NOAA Full — Ultimate Weather & Climate Intelligence MCP Server for Pydantic AI 36 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect NOAA Full — Ultimate Weather & Climate Intelligence through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to NOAA Full — Ultimate Weather & Climate Intelligence "
"(36 tools)."
),
)
result = await agent.run(
"What tools are available in NOAA Full — Ultimate Weather & Climate Intelligence?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About NOAA Full — Ultimate Weather & Climate Intelligence MCP Server
The ultimate NOAA Mega-Server — 36 tools across 7 domains from 5 official APIs.
Pydantic AI validates every NOAA Full — Ultimate Weather & Climate Intelligence tool response against typed schemas, catching data inconsistencies at build time. Connect 36 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
36 Tools
- 🌤️ Forecast (5) — Daily, hourly, grid, AFD, metadata
- ⚠️ Alerts (4) — By state, zone, point, types
- 📡 Observations (5) — Stations, current, history, radar
- ✈️ Aviation (5) — METAR, TAF, PIREP, SIGMET, station
- 🌊 Marine (6) — Tides, predictions, currents, water temp, met, sea level
- ☀️ Space (6) — Kp, forecast, solar wind, aurora, flux, Dst
- 📊 Climate (5) — Daily, monthly, yearly, normals, station search
No API Key Required
The NOAA Full — Ultimate Weather & Climate Intelligence MCP Server exposes 36 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect NOAA Full — Ultimate Weather & Climate Intelligence to Pydantic AI via MCP
Follow these steps to integrate the NOAA Full — Ultimate Weather & Climate Intelligence MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 36 tools from NOAA Full — Ultimate Weather & Climate Intelligence with type-safe schemas
Why Use Pydantic AI with the NOAA Full — Ultimate Weather & Climate Intelligence MCP Server
Pydantic AI provides unique advantages when paired with NOAA Full — Ultimate Weather & Climate Intelligence through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your NOAA Full — Ultimate Weather & Climate Intelligence integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your NOAA Full — Ultimate Weather & Climate Intelligence connection logic from agent behavior for testable, maintainable code
NOAA Full — Ultimate Weather & Climate Intelligence + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the NOAA Full — Ultimate Weather & Climate Intelligence MCP Server delivers measurable value.
Type-safe data pipelines: query NOAA Full — Ultimate Weather & Climate Intelligence with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple NOAA Full — Ultimate Weather & Climate Intelligence tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query NOAA Full — Ultimate Weather & Climate Intelligence and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock NOAA Full — Ultimate Weather & Climate Intelligence responses and write comprehensive agent tests
NOAA Full — Ultimate Weather & Climate Intelligence MCP Tools for Pydantic AI (36)
These 36 tools become available when you connect NOAA Full — Ultimate Weather & Climate Intelligence to Pydantic AI via MCP:
get_active_alerts
Filter by state (2-letter code: TX, FL, CA), severity (Extreme, Severe, Moderate, Minor), urgency (Immediate, Expected, Future), or event type (Tornado Warning, Hurricane Warning, etc.). Get active weather alerts by US state or severity
get_alert_types
). Use this to discover valid event type values for filtering alerts. List all NWS weather alert types available
get_alerts_by_point
Internally resolves the location to find active alerts in that area. Get active weather alerts for a specific US latitude/longitude
get_alerts_by_zone
g., TXZ211, FLZ050). Zone IDs can be found via the get_point_metadata tool. Useful for focused monitoring of a specific area. Get active weather alerts for a specific NWS zone
get_aurora_forecast
Powered by real-time solar wind data. The gold standard for aurora forecasting worldwide. Get the aurora probability forecast map data (Ovation model)
get_aviation_station
Use ICAO codes (KJFK, EGLL, LFPG, SBGR). Get aviation weather station information by ICAO code
get_climate_normals
This is the statistical baseline that defines "normal" weather for any location. Get 30-year climate normals — the baseline for what is "normal" weather
get_currents
Available at select CO-OPS stations with current meters. Get observed ocean current speed and direction at a US coastal station
get_daily_data
This is the planet's largest archive of daily weather records. Filter by station, data types (TMAX, TMIN, PRCP, SNOW, SNWD), and date range. Stations are worldwide but densest coverage is in the US. Get daily weather data (GHCN-Daily): temperatures, precipitation, snow
get_dst_index
Measures the intensity of the ring current around Earth. Values below -50 nT indicate a moderate storm, below -100 nT a strong storm, below -250 nT a severe storm. Critical for satellite operators and power grid monitoring. Get the Dst index — real-time geomagnetic storm intensity
get_forecast
Provide latitude and longitude for any US location. Returns high/low temps, wind speed/direction, precipitation probability, and detailed narrative. Get 7-day weather forecast for a US location by latitude and longitude
get_forecast_discussion
Use the 3-letter WFO code (e.g., OKX=New York, LAX=Los Angeles, MFL=Miami). Lists recent product IDs — retrieve the latest for full text. Get the Area Forecast Discussion (AFD) from a NWS Weather Forecast Office
get_grid_data
Useful for programmatic analysis. US only. Get raw NWS grid weather data: temperature, precipitation, wind, humidity arrays
get_hourly_forecast
5 days. Includes temperature, wind, humidity, precipitation, and sky condition for each hour. US locations only. Get hour-by-hour weather forecast (156 hours) for a US location
get_k_index_forecast
Use this to plan for aurora viewing, satellite vulnerabilities, or HF radio propagation impacts. Get the 3-day Kp index forecast — predicted geomagnetic activity
get_latest_observation
Provide a 4-character station ID such as KJFK, KLAX, KORD, KDFW. Get current weather conditions from a specific NWS station
get_metar
Provide ICAO codes comma-separated (KJFK, EGLL, LFPG). Returns temperature, wind, visibility, clouds, pressure, weather phenomena. Optionally retrieve past hours of data. Get METAR (current airport weather) for any airport worldwide by ICAO code
get_meteorological
Complements water-level data for a complete coastal picture. Get coastal meteorological data: air temp, wind, pressure at a station
get_monthly_summary
Monthly aggregates of temperature averages, precipitation totals, and degree days. Less granular than daily but ideal for climate trend analysis. Get monthly climate summary (GSOM): average temp, total precipitation, heating degree days
get_observation_history
Useful for seeing temperature trends, wind changes, and weather evolution over recent hours. Get recent observation history for a NWS station
get_pirep
Filter by age (hours). Get PIREPs (Pilot Reports) for turbulence, icing, and weather conditions
get_planetary_k_index
Kp ranges 0-9. Values ≥5 indicate geomagnetic storms with visible aurora at lower latitudes. Updated every 3 hours. Essential for aurora hunters, satellite operators, and power grid managers. Get the NOAA Planetary K-index — geomagnetic activity and aurora probability
get_point_metadata
US locations only. Get NWS metadata for a US location: responsible WFO, grid coordinates, zones
get_radar_stations
List all NWS radar stations and their status
get_sea_level_trends
Shows long-term relative sea level trends calculated from decades of tide gauge data. Critical for climate research. Get long-term sea level rise trends for a US coastal station
get_sigmet
These define areas of significant weather hazards for aviation: convection, turbulence, icing, IFR conditions, mountain obscuration. Get SIGMETs and AIRMETs — significant aviation weather hazards
get_solar_flux
7 solar flux index. Higher values (>100 SFU) indicate increased solar activity, more sunspots, and higher probability of solar flares and CMEs. Normal quiet-sun values are 70-80 SFU. Get the 10.7cm solar radio flux — a proxy for solar activity level
get_solar_wind
The solar wind drives geomagnetic storms — when speed exceeds 500 km/s with southward Bz, aurora probability increases dramatically. Get real-time solar wind speed and magnetic field conditions
get_station_metadata
Useful for understanding where a station is and what data it provides. Get metadata about a specific NWS weather station
get_stations
Each station has a 4-character ID (e.g., KJFK, KLAX). US only. Use station IDs with get_latest_observation. Find nearby NWS weather observation stations by latitude/longitude
get_taf
Includes forecast groups with wind, visibility, clouds, and weather changes. ICAO codes only. Get TAF (airport weather forecast) for any airport worldwide by ICAO code
get_tide_predictions
Provides predicted high and low tide times and heights. Useful for fishing, boating, coastal activities. Default is next 48 hours. Get tide predictions (hi/lo) for a US coastal station
get_water_levels
Data in meters relative to station datum. Provide a CO-OPS station ID (e.g., 8518750 for The Battery, NYC; 9414290 for San Francisco). Get observed water levels (tides) at a US coastal station
get_water_temperature
Useful for marine biology, fishing, surfing, and coastal research. Get water temperature at a US coastal station
get_yearly_summary
Yearly temperature averages, precipitation totals, and extreme values. Perfect for long-term climate analysis spanning decades. Get annual climate summary (GSOY): yearly averages and extremes
search_stations
Returns station IDs, names, and locations for use with other climate tools. Search NCEI weather stations by location bounding box or keyword
Example Prompts for NOAA Full — Ultimate Weather & Climate Intelligence in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with NOAA Full — Ultimate Weather & Climate Intelligence immediately.
"Full weather briefing: NYC forecast, alerts, airport conditions, and tides"
"Is there any space weather activity and can I see the aurora?"
Troubleshooting NOAA Full — Ultimate Weather & Climate Intelligence MCP Server with Pydantic AI
Common issues when connecting NOAA Full — Ultimate Weather & Climate Intelligence to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiNOAA Full — Ultimate Weather & Climate Intelligence + Pydantic AI FAQ
Common questions about integrating NOAA Full — Ultimate Weather & Climate Intelligence MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect NOAA Full — Ultimate Weather & Climate Intelligence with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect NOAA Full — Ultimate Weather & Climate Intelligence to Pydantic AI
Get your token, paste the configuration, and start using 36 tools in under 2 minutes. No API key management needed.