How to Use the Nalpeiron V10 MCP in Pydantic AI

Use Nalpeiron V10 with Pydantic AI for type-safe license management and strict validation.

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Connect Nalpeiron V10 MCP to Pydantic AI

Create your Vinkius account to connect Nalpeiron V10 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 Nalpeiron V10 with Pydantic AI

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Validate license responses with Pydantic AI

Every response from `get_license` is validated against your Pydantic schemas. If the API returns a malformed object, the agent throws a validation error instead of processing bad data. This ensures your licensing logic is always correct. You eliminate silent failures that occur when an API changes its output structure.

Manage license lifecycle in Pydantic AI

Your agent executes `activate_license` and `deactivate_license` while enforcing strict type constraints on the input arguments. It guarantees that every call contains the required machine ID. This prevents runtime errors in your licensing workflow. You get a predictable execution path that matches your defined data models.

Retrieve customer lists using Pydantic AI

Call `list_customers` to get a structured list of your clients. Pydantic AI converts the raw MCP output into clean Python objects for your application to use. It makes working with large datasets simple. You can iterate through your customer base and verify entitlements without manually parsing JSON blobs.

Setup guide

Set up Nalpeiron V10 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": {
        "nalpeiron-v10-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Nalpeiron V10 tools.",
)

result = await agent.run("List recent Nalpeiron V10 transactions")
print(result.output)

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Common questions about Nalpeiron V10 MCP in Pydantic AI

It validates every tool response against your defined models. If Nalpeiron V10 returns an unexpected field, the agent catches it immediately.

No, you connect to the Vinkius-hosted endpoint. Your Pydantic AI agent communicates with the server over HTTP or SSE.

Absolutely. Just include the toolset in your agent configuration, and Pydantic AI will handle the type-safe conversion of the license list.

Use the MCPToolset class with your endpoint URL. It is the standard way to integrate the server into your agent's tool collection.

The server treats email fields as sensitive identifiers. By using Pydantic models, you can enforce strict access controls on how the agent handles this customer information.

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