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Placetel MCP Server for Pydantic AIGive Pydantic AI instant access to 10 tools to Get Group, Get Sip User, Get User, and more

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Placetel through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Ask AI about this App Connector for Pydantic AI

The Placetel app connector for Pydantic AI is a standout in the Communication Messaging category — giving your AI agent 10 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Placetel "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Placetel?"
    )
    print(result.data)

asyncio.run(main())
Placetel
Fully ManagedVinkius Servers
60%Token savings
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V8 IsolateSandboxed
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<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Placetel MCP Server

Placetel

The Placetel MCP Server allows AI agents to interact with your Placetel PBX data seamlessly.

Pydantic AI validates every Placetel tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

  • Retrieve users, SIP users, and groups.
  • Access phone numbers and routing plans.
  • Manage devices and call detail records (CDRs).

The Placetel MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 10 Placetel tools available for Pydantic AI

When Pydantic AI connects to Placetel through Vinkius, your AI agent gets direct access to every tool listed below — spanning cloud-pbx, sip-trunking, call-routing, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

get_group

Get details for a specific group

get_sip_user

Get details for a specific SIP user

get_user

Get details for a specific user

list_call_detail_records

List Call Detail Records (CDRs)

list_calls

List active or recent calls

list_devices

List all devices

list_groups

List all Placetel groups

list_numbers

List all Placetel numbers

list_sip_users

List all Placetel SIP users

list_users

List all Placetel users

Connect Placetel to Pydantic AI via MCP

Follow these steps to wire Placetel into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install Pydantic AI

Run pip install pydantic-ai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 10 tools from Placetel with type-safe schemas

Why Use Pydantic AI with the Placetel MCP Server

Pydantic AI provides unique advantages when paired with Placetel through the Model Context Protocol.

01

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

02

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Placetel integration code

03

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

04

Dependency injection system cleanly separates your Placetel connection logic from agent behavior for testable, maintainable code

Placetel + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Placetel MCP Server delivers measurable value.

01

Type-safe data pipelines: query Placetel with guaranteed response schemas, feeding validated data into downstream processing

02

API orchestration: chain multiple Placetel tool calls with Pydantic validation at each step to ensure data integrity end-to-end

03

Production monitoring: build validated alert agents that query Placetel and output structured, schema-compliant notifications

04

Testing and QA: use Pydantic AI's dependency injection to mock Placetel responses and write comprehensive agent tests

Example Prompts for Placetel in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Placetel immediately.

01

"List all devices in Placetel."

02

"Get call details from today."

03

"List all users in my account."

Troubleshooting Placetel MCP Server with Pydantic AI

Common issues when connecting Placetel to Pydantic AI through the Vinkius, and how to resolve them.

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Placetel + Pydantic AI FAQ

Common questions about integrating Placetel MCP Server with Pydantic AI.

01

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
02

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
03

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Placetel MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.