How do I find my MeteoSource API Key?

Sign up for a free account at [**meteosource.com**](https://www.meteosource.com/), and you will find your API Key in your dashboard. Copy and paste it below.

Does it support location search by name?

Yes. Use the `search_weather_places` tool providing the city or place name. Your agent will return the unique `place_id` required for forecasts.

Are hourly forecasts included?

Yes. The `get_point_forecast` tool retrieves comprehensive weather metadata including current conditions, daily summaries, and hourly breakdowns.

MeteoSource MCP Server for Pydantic AI 5 tools — connect in under 2 minutes

Name: MeteoSource
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 5 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect MeteoSource 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 MeteoSource "
            "(5 tools)."
        ),
    )

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

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 MeteoSource MCP Server

Empower your AI agent to orchestrate your entire meteorological research and weather auditing workflow with MeteoSource, the comprehensive source for hyper-local weather data. By connecting the MeteoSource API to your agent, you transform complex forecast searches into a natural conversation. Your agent can instantly search for monitored places, audit daily and hourly forecasts, and retrieve timezone metadata without you ever touching a weather portal. Whether you are planning outdoor events or conducting regional climate audits, your agent acts as a real-time meteorological consultant, ensuring your data is always precise and localized.

Pydantic AI validates every MeteoSource tool response against typed schemas, catching data inconsistencies at build time. Connect 5 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

Place Auditing — Search for thousands of global locations and retrieve high-resolution place IDs and geographic metadata.
Forecast Oversight — Audit comprehensive point forecasts, including current conditions, daily summaries, and hourly breakdowns.
Geographic Discovery — Find the nearest monitored place by latitude and longitude to maintain strict organizational control over local data.
Temporal Intelligence — Query timezone information for specific places to assist in time-sensitive logistics and event planning.
Operational Monitoring — Check API status to ensure your meteorological research workflow is always operational.

The MeteoSource MCP Server exposes 5 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 MeteoSource to Pydantic AI via MCP

Follow these steps to integrate the MeteoSource 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 5 tools from MeteoSource with type-safe schemas

Why Use Pydantic AI with the MeteoSource MCP Server

Pydantic AI provides unique advantages when paired with MeteoSource 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 MeteoSource integration code

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

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

MeteoSource + Pydantic AI Use Cases

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

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

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

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

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

MeteoSource MCP Tools for Pydantic AI (5)

These 5 tools become available when you connect MeteoSource to Pydantic AI via MCP:

check_api_status

Check if the MeteoSource service is operational

get_nearest_weather_place

Find the nearest monitored place by latitude and longitude

get_place_timezone

Get timezone information for a specific place_id

get_point_forecast

Get weather forecast for a specific place_id

search_weather_places

Search for a place by name to get its place_id for forecasts

Example Prompts for MeteoSource in Pydantic AI

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

"Get weather forecast for 'London' using MeteoSource."

"Search for weather station near latitude 48.8566 and longitude 2.3522."

"What is the timezone for place 'tokyo'?"

Troubleshooting MeteoSource MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

MeteoSource + Pydantic AI FAQ

Common questions about integrating MeteoSource 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 MeteoSource MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.