How to Use the Mediastack MCP in Pydantic AI

Q: Can I use Mediastack to pull historical news in Pydantic AI?

Yes. You call getnews with a date parameter. The tool returns the data, and Pydantic AI validates the structure before your agent acts on the information.

Implement type-safe news retrieval in Pydantic AI with this Mediastack MCP Server, ensuring every article response is validated.

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Connect Mediastack MCP to Pydantic AI

Create your Vinkius account to connect Mediastack to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Mediastack with Pydantic AI

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Type-safe news for Pydantic AI

The `get_news` tool provides structured data that fits perfectly into your Pydantic models. If the API returns unexpected fields, your agent catches the validation error immediately. This prevents bad data from breaking your agent's logic. You get a reliable way to ingest news without worrying about silent data corruption.

Source validation in Pydantic AI

Use `list_sources` to get the list of publishers. Because this is typed, your Pydantic AI agent confirms the source IDs before making a news request. It removes the guesswork from your agent's configuration. You define the allowed sources in your model, and the agent enforces that schema at runtime.

Unified MCP integration for Pydantic AI

Connect using the `MCPToolset` class for a clean setup. It handles the communication between Pydantic AI and the server without extra boilerplate code. The server runs externally and communicates via SSE or HTTP. This keeps your agent's memory footprint low and its performance predictable.

Setup guide

Set up Mediastack MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "mediastack-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Mediastack tools.",
)

result = await agent.run("List recent Mediastack transactions")
print(result.output)

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Common questions about Mediastack MCP in Pydantic AI

Every tool response is checked against your defined Pydantic models. If the server output doesn't match your schema, the agent throws an error, preventing invalid state.

Yes. You call `get_news` with a date parameter. The tool returns the data, and Pydantic AI validates the structure before your agent acts on the information.

Your Pydantic AI agent will fail with a validation error. This ensures you never process corrupted data in your application.

Yes. Since it uses standard MCP, it works with any LLM you use inside the Pydantic AI framework.

All communication is performed over encrypted channels. The server only processes the specific article metadata you request, and it does not store your queries.

Use it with your favorite AI tools

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