How to Use the ESA Near Earth Objects MCP in Pydantic AI

Enforce strict runtime type validation on ESA Near Earth Objects data using Pydantic AI.

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Connect ESA Near Earth Objects MCP to Pydantic AI

Create your Vinkius account to connect ESA Near Earth Objects 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 ESA Near Earth Objects with Pydantic AI

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Validate MCP Server Risk Outputs at Runtime

`get_risk_list` extracts the official ESA asteroid impact risk list into strongly typed Pydantic models. You define the exact schema for Palermo Scale values and cumulative impact probabilities. If the API returns a string instead of a float, the agent throws a validation error immediately. This MCP integration prevents silent failures in your planetary defense logic. When you call `get_impact_table` to check virtual impactors, Pydantic AI guarantees the projected impact dates match your datetime formats. No hallucinated threat levels slip through.

Lock Down Orbital Elements and Ephemerides

`get_object_orbital_elements` provides the mathematical foundation for trajectory computation. Your agent relies on this data being perfectly structured. Pydantic catches any missing fields before the math runs, saving you from broken calculations. The same applies to `get_object_ephemerides`. When you request right ascension, declination, and visual magnitude for specific timesteps, the framework verifies the coordinate arrays. You swap between OpenAI and Anthropic models without worrying about parsing differences.

Automate Priority Follow-Up Tasks

`get_priority_list` fetches near-Earth objects that desperately need telescope time to fix incomplete orbital arcs. Your agent parses this list and schedules observations. Type safety ensures the designation strings are formatted correctly before hitting your internal systems. Before committing resources, the agent verifies the target via `get_object_physical_properties`. You get the exact physical data sourced from the ESA NEOCC database. If the connection drops, `check_esa_neocc_status` lets the agent handle the outage gracefully.

Setup guide

Set up ESA Near Earth Objects 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": {
        "esa-near-earth-objects-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent ESA Near Earth Objects transactions")
print(result.output)

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Common questions about ESA Near Earth Objects MCP in Pydantic AI

Install the slim package with MCP support. Use MCPToolset with your HTTP endpoint and pass it to your Agent's toolsets array.

Strict type enforcement is critical here. The agent cannot hallucinate asteroid dimensions or miss distances because every response must pass your defined schema validation.

Yes. The unified toolset approach supports both Streamable HTTP and SSE transports for fetching real-time telemetry updates.

The agent fails loudly. If the upcoming approach tool returns a malformed date string, Pydantic throws a validation error instead of passing bad data to your downstream systems.

The server operates purely as a read-only conduit for public ESA data. Pydantic AI validates the right ascension and declination locally, ensuring your proprietary telescope targeting logic never leaves your local execution environment.

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