How to Use the Atlas MCP in Pydantic AI
Build type-safe support agents that validate Atlas ticketing data at runtime with Pydantic AI.
Works with every AI agent you already use
…and any MCP-compatible client
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.
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.
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
Install Pydantic AI with FastMCP
Run
pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecatedMCPServerHTTPclass with full protocol support. - 2
Configure the FastMCPToolset
Pass a JSON-style config dict to
FastMCPToolsetwith 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 callagent.run(). Swapopenai:gpt-4ofor any supported model — Anthropic, Google, Mistral, or Groq.
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) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Atlas. 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 Atlas MCP in Pydantic AI
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