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How to Use the Atlas MCP in Pydantic AI

Build type-safe support agents that validate Atlas ticketing data at runtime with Pydantic AI.

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Connect Atlas MCP to Pydantic AI

Create your Vinkius account to connect Atlas 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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Type-safe ticket validation via Pydantic AI

The `get_ticket` tool returns structured support ticket data that Pydantic AI validates against strict Python schemas. If the Atlas API structure changes, your Pydantic AI agent catches the mismatch instantly instead of failing silently. This runtime safety means your Pydantic AI code won't crash on unexpected null values when fetching Atlas tickets. Your Pydantic AI agent parses the Atlas data safely and extracts exactly what it needs for the next step.

Bulletproof customer lookups with the Atlas MCP Server

The `get_customer` tool retrieves detailed Atlas profiles that are parsed directly into typed Pydantic AI models. Your Pydantic AI agent uses this Atlas MCP tool to ensure customer identifiers match your internal database schemas. When listing profiles using `list_customers`, the Pydantic AI framework enforces type constraints on every Atlas field. This prevents your Pydantic AI agent from passing corrupt Atlas contact details to downstream workflows.

Validate help center articles before drafting replies

The `list_articles` tool lets your Pydantic AI agent search your Atlas help center to find answers to customer questions. Pydantic AI validates the Atlas article structure, ensuring the agent only reads well-formed content. If the Pydantic AI agent decides to escalate the issue, it calls `create_ticket` with typed arguments. The Pydantic AI framework ensures the payload contains all required fields before making the Atlas API call.

Setup guide

Set up Atlas MCP in Pydantic AI

Prerequisites

  • Python 3.10+ installed
  • pydantic-ai-slim[fastmcp] package
  • Active Vinkius subscription with a valid endpoint token
  1. 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. 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. 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": {
        "atlas-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Atlas MCP in Pydantic AI

Import the `MCPToolset` class and pass your Vinkius Atlas server URL to it. Add this toolset to your Pydantic AI agent instance to expose the Atlas ticketing tools over MCP.
The Pydantic AI framework raises a validation error immediately, stopping the Atlas agent run. This prevents your Pydantic AI agent from making decisions based on corrupted or incomplete Atlas JSON.
Yes, Pydantic AI is model-agnostic. You can run local models or commercial APIs while using this Atlas MCP Server to manage your support tickets.
Vinkius manages the Atlas authentication handshake using a single secure token. Your Pydantic AI code only needs the Vinkius endpoint URL to access the Atlas ticketing tools.
All Atlas help articles retrieved via `list_articles` are fetched over encrypted TLS connections. Vinkius processes these requests in ephemeral memory, meaning your Atlas documentation is never stored or cached on our platform during Pydantic AI runs.

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