How to Use the ECB Discovery — Universal Statistical Data Access MCP in Pydantic AI

Q: How do I configure ECB Discovery — Universal Statistical Data Access in Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your HTTP endpoint on Vinkius. Pass this toolset array into your Agent constructor.

Type-safe European Central Bank statistical queries for your Pydantic AI agents.

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Connect ECB Discovery — Universal Statistical Data Access MCP to Pydantic AI

Create your Vinkius account to connect ECB Discovery — Universal Statistical Data Access 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 ECB Discovery — Universal Statistical Data Access with Pydantic AI

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Type-Safe Catalog Browsing

The `list_dataflows` tool pulls the European Central Bank dataset catalog directly into your agent's reasoning loop. It fetches the required codes, like BSI or YC, along with their descriptions. Your agent uses this index to find the right statistical domain. Pydantic AI ensures that any downstream function relying on this catalog receives exactly the data structure it expects.

Fetch Validated SDMX Data

The `query_ecb_data` tool requires a dataflow code and an SDMX series key to extract specific numbers. An agent passes BKN and the correct series key to pull banking statistics. If the ECB returns an unexpected format, Pydantic AI catches it immediately. The framework throws a validation error instead of passing hallucinated economic data into your production application.

Run on Any MCP Server Backend

You trigger the `list_dataflows` tool across whatever LLM backend makes sense for your budget. Pydantic AI routes the tool calls whether you use Anthropic, OpenAI, or a local model. The behavior remains identical across providers. Your agent requests monetary aggregates, the server fetches the SDMX response, and the framework validates the output.

Setup guide

Set up ECB Discovery — Universal Statistical Data Access 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": {
        "ecb-discovery-universal-statistical-data-access-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to ECB Discovery — Universal Statistical Data Access tools.",
)

result = await agent.run("List recent ECB Discovery — Universal Statistical Data Access transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by European Central Bank. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ECB Discovery — Universal Statistical Data Access MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Create an MCPToolset pointing to your HTTP endpoint on Vinkius. Pass this toolset array into your Agent constructor.

No. The legacy classes are deprecated. You must use the unified MCPToolset approach to connect to the external server.

The framework enforces strict runtime validation. If the tool returns malformed SDMX statistics, the agent fails loudly rather than silently corrupting your application state.

Yes. Pydantic AI is completely model-agnostic. As long as your local model supports tool calling, it can trigger these MCP server endpoints.

Vinkius manages the connection via an ephemeral sandbox. The system processes public financial statistics and yield curves, then destroys the execution environment immediately after returning the payload.

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