How to Use the Placetel MCP in Pydantic AI

Q: How do I integrate Placetel into Pydantic AI?

Install the slim library and use the MCPToolset class. You pass the server URL to the toolset and include it in your agent configuration for automatic schema checking.

Get type-safe Placetel integration for Pydantic AI with runtime model validation.

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Connect Placetel MCP to Pydantic AI

Create your Vinkius account to connect Placetel to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Placetel with Pydantic AI

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Perplexity

Strictly validated user lookups

Every response from `get_user` or `list_users` is validated against your Pydantic schemas. If the API returns a malformed record, the system triggers a validation error immediately. You avoid silent failures that plague loosely typed agents. The agent refuses to proceed until the data conforms to your expectations, ensuring your internal state remains consistent.

Type-safe call log processing

When the agent calls `list_call_detail_records`, the output is cast into verified objects. You define the structure, and the framework enforces it during the tool execution cycle. This setup allows you to handle `list_calls` results with confidence. If an endpoint changes its schema, your agent halts rather than processing garbage data.

Inventory control with schema enforcement

Managing your phone numbers and SIP users becomes a reliable process. The agent uses `list_numbers` and `list_sip_users` to populate your models with verified data. Each tool response is checked for field integrity. You gain full control over your inventory, knowing that every piece of data processed by the agent has been validated at the boundary.

Setup guide

Set up Placetel 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": {
        "placetel-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Placetel MCP in Pydantic AI

Install the slim library and use the `MCPToolset` class. You pass the server URL to the toolset and include it in your agent configuration for automatic schema checking.

The agent will raise a validation error. Because the framework enforces strict types, you will know exactly which field failed to match your model definition.

It is a perfect match. The server provides the raw data, and the framework ensures it is formatted correctly for your application logic.

The server runs in an ephemeral, isolated environment managed by Vinkius. You connect using a token, and the transport is restricted to verified tool calls.

It processes administrative telephony data, including user records and call logs. This information is treated as transient state, protected by runtime type checking and secure transport.

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