Threat Intelligence MCP Server

World Intelligence MCP Server
30개 이상의 도메인에 걸친 실시간 글로벌 인텔리전스, 120개의 MCP 도구, 라이브 운영센터 대시보드, CLI, 그리고 축적된 인텔리전스에 대한 엔터프라이즈급 시맨틱 검색을 위한 Qdrant 벡터 스토어를 제공합니다. 모든 데이터는 무료 공개 API에서 제공되며 유료 구독이 필요 없습니다.
세계 인식이 필요한 AI 에이전트를 위해 설계되었습니다: 시장 상황, 지정학적 리스크, 군사 태세, 공급망 중단, 사이버 위협 등 — 모두 Model Context Protocol을 통해 조회할 수 있습니다. 벡터 스토어는 "대만 인근 군사 활동" 또는 "의료 분야를 겨냥한 사이버 위협" 과 같은 자연어 쿼리를 모든 과거 데이터에 대해 지원합니다.
제공 기능
도메인 | 도구 수 | 데이터 소스 |
금융 시장 | 7 | Yahoo Finance, CoinGecko, Alternative.me, Mempool |
외환 및 통화 | 3 | ECB/Frankfurter (8대 주요 페어, 시계열, 교차 환율) |
채권 및 수익률 | 2 | FRED, Yahoo Finance (수익률 곡선, 채권 ETF, 스프레드 분석) |
실적 발표 | 2 | Yahoo Finance (메가캡 캘린더, 서프라이즈 이력) |
SEC 공시 | 3 | SEC EDGAR (전문 검색, 기업 공시, 8-K 중요 사건) |
기업 정보 보강 | 1 | Yahoo Finance + GDELT + SEC + GitHub (복합 프로필) |
매크로 종합 | 1 | 가중 6-신호 시장 판정 (Fear&Greed, VIX, 섹터, DXY, BTC, 수익률) |
경제 지표 | 6 | AAA 유가, EIA 에너지, FRED 매크로, World Bank |
중앙은행 | 1 | 15개 중앙은행 정책 금리 |
BTC 기술적 분석 | 1 | SMA 50/200, 골든/데드 크로스, Mayer Multiple |
자연 재해 | 2 | USGS 지진, NASA FIRMS 산불 |
환경 | 2 | NASA EONET, GDACS 재난 경보 |
기후 | 1 | Open-Meteo 기온/강수 이상치 |
분쟁 및 안보 | 4 | ACLED 사건, UCDP, 소요 감지, 인도주의 데이터 |
군사 및 국방 | 6 | adsb.lol, OpenSky, hexdb.io, 급증 감지, 전구 태세, 항공기 배치 |
인프라 | 4 | Cloudflare Radar, 해저 케이블, 연쇄 분석, 클라우드 상태 |
해양 | 2 | NGA 항해 경고, 선박 스냅샷 |
항공 | 2 | FAA 공항 지연, 국내선 스냅샷 |
뉴스 및 미디어 | 3 | 119개 RSS 피드 (4계층), GDELT, 트렌딩 키워드 |
인텔리전스 분석 | 8 | 신호 수렴, 초점 지점, 불안정 지수, 리스크 점수, 확대 |
NLP 인텔리전스 | 4 | 개체 추출, 사건 분류, 뉴스 클러스터링, 키워드 급증 |
전략 종합 | 4 | 전략 태세, 세계 브리핑, 함대 보고서, 인구 노출 |
지리공간 | 11 | 군사 기지, 항구, 파이프라인, 원자력 시설, 케이블, 데이터센터, 우주기지, 광물, 거래소, 무역로, 클라우드 리전 |
AI 및 기술 | 4 | arXiv 논문, HuggingFace 모델, Hacker News, GitHub 트렌딩 |
사이버 위협 | 1 | URLhaus, Feodotracker, CISA KEV, SANS |
보건 | 1 | WHO DON, ProMED, CIDRAP 질병 발생 |
우주 기상 | 1 | NOAA SWPC (Kp 지수, 태양 플레어, 경보) |
사회 및 제재 | 3 | Reddit 속도, OFAC SDN 목록, 핵실험장 모니터링 |
국가 인텔리전스 | 3 | 국가 브리핑, 국가 주식, 금융 중심지 |
예측 시장 | 1 | Polymarket 이벤트 계약 |
선거 | 1 | 리스크 점수를 포함한 글로벌 선거 캘린더 |
이재민 | 1 | UNHCR 난민/국내실향민 데이터 |
해운 | 1 | 건화물 해운 스트레스 지수 |
정부 | 1 | USAspending.gov 연방 계약 |
교통 | 2 | 도로 교통 흐름, 실시간 사고 |
교차 도메인 알림 | 2 | 알림 다이제스트, 주간 트렌드 |
모니터링 | 2 | 웹캠, 서버 상태/헬스 |
벡터 검색 | 5 | Qdrant 시맨틱 검색, 유사도, 타임라인, 통계 |
교차 도메인 분석 | 3 | 상관관계, 도메인 요약, 트렌드 감지 |
보고서 | 1 | PDF/HTML 다중 도메인 인텔리전스 보고서 |
일일 다이제스트 | 1 | 인용 포함 마크다운 아침 브리핑: 주요 사건, 헤드라인, 트렌드, 타임라인 |
AOI 지오펜스 | 5 | 사용자 정의 관심 영역: 정의/목록/삭제, 인용 포함 다중 도메인 브리핑, 사용자 영역에 대한 핫스팟 확대 점수 |
상황 브리핑 | 1 | MCP를 통한 인용 포함 상황 인식 브리핑: 로컬 Ollama로 종합된 제한된 서버 측 개요, 기계적 인용 폴백 포함 |
총 120개 도구 — 30개 이상의 인텔리전스 도메인.
Related MCP server: MCP Threat Intel Server
빠른 시작
설치
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, coverageMCP 서버로 실행
world-intel-mcp # stdio mode for Claude Code, Cursor, etc.Claude Code 설정
~/.claude.json에 추가:
{
"mcpServers": {
"world-intel-mcp": {
"command": "world-intel-mcp"
}
}
}대시보드
intel-dashboard # http://localhost:8501
intel-dashboard --port 9000 # custom portPDF/HTML 보고서
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 sections맵 중심 운영 센터: 토글 가능한 레이어(지진, 군사, 분쟁, 화재, 수렴, 원자력, 인프라)가 있는 Leaflet 맵, 47개 라이브 SSE 피드, HUD 바, 글라스모픽 패널, 소스별 서킷 브레이커 상태.
CLI
intel markets # stock indices
intel earthquakes --min-mag 5.0
intel status # cache + circuit breaker health아키텍처
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: 중앙 집중식 비동기 HTTP 클라이언트(httpx). 재시도, 소스별 속도 제한, 오래된 데이터 폴백. 새로 가져온 데이터는 자동으로 벡터 스토어에 저장.
CircuitBreaker: 소스별 추적. 연속 3회 실패 시 5분간 차단. 각 RSS 피드는 자체 브레이커를 가짐.
Cache: SQLite WAL 모드 TTL 캐시.
get()은 라이브 데이터를 반환하고,get_stale()은 폴백용 만료 데이터를 반환.VectorStore: Qdrant + FastEmbed (BAAI/bge-small-en-v1.5, 384차원). 비차단 저장을 위한 비동기 백그라운드 워커 큐. 축적된 모든 인텔리전스에 대한 시맨틱 검색 지원.
Collector: 46개 소스를 병렬로 가져와 벡터 스토어를 채우는 독립 데몬. 1회 실행 또는 데몬으로 실행(기본 5분 간격).
Sources (
sources/*.py): 30개 이상의 모듈, 각각async def fetch_*(fetcher, **kwargs) -> dict를 내보냄.Analysis (
analysis/*.py): 교차 도메인 종합 — 신호 집계, 불안정 지수, NLP, 기업 정보 보강, 매크로 종합.Config (
config/*.py): 선별된 데이터셋 — 22개 핫스팟, 70개 이상의 기지, 40개 항구, 24개 파이프라인, 24개 원자력 시설, 34개 케이블, 48개 데이터센터, 27개 우주기지, 82개 거래소.
MCP 도구 참조
금융 시장 (7)
도구 | 설명 |
| 주가 지수 시세 (S&P 500, Dow, Nasdaq, FTSE, Nikkei) |
| CoinGecko의 주요 암호화폐 가격 및 시가총액 |
| 스테이블코인 페그 상태 (USDT, USDC, DAI, FDUSD) |
| 비트코인 현물 ETF 가격 및 거래량 |
| 미국 주식 섹터 성과 (11개 SPDR ETF) |
| 7개 매크로 지표 (Fear & Greed, VIX, DXY, 금, 10Y, BTC) |
| 상품 선물 (금, 은, 원유, 천연가스, 곡물) |
외환 및 통화 (3)
도구 | 설명 |
| ECB 최신 환율. 기준/대상 통화로 필터링 |
| 추세 분석이 포함된 과거 환율 (일수 설정 가능) |
| 주요 8개 통화쌍 + 교차 환율 + DXY 대리 지표 |
채권 및 수익률 (2)
도구 | 설명 |
| 미국 국채 수익률 곡선 (2Y-30Y), 2s10s/3m10y 스프레드, 역전 플래그 |
| 채권 ETF: AGG, TLT, HYG, LQD, TIP 가격/변동 |
실적 (2)
도구 | 설명 |
| 대형주 20개 종목의 예정 실적 발표 및 EPS 추정치 |
| 과거 실적 서프라이즈 (실적 vs 추정치, 추세) |
SEC 서류 (3)
도구 | 설명 |
| 전체 EDGAR 서류 전체 텍스트 검색 |
| 티커별 기업 서류 (10-K, 10-Q, 8-K) 및 CIK 확인 |
| 최신 8-K 중요 사건 (M&A, 임원 변동, 실적) |
기업 정보 보강 (1)
도구 | 설명 |
| 통합 프로필: 주가 + 재무 + 뉴스 + SEC + GitHub |
거시 종합 (1)
도구 | 설명 |
| 가중 시장 점수 (0-100) 및 판정: RISK_ON ~ STRONG_CAUTION |
경제 (6)
도구 | 설명 |
| AAA 제공 미국 일일 소매 휘발유, 경유, E85 가격 |
| EIA 제공 미국 주거용 천연가스 가격 |
| EIA 제공 부문/주별 미국 전력 소매 요금 |
| EIA 제공 Brent/WTI 원유 및 천연가스 |
| FRED 경제 데이터 (GDP, CPI, 실업률, 금리) |
| 국가별 세계은행 개발 지표 |
중앙은행 (1)
도구 | 설명 |
| 주요 중앙은행 15곳의 정책 금리 |
BTC 기술적 지표 (1)
도구 | 설명 |
| 비트코인 SMA 50/200, 골든/데스 크로스, 마이어 멀티플 |
자연재해 (2)
도구 | 설명 |
| USGS 지진 (규모/시간/한도 설정 가능) |
| NASA FIRMS 위성 화재 핫스팟 (전 세계 9개 지역) |
환경 (2)
도구 | 설명 |
| NASA EONET 자연 재해 이벤트 |
| GDACS 재난 경보 및 심각도 점수 |
분쟁 및 안보 (4)
도구 | 설명 |
| ACLED 무력 충돌 이벤트 |
| 웁살라 충돌 데이터 프로그램 이벤트 |
| Haversine 중복 제거 기반 사회 불안 |
| HDX 인도주의 위기 데이터셋 |
군사 및 국방 (6)
도구 | 설명 |
| adsb.lol 기반 군용 항공기 (OpenSky 대체) |
| 5개 전구 활동 (EU, 인도-태평양, 중동, 북극, 한국) |
| ICAO24 헥스 기반 항공기 조회 (hexdb.io) |
| 일괄 항공기 조회 (다중 헥스 코드) |
| 외국 항공기 집중 이상 탐지 |
| USNI 뉴스 해군 함대 추적기 |
인프라 (4)
도구 | 설명 |
| Cloudflare Radar 인터넷 장애 |
| 해저 케이블 회랑 상태 |
| 인프라 연쇄 시뮬레이션 |
| 클라우드 플랫폼 상태 (AWS, Azure, GCP, Cloudflare, GitHub) |
해양 (2)
도구 | 설명 |
| NGA 해상 항행 경보 |
| 9개 전략 수로의 해군 활동 |
지리공간 데이터셋 (10)
도구 | 설명 |
| 9개 운영 주체의 군사 기지 70곳 |
| 6개 유형의 전략 항구 40곳 |
| 석유/가스/수소 파이프라인 24개 |
| 원자력 발전/농축/연구 시설 24곳 |
| 해저 통신 케이블 34개 |
| 전 세계 AI/HPC 데이터센터 48곳 |
| 전 세계 우주 발사장 27곳 |
| 전략 광물 매장지 27곳 |
| 전 세계 증권거래소 82곳 |
| 주요 무역 항로 및 병목 지점 |
뉴스 및 미디어 (3)
도구 | 설명 |
| 4단계 소스 순위가 있는 전 세계 RSS 피드 119개 |
| 급증 감지가 포함된 인기 키워드 |
| GDELT 2.0 글로벌 뉴스 검색 |
정보 분석 (8)
도구 | 설명 |
| 다중 도메인 신호의 지리적 수렴 |
| 다중 신호 초점 지점 탐지 |
| 국가별 신호 집계 |
| 기준 대비 활동 편차 |
| 국가 불안정 지수 v2 (0-100) |
| ACLED 기반 충돌 위험 점수 |
| 인텔 핫스팟 22곳의 확대 점수 |
| 종합 국가 정보 도시에 |
NLP 정보 (4)
도구 | 설명 |
| 개체명 추출 (국가, 지도자, 조직, CVE, APT) |
| 14개 위협 범주로 이벤트 분류 |
| Jaccard 유사도 기반 주제 클러스터링 |
| Welford 알고리즘 기반 키워드 급증 탐지 |
전략 종합 (4)
도구 | 설명 |
| 9개 가중 도메인의 종합 글로벌 위험 |
| 구조화된 일일 정보 요약 |
| 대비 점수가 포함된 해군 함대 활동 보고서 |
| 활성 이벤트 인근 위험 인구 (105개 도시 데이터셋) |
기후 (1)
도구 | 설명 |
| Open-Meteo 기온/강수 이상 |
예측 시장 (1)
도구 | 설명 |
| Polymarket 예측 계약 |
선거 (1)
도구 | 설명 |
| 위험 점수가 포함된 글로벌 선거 달력 |
이재민 (1)
도구 | 설명 |
| UNHCR 난민/국내 실향민 통계 |
항공 (2)
도구 | 설명 |
| FAA 공항 지연 상태 |
| OpenSky 기반 글로벌 항공 교통 스냅샷 |
사이버 위협 (1)
도구 | 설명 |
| 통합 사이버 정보 (URLhaus, CISA KEV, SANS) |
우주 기상 (1)
도구 | 설명 |
| 태양 활동 (Kp 지수, X선 플럭스, SWPC 경보) |
AI 및 기술 (4)
도구 | 설명 |
| arXiv AI 논문, HuggingFace 모델 |
| Hacker News 인기 기사 |
| GitHub 인기 저장소 |
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자신의 지역 모니터링(지오펜스/AOI)
정적 기반시설 결과(기지, 항구, 핵시설, 해저 케이블, 데이터센터, 우주기지)는 이 저장소의 선별된 전략 데이터셋을 기반으로 하며, 이는 전 세계를 대상으로 하되 의도적으로 희소하게 구성되어 있어 완전한 지역 등록부가 아닙니다. AOI 브리핑이 조용하다는 것은 선별된 데이터셋 중 해당 범위 내에 아무것도 없다는 뜻이지, 해당 지역에 기반시설이 없다는 뜻이 아닙니다.
120개 도구 중 28개가 일부 지리적 매개변수를 받지만, AOI
계열 이전에는 intel_signal_convergence만이 실제 지점+반경을
수용했고, intel_military_flights는 bbox를 사용했으며, 핫스팟
급증 점수 산정은 22개의 하드코딩된 INTEL_HOTSPOTS로 제한되어
있었습니다. intel_aoi_* 도구를 사용하면 자신의 지역(도시, 국경
지역, 시설)에 이름을 붙이고 동일한 인용 포함 다중 도메인 처리를
받을 수 있습니다.
AOI를 한 번 정의한 후, 온디맨드로 브리핑과 점수 산정을 수행하세요:
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_brief는 지리 인식이 가능한 모든 도메인을 피츠버그
주변 50km 반경으로 필터링합니다: 지진, 군용 비행(반경에서 파생된
bbox), 산불(NASA FIRMS에는 지점+반경 쿼리가 없으므로 지역 매핑),
ACLED 분쟁 이벤트, 인근 항공 교통 샘플, 인근 정적 기반시설(군사
기지, 항구, 파이프라인, 핵시설, 해저 케이블, 데이터센터, 우주기지)
및 거리(km), 그리고 "Pittsburgh"에 대한 뉴스 헤드라인 언급.
응답의 모든 항목에는 번호가 매겨진 sources 목록으로 연결되는
[n] 인용이 포함되며, data_gaps는 AOI 범위로 지정할 수 없었던
도메인(예: AOI가 NASA FIRMS의 커버리지 지역 밖에 있는 경우 산불,
또는 ACLED 자격 증명이 구성되지 않은 경우 분쟁 이벤트)을 조용히
생략하는 대신 명시적으로 표시합니다.
intel_aoi_escalation은 22개 내장 핫스팟에 대해
intel_hotspot_escalation을 구동하는 것과 동일한 기준/군사/분쟁/
사회 불안 점수 산정 엔진을 실행하되, 고정된 2도 창 대신 AOI의
자체 반경으로 범위를 제한합니다.
AOI는 서버가 이미 사용하는 동일한 SQLite 캐시 데이터베이스
(기본적으로 ~/.cache/world-intel-mcp/cache.db, 또는
$WORLD_INTEL_CACHE_DB) 내의 전용 테이블에 유지되므로, 예약된
에이전트가 재시작을 거쳐도 intel_aoi_list / intel_aoi_delete로
관리하면서 모든 명명된 지역을 계속 모니터링할 수 있습니다.
벡터 저장소
선택적 Qdrant 벡터 저장소는 시간이 지남에 따라 인텔리전스를 축적하여 의미론적 검색을 지원합니다. Fetcher를 통해 가져온 모든 데이터는 자동으로 임베딩되어 저장됩니다.
설정
# 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 cyclemacOS launchd 서비스로 실행
scripts/collector-daemon.sh는 수집기를 launchd 에이전트로
관리하여 재부팅 후에도 유지되도록 합니다. 이 스크립트는
com.agentic.intel-collector.plist.template에 이 체크아웃의 자체
경로(스크립트 자체 위치에서 확인되므로 모든 클론에서 작동)를
채워 넣고 결과를 ~/Library/LaunchAgents/에 설치합니다.
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 it의미론적 검색 예시
데이터가 축적되면 AI 에이전트가 모든 도메인을 대상으로 쿼리할 수 있습니다:
"대만 해협 인근 군사 활동" — 군용 비행, 해군 경고, 전구 전력 자세 데이터 검색
"의료 분야를 겨냥한 사이버 위협" — 의료 관련 URLhaus, CISA KEV 항목 검색
"경기 침체를 시사하는 경제 지표" — 수익률 곡선 역전, 거시 신호, FRED 데이터 검색
벡터 저장소는 임베딩에 FastEmbed(ONNX 기반, BAAI/bge-small-en-v1.5)를 사용합니다 — GPU 불필요, 콜드 스타트 약 3초.
환경 변수
변수 | 필수 | 설명 |
| 아니요 | ACLED 분쟁 이벤트 |
| 아니요 | 위성 산불 데이터 |
| 아니요 | 에너지 가격 데이터 |
| 아니요 | 인터넷 장애 데이터 |
| 아니요 | 거시 경제 데이터(수익률 곡선에도 사용) |
| 아니요 | 군용 비행 대체 소스 |
| 아니요 | 군용 비행 대체 소스 |
| 아니요 | AI 생성 브리핑용 Ollama 서버(기본값: |
| 아니요 | AI 생성 브리핑용 Ollama 모델(기본값: |
| 아니요 | 로깅 수준(기본값: INFO) |
그 외 모든 것은 무료, 인증 없는 공개 API를 사용합니다.
개발
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 module새 소스 추가하기
sources/your_source.py파일을 생성하고async def fetch_your_data(fetcher: Fetcher, **kwargs) -> dict를 작성합니다.fetcher.get_json(url, source="your-source", cache_key=..., cache_ttl=300)을 사용합니다 — 자동 캐싱, 재시도, 서킷 브레이킹, 속도 제한이 적용됩니다.server.py에서TOOLS에Tool(...)을 추가하고,_dispatch()에case를 추가합니다(인라인 임포트 사용).respx를 사용하여 HTTP를 모킹하는 테스트를 추가합니다(패턴은tests/test_forex.py참조).선택적으로
dashboard/app.py(SSE)와cli.py(Click)에 추가합니다.
라이선스
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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