Can my AI automatically add a new lead to BatchDialer?

Yes! Use the `add_lead` action with the contact's name and phone number. Your agent will register the lead in your BatchDialer system instantly.

How do I easily list all recent call logs?

Simply ask the agent to `list_call_logs`. It will compile the history of recent outbound activities, including status and duration.

Does the integration allow starting an active call from the agent?

No. For security and operational safety, the current toolset focuses on management and auditing (listing campaigns, managing leads, checking logs). Initiating live calls must be done through the BatchDialer interface.

BatchDialer MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: BatchDialer
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 BatchDialer "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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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 BatchDialer MCP Server

Connect your BatchDialer account to any AI agent and take full control of your sales and outbound calling operations through natural conversation.

Pydantic AI validates every BatchDialer 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

Campaign Management — List and inspect all dialing campaigns to monitor active sales operations.
Lead & Contact Control — Add, query, and manage your contacts (leads) to ensure your dialing lists are always up to date.
Call Log Analysis — Retrieve complete call histories, including durations and outcomes (dispositions).
Phone Number Management — Monitor your caller IDs and managed phone numbers directly from the agent.
Outcome Tracking — List and understand call dispositions to categorize lead interactions accurately.

The BatchDialer 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.

How to Connect BatchDialer to Pydantic AI via MCP

Follow these steps to integrate the BatchDialer MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

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

Explore tools

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

Why Use Pydantic AI with the BatchDialer MCP Server

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

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

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

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

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

BatchDialer + Pydantic AI Use Cases

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

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

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

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

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

BatchDialer MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect BatchDialer to Pydantic AI via MCP:

add_lead

Add a new lead/contact

get_call_details

Get details of a specific call

get_campaign

Get specific campaign details

get_lead

Get specific lead details

get_user_profile

Get authenticated user profile

list_call_logs

List call logs/history

list_campaigns

List all BatchDialer campaigns

list_dispositions

List call outcomes/dispositions

list_leads

List contacts/leads

list_phone_numbers

List managed phone numbers

Example Prompts for BatchDialer in Pydantic AI

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

"List all our active dialing campaigns on BatchDialer."

"Add a new lead: John Doe, phone 555-0199, email john@example.com."

"Show the recent call logs from today."

Troubleshooting BatchDialer MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

BatchDialer + Pydantic AI FAQ

Common questions about integrating BatchDialer MCP Server with Pydantic AI.

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.

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.

Can I switch LLM providers without changing MCP code?

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