"GIS data acquisition for disaster detection and risk prediction" matching MCP tools:

• get_disaster_risk

  DC Hub — Data Center & Power Intelligence

  Use when a user wants the natural-hazard / disaster risk for a lat/lon — flood, wildfire, hurricane, earthquake, heat, drought, tornado, etc. Grounded in the FEMA National Risk Index (NRI), the authoritative US county-level hazard dataset (live query, never estimated; points outside US NRI coverage return coverage=unavailable). Example: get_disaster_risk lat=33.45 lon=-112.07. Returns {disaster_risk:{composite_score (0-100, higher=worse), rating (Very Low..Very High), national_percentile}, hazards:{Wildfire, Hurricane, Earthquake, Heat Wave, ...: rating}, top_hazards:[{hazard, rating}], coverage (validated|unavailable), source, caveats}. County-level resolution. For chronic water stress use get_water_risk; for one blended site verdict use get_composite_site_score.

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• usaspending_disaster_spending

  usaspending-mcp-server

  Fetch disaster and emergency supplemental spending (COVID-19, hurricanes, infrastructure law, etc.) broken down by agency, CFDA assistance program, recipient, or geography. Use the dimension parameter to select the breakdown axis: overview (top-level totals), agency, cfda, recipient, or geography. Filter by DEF codes (Disaster/Emergency Funding codes) to isolate a specific emergency appropriation. DEF codes appear in usaspending_get_award account_obligations_by_defc and usaspending_get_agency def_codes fields.

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• list_domains

  cassandra-predict

  List Cassandra prediction verticals available via predict(domain, entity), with what each predicts and its data sources.

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• get_disaster_risk

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants the natural-hazard / disaster risk for a lat/lon — flood, wildfire, hurricane, earthquake, heat, drought, tornado, etc. Grounded in the FEMA National Risk Index (NRI), the authoritative US county-level hazard dataset (live query, never estimated; points outside US NRI coverage return coverage=unavailable). Example: get_disaster_risk lat=33.45 lon=-112.07. Returns {disaster_risk:{composite_score (0-100, higher=worse), rating (Very Low..Very High), national_percentile}, hazards:{Wildfire, Hurricane, Earthquake, Heat Wave, ...: rating}, top_hazards:[{hazard, rating}], coverage (validated|unavailable), source, caveats}. County-level resolution. For chronic water stress use get_water_risk; for one blended site verdict use get_composite_site_score.

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• disaster_recovery_profile

  livedatalink

  One-call disaster-history and recovery read for a US area (county or place), keyed by NAME + state - distinct from location_risk_report, which scores a single site by address/lat-lon. Joins FEMA's OpenFEMA disaster declarations (the area's federally-declared disaster history: incident types, frequency, most-recent event, and the federal-assistance signal - which programs, Individual Assistance / Individuals & Households / Public Assistance / Hazard Mitigation, were authorized) with optional US Census ACS county population for exposure context (keyed off the FIPS codes the FEMA records carry; needs a Census key and degrades gracefully) and an optional best-effort parcel record for property context when an address is given (Maryland statewide / Texas-Harris County only). Returns a readable profile with a headline banding the area's disaster exposure LOW / MODERATE / HIGH from the declaration record. The FEMA leg is keyless and is the core signal; a source that fails is noted, not fatal. INFORMATIONAL public-record synthesis, NOT an insurance rating, a property flood-risk score, or a professional risk assessment.

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• assess_weather_risk

  ParaSure

  [$0.10 USDC/call · Solana USDC · x402] Entry point for every agent flow. Given a business location and type, returns a weather risk score (0-1), the top perils ranked by severity, historical frequency data, and an overall risk level (low/moderate/high/severe). Powered by 5 years of Open-Meteo historical data — returns real data, not sandbox. Always call this first before requesting a quote.

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• score_pr_for_deploy_risk

  Koalr

  Score a specific pull request for deployment risk using Koalr's 36-signal model. Returns a 0–100 risk score with a detailed factor breakdown covering change entropy, DDL migration detection, author file expertise, PR size, CODEOWNERS violations, blast radius, coverage delta, and more. Use this to answer "How risky is this PR?" or "Should we merge this before the release?". Read-only — scoring does not modify the PR.

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• get_corridor_risk

  SupplyMaven MCP Server

  Monitor risk levels across 10 major ocean freight trade corridors (China-US West Coast, China-US East Coast, China-Mexico, Taiwan-US, India-US, China-Europe, Europe-US, Middle East-Europe, Brazil-US). Each corridor chains origin ports, chokepoints, and destination ports into a single lane scored by its weakest link (highest risk waypoint). Scores combine real-time port congestion data with active natural disaster proximity. Used by logistics planners for route risk comparison, procurement teams for supply chain exposure assessment, and freight forwarders for disruption early warning.

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• weather_climate_intel

  mcp-knowledge

  Physical climate intelligence for insurance underwriting, agritech, logistics, energy trading and ESG/climate risk disclosure. Three modes: (1) forecast — 14-day daily weather forecast with temperature, precipitation, wind and humidity; (2) historical — daily records and monthly aggregates for any date range since 1940, with anomaly detection (P90/P95 heat events, extreme precipitation days); (3) climate_risk — long-term physical risk scoring combining CMIP6 ensemble projections (2020-2050), altitude, FEMA flood zones (US) and historical baselines. Risk dimensions: flood, heat (days >35°C/year), drought (SPI), wildfire, sea-level. Overall score 0-100 (100 = severe). Location: city string or lat/lon coordinates. Sources: Open-Meteo (keyless, global, 1940→2050), Open-Elevation, FEMA NFHL (US), NOAA CDO (optional NOAA_API_KEY env var for US+global station data). SLA: ≤25s p95. Cache: 1h forecast / 24h historical / 7d climate_risk.

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• weather_climate_intel

  gapup-mcp

  Physical climate intelligence for insurance underwriting, agritech, logistics, energy trading and ESG/climate risk disclosure. Three modes: (1) forecast — 14-day daily weather forecast with temperature, precipitation, wind and humidity; (2) historical — daily records and monthly aggregates for any date range since 1940, with anomaly detection (P90/P95 heat events, extreme precipitation days); (3) climate_risk — long-term physical risk scoring combining CMIP6 ensemble projections (2020-2050), altitude, FEMA flood zones (US) and historical baselines. Risk dimensions: flood, heat (days >35°C/year), drought (SPI), wildfire, sea-level. Overall score 0-100 (100 = severe). Location: city string or lat/lon coordinates. Sources: Open-Meteo (keyless, global, 1940→2050), Open-Elevation, FEMA NFHL (US), NOAA CDO (optional NOAA_API_KEY env var for US+global station data). SLA: ≤25s p95. Cache: 1h forecast / 24h historical / 7d climate_risk.

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• list_signals

  ClearMarket

  Browse the CM Signal daily wire — short, structured prediction-market bulletins (the price is the lede, news is context). Filter by event_id (every wire touching a specific event), category, venue, or detection type (news_cycle, cross_venue_divergence, benchmark_drift, volume_spike). Returns compact records, newest first; call get_signal for the full bulletin. Use to find ClearMarket's editorial read on what is moving.

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• get_station_hazard

  Gachi Data API — Japan Station & Accessibility Data

  Official disaster-risk categories at a Japanese train station, relayed live from the MLIT 不動産情報ライブラリ (Real Estate Information Library): flood inundation-depth rank, landform / liquefaction classification, and storm-surge inundation-area presence (landslide & tsunami are license-restricted and return available:false with a link to the official maps). Returns the official values/categories as-is — no composite score, no judgment. Accepts a station name in Japanese (新宿, 武蔵小杉) or romaji (Shinjuku, Musashi-Kosugi). For research/analytics; NOT a substitute for official government hazard maps or evacuation decisions.

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• get_career_trajectory

  Provenio — Art Intelligence

  Cost: ~2s. Artist career trajectory analysis: milestones, career stage, pattern detection (A–E), and comparable historical artists who followed the same path. Career stages: PRE_MARKET → REGIONAL → CRITICAL_PHASE → MARKET_ENTRY → ESTABLISHED. Patterns: A=institutional escalation, B=curator championing, C=movement anchor, D=late market discovery, E=diaspora rediscovery. Use for: emerging artist discovery, gallery acquisition decisions, curator narrative building. Do NOT use for purely biographical queries — use get_artist instead.

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• get_cfpb_complaint_intelligence

  Stratalize Finance

  Use when assessing consumer finance risk, benchmarking complaint volume against peers, or conducting pre-acquisition due diligence on a financial institution. Returns CFPB complaint rollups by company and product — volume, issue themes, and response rate trends. Example: Regional Bank X — 847 CFPB complaints in 2023, 34% on mortgage servicing, complaint volume 2.3x peer median — elevated consumer protection risk signal. Source: CFPB Consumer Complaint Database synced data.

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• get_research

  zipexplore-mcp

  Returns ZipExplore's published research articles — original data findings on topics including disaster risk vs. housing costs (the Hazard Premium), food insecurity in fully-employed communities (SNAP economy), economically declining towns vs. retirement communities, and income variation among America's oldest ZIP codes. Each entry includes the key finding, specific quantitative results, caveats, and a URL to the full article. Call this when a user asks about these topics or when published findings would add context to a live data lookup.

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• fema_search_disasters

  fema-mcp-server

  Search federal disaster declarations by state, incident type, declaration type, date range, and county. Returns deduplicated declaration-level summaries — each disaster number appears once with a designatedAreaCount showing how many counties/municipalities were designated. The disaster number is the chain key for fema_get_disaster, fema_get_public_assistance, and fema_get_housing_assistance. Use declaration_type to filter: DR (major disaster, most common), EM (emergency), FM (fire management). Date filters apply to the declaration date. Use fema_get_disaster to retrieve all designated-area rows for a specific declaration.

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• fema_get_public_assistance

  fema-mcp-server

  Retrieve Public Assistance (PA) funded project records for a disaster or state — shows where federal recovery money was obligated. Returns applicant, damage category, project size and status, federal share obligated, and total obligated amounts. Either disaster_number or state must be provided. Use disaster_number (from fema_search_disasters) to scope to a single declaration, or state to browse all PA projects for a state. PA projects are created only when the PA program is declared (pa_declared: true on the disaster).

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• axiorank_author_detector

  AxioRank — Agent Firewall

  Describe a risk in plain English and AxioRank's AI proposes a custom content detector (regex or keyword) and SAVES it DISABLED for your review. An LLM never arms detection unattended. Outbound content categories only (secret/pii/destructive/injection/egress). Requires the `policies:write` scope and an AI-assessments-enabled plan (Team+).

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• drug_price_lookup

  mcp

  Look up the live public acquisition cost (CMS NADAC — National Average Drug Acquisition Cost) for a prescription drug in the United States: the per-unit price retail pharmacies actually pay to buy it, by strength, with the effective date. This is the honest baseline for judging what an employer health plan is charged by its PBM. Accepts a brand or generic name (e.g. "Gleevec", "imatinib", "atorvastatin").

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• get_vedic_astrology_yoga

  Vedic Astrology and Kundli MCP Server by RoxyAPI

  Browse the 300+ entry Vedic planetary-yoga glossary. Returns id and name for every cataloged yoga (Raja, Dhana, Pancha Mahapurusha, Nabhasa, Chandra-Mangala, and more). This is a dictionary lookup, not chart-driven detection: it does not inspect a birth chart. Use GET /yoga/{id} for the full glossary entry, or POST /yoga/detect to run the 12 classical detection rules against a specific kundli. Ideal for yoga-browser UIs, search, and progressive data loading.

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