Can my AI check NDVI for my field to assess crop health?

Yes! Use the `get_ndvi` tool with your field coordinates and a date. NDVI values range from -1 to 1, with 0.6-0.9 indicating healthy dense vegetation and 0.2-0.5 indicating stressed or sparse vegetation. For trend analysis over a growing season, use `get_historical_ndvi` with a date range. The data comes from satellite imagery processed by OpenWeather algorithms.

How do I calculate irrigation needs using evapotranspiration data?

Use the `get_evapotranspiration` tool with your field coordinates and date. ET values show how much water your crops are losing through transpiration and soil evaporation. Compare ET with rainfall (from `get_current_weather` or `get_weather_forecast`) to determine irrigation deficits. If ET exceeds precipitation, irrigation is needed to replace the difference. For precise scheduling, track daily ET over time and accumulate deficits.

Can I get frost warnings to protect my crops?

Yes! Use the `get_frost_risk` tool with your field coordinates. It returns frost risk levels (low, moderate, high, critical) based on temperature forecasts and local conditions. For proactive planning, combine with `get_weather_forecast` to monitor approaching cold fronts. High or critical frost risk indicates you should activate frost protection measures like irrigation, wind machines, or covers.

OpenWeather Agro MCP Server for Pydantic AI 12 tools — connect in under 2 minutes

Name: OpenWeather Agro
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect OpenWeather Agro through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 OpenWeather Agro "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in OpenWeather Agro?"
    )
    print(result.data)

asyncio.run(main())

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* 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 OpenWeather Agro MCP Server

Connect your OpenWeather Agro API to any AI agent and take full control of satellite-based vegetation monitoring, weather-driven agricultural insights, and precision farming data through natural conversation.

Pydantic AI validates every OpenWeather Agro tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through 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.

What you can do

NDVI Analysis — Monitor crop vegetation health with satellite-derived NDVI values
EVI Monitoring — Track enhanced vegetation index for high-biomass and dense canopy areas
Soil Temperature — Check soil thermal conditions for seed germination and root activity
Evapotranspiration — Calculate crop water use for precision irrigation scheduling
Current Weather — Get real-time weather conditions for daily farming decisions
Weather Forecast — Access 5-day forecasts for planting and harvest planning
Historical Weather — Retrieve past weather data for crop performance analysis
Growing Degree Days — Track heat accumulation for crop development staging
Satellite Imagery — Access satellite imagery for visual field assessment
Historical NDVI — Analyze vegetation health trends over growing seasons
Crop Health Index — Get comprehensive crop condition scores
Frost Risk — Assess frost danger for crop protection planning

The OpenWeather Agro MCP Server exposes 12 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 OpenWeather Agro to Pydantic AI via MCP

Follow these steps to integrate the OpenWeather Agro 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 12 tools from OpenWeather Agro with type-safe schemas

Why Use Pydantic AI with the OpenWeather Agro MCP Server

Pydantic AI provides unique advantages when paired with OpenWeather Agro 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 OpenWeather Agro integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your OpenWeather Agro connection logic from agent behavior for testable, maintainable code

OpenWeather Agro + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the OpenWeather Agro MCP Server delivers measurable value.

Type-safe data pipelines: query OpenWeather Agro with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple OpenWeather Agro tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query OpenWeather Agro and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock OpenWeather Agro responses and write comprehensive agent tests

OpenWeather Agro MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect OpenWeather Agro to Pydantic AI via MCP:

get_crop_health_index

CHI provides a single metric for overall crop health, making it easier to track field conditions over time and compare across fields. Essential for quick field health assessment, prioritizing scouting missions, and communicating crop status to stakeholders. AI agents should use this when users ask "what is the overall crop health score for my field", "get a quick health assessment", or need a simplified crop condition metric. Date format: YYYY-MM-DD. Get Crop Health Index (CHI) for comprehensive crop condition assessment

get_current_weather

Essential for daily farming decisions, spray application timing, harvest planning, and frost protection. AI agents should use this when users ask "what is the weather like at my farm right now", "should I spray pesticides today", or need current weather data for agricultural operations. Get current weather conditions for agricultural decision making

get_evapotranspiration

ET combines soil evaporation and plant transpiration, providing the most accurate measure of crop water use. Essential for precision irrigation scheduling, water resource management, and drought assessment. AI agents should reference this when users ask "what is the evapotranspiration rate for my field", "calculate irrigation needs", or need crop water use data for irrigation planning. Date format: YYYY-MM-DD. Get evapotranspiration rates for irrigation scheduling and water management

get_evi

EVI is more sensitive than NDVI in high-biomass regions and less affected by atmospheric conditions and soil background. Essential for monitoring dense canopies, tropical crops, and areas with high vegetation cover. AI agents should reference this when users ask "what is the EVI for my dense crop area", "monitor high-biomass vegetation", or need enhanced vegetation index for areas where NDVI saturates. Date format: YYYY-MM-DD. Get EVI (Enhanced Vegetation Index) for high-biomass crop monitoring

get_frost_risk

Returns risk levels (low, moderate, high, critical), predicted frost timing, and recommended protection measures. Essential for frost-sensitive crops (fruits, vegetables, vineyards), irrigation-based frost protection, and crop insurance documentation. AI agents should reference this when users ask "is there frost risk for my orchard tonight", "assess frost danger for my crops", or need frost warning data for crop protection planning. Get frost risk assessment for crop protection planning

get_growing_degree_days

GDD measures heat accumulation used to predict crop development stages, pest emergence, and harvest timing. Essential for phenology tracking, variety selection, and timing agricultural operations. AI agents should reference this when users ask "calculate GDD for my corn field this season", "track crop development stages", or need heat unit accumulation data for agricultural planning. Date format: YYYY-MM-DD. Calculate Growing Degree Days (GDD) for crop development tracking

get_historical_ndvi

Returns time-series NDVI values showing vegetation health progression, stress detection, and recovery patterns. Essential for seasonal crop performance comparison, drought impact assessment, and long-term field health monitoring. AI agents should reference this when users ask "show me NDVI trends for my field over the growing season", "compare vegetation health between seasons", or need historical vegetation index data for agricultural trend analysis. Date format: YYYY-MM-DD. Get historical NDVI trends for seasonal vegetation health analysis

get_ndvi

NDVI ranges from -1 to 1, with higher values (0.6-0.9) indicating healthy dense vegetation and lower values (0.2-0.5) indicating stressed or sparse vegetation. Essential for crop health monitoring, growth stage assessment, and yield prediction. AI agents should use this when users ask "what is the NDVI for my field on this date", "check crop vegetation health", or need satellite-based vegetation index data for agricultural analysis. Date format: YYYY-MM-DD. Get NDVI (Normalized Difference Vegetation Index) for crop health assessment

get_satellite_imagery

Returns imagery metadata and access URLs for visual crop assessment, field boundary verification, and change detection analysis. Essential for remote field monitoring, damage assessment, and visual crop health evaluation. AI agents should use this when users ask "get satellite imagery for my field", "show me the latest satellite view of my farm", or need visual imagery for agricultural monitoring. Date format: YYYY-MM-DD. Zoom: 1-16. Get satellite imagery for visual crop assessment and field monitoring

get_soil_temperature

Soil temperature is critical for seed germination timing, root activity assessment, and nutrient uptake optimization. Essential for planting decisions, irrigation scheduling, and soil health monitoring. AI agents should use this when users ask "what is the soil temperature for planting", "check if soil is warm enough for germination", or need soil thermal data for agricultural planning. Date format: YYYY-MM-DD. Get satellite-derived soil temperature for seed germination and root activity assessment

get_weather_forecast

Essential for planting schedules, harvest timing, spray application windows, and irrigation planning. AI agents should reference this when users ask "what is the weather forecast for my farm this week", "will it rain in the next 5 days", or need forward-looking weather data for agricultural planning. Get multi-day weather forecast for agricultural planning

get_weather_history

Essential for comparing current conditions with historical patterns, analyzing crop performance under past weather conditions, and validating crop models. AI agents should use this when users ask "what was the weather like on this date last year", "show me historical weather for my field", or need past weather data for agricultural analysis. Date format: Unix timestamp (seconds since 1970). Get historical weather data for crop analysis and trend assessment

Example Prompts for OpenWeather Agro in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with OpenWeather Agro immediately.

"What is the NDVI for my corn field at coordinates 41.8780, -93.0977 on April 1st?"

"Calculate the growing degree days for my wheat field from March 1 to today."

"Is there frost risk for my vineyard tonight? I need to know if I should turn on the wind machines."

Troubleshooting OpenWeather Agro MCP Server with Pydantic AI

Common issues when connecting OpenWeather Agro to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

OpenWeather Agro + Pydantic AI FAQ

Common questions about integrating OpenWeather Agro MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an 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?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your OpenWeather Agro MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.