functionality. The description is specific about what it returns but lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'fetch_threat_feed' and 'get_recent_iocs', there's no indication of whether this tool is for metadata listing, bulk retrieval, or other contexts. No prerequisites or exclusions are mentioned, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_threat_statsB
Get statistics about loaded threat data and cache status.
Returns: JSON with threat intelligence statistics
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'cache status' which hints at behavioral aspects related to caching, but doesn't disclose details like whether this is a read-only operation, performance characteristics, or error handling. The description adds some context but lacks comprehensive behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief with two sentences, but the second sentence 'Returns: JSON with threat intelligence statistics' is redundant given the output schema exists. This wastes space without adding value, reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, output schema provided), the description is mostly complete. It covers the purpose and hints at cache-related behavior, but could benefit from more usage guidance relative to siblings. The output schema handles return values, so no need to explain them in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here, and the baseline for 0 parameters is 4, as it avoids unnecessary repetition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with the verb 'Get' and resource 'statistics about loaded threat data and cache status', making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_dashboard_summary' or 'get_threat_feeds', which might provide overlapping or related statistics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_dashboard_summary' and 'get_threat_feeds' that might offer similar or complementary data, there's no indication of context, prerequisites, or exclusions to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
11 tool updates
- First observed
check_bulk_ips - First observed
check_hash_reputation - First observed
check_ip_reputation - First observed
check_network_against_threats - First observed
clear_threat_cache - First observed
fetch_threat_feed - First observed
get_cisa_kev - First observed
get_dashboard_summary - First observed
get_recent_iocs - First observed
get_threat_feeds - First observed
get_threat_stats
TDQS
Each tool has a clearly distinct purpose with no ambiguity. The tools cover specific threat intelligence operations like checking IPs/hashes, fetching feeds, getting CISA KEVs, retrieving IOCs, and managing cache/stats, all with well-defined boundaries. There is no overlap that would cause misselection.
Tool names follow a consistent verb_noun pattern throughout, such as check_bulk_ips, fetch_threat_feed, get_cisa_kev, and clear_threat_cache. All tools use snake_case with clear, descriptive names that align with their functions, making them predictable and readable.
With 11 tools, the count is well-scoped for a threat intelligence server, covering essential operations like reputation checks, feed management, data retrieval, and cache control. Each tool earns its place without feeling excessive or insufficient for the domain.
The tool surface provides complete coverage for threat intelligence workflows, including checking various IOCs (IPs, hashes, networks), fetching and managing feeds, retrieving vulnerabilities and recent IOCs, and supporting dashboards and statistics. There are no obvious gaps that would hinder agent operations.
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