How to Use the Copernicus Data Space MCP in Pydantic AI

Q: How do I register the Copernicus Data Space toolset in Pydantic AI?

Install the MCP extension and initialize MCPToolset with the Vinkius server URL. Pass this toolset to your agent's toolsets parameter to connect the MCP Server and enable all fourteen tools.

Q: How do I find available metadata filtering parameters?

Use listattributes to get the allowed metadata keys for a collection. This tells your agent exactly which parameters, like cloud cover, are valid for filtering.

Build type-safe geospatial pipelines using Pydantic AI to validate Copernicus satellite metadata at runtime.

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Connect Copernicus Data Space MCP to Pydantic AI

Create your Vinkius account to connect Copernicus Data Space 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 Copernicus Data Space with Pydantic AI

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Validate Copernicus Data Space queries with Pydantic AI

`search_products` queries the Copernicus catalog using strict collection names, date ranges, and WKT polygons. Because Pydantic AI enforces type safety, unexpected API responses trigger an immediate validation error rather than passing corrupt data to your agent. If your workflow requires precise orbital matching, `search_by_orbit_number` targets exact repeat-pass paths. The returned metadata conforms directly to your defined schemas, keeping your change-detection pipelines reliable.

Extract validated metadata and file hierarchies

`get_product` retrieves a satellite product's sensing time, footprint geometry, file size, and checksum by its UUID. Your agent validates these fields against runtime types to ensure no malformed geometries break your GIS software. To inspect the internal structure, `list_product_nodes` maps out the measurement files and metadata XMLs. This structural map lets your agent verify the presence of specific spectral bands before initiating a download.

Securely download raw Sentinel data via MCP Server

`get_product_download_url` generates an authenticated link and a short-lived Bearer token for raw SAFE files. The agent receives this temporary download URL as a validated string, ready for immediate ingestion. To prevent authorization errors from halting your pipeline, `check_copernicus_status` verifies your credentials. This MCP Server verification catches authentication issues at startup before running expensive search queries.

Setup guide

Set up Copernicus Data Space 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": {
        "copernicus-data-space-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Copernicus Data Space 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 Copernicus Data Space. 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 Copernicus Data Space MCP in Pydantic AI

Install the MCP extension and initialize `MCPToolset` with the Vinkius server URL. Pass this toolset to your agent's `toolsets` parameter to connect the MCP Server and enable all fourteen tools.

Yes. Every response from the search tools is validated against runtime schemas. This ensures geometries, file sizes, and UUIDs match expected types before your agent processes them.

Yes. Since the framework is model-agnostic, you can connect this toolset to local LLMs or commercial APIs. The MCP Server handles all communication with the Copernicus API transparently.

Use `list_attributes` to get the allowed metadata keys for a collection. This tells your agent exactly which parameters, like cloud cover, are valid for filtering.

Your spatial coordinates, search parameters, and API keys are transmitted over encrypted connections directly to the Copernicus API. Vinkius runs the server in an ephemeral sandbox, meaning no search history or credentials persist on disk.

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