How to Use the BatchDialer MCP in Pydantic AI

Q: Can Pydantic AI manage multiple BatchDialer phone numbers?

Yes. The agent uses listphonenumbers to fetch your managed lines as strictly typed Python objects, letting you route calls safely.

Build type-safe outbound sales agents with Pydantic AI and this verified BatchDialer MCP Server.

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Connect BatchDialer MCP to Pydantic AI

Create your Vinkius account to connect BatchDialer 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 BatchDialer with Pydantic AI

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Validate BatchDialer Lead Data with Pydantic AI

The `add_lead` tool inserts new contacts into your dialer while Pydantic AI validates every field. Running this through our MCP connection ensures strict schemas are enforced. Using `get_lead` lets your agent inspect existing contact records with strict type safety. This guarantees your Python code never crashes due to unexpected null values in your dialer database.

Type-Safe Campaign Audits

The `list_campaigns` tool fetches your active outbound lists and maps them directly to strict Python models. Your agent can safely inspect campaign states without risking silent data corruption. When drilling down, the agent calls `get_campaign` to verify specific budget limits and call schedules. This ensures your automated scripts only interact with valid, active dialing pools.

Parse Call Logs and Outcomes Securely

The `list_call_logs` tool retrieves your dialer history directly into strongly typed Pydantic objects. This makes it incredibly easy to parse timestamps and call durations without writing manual parsing code. Your agent cross-references these logs with `list_dispositions` to categorize every call outcome. Because the data matches your schemas exactly, your downstream reporting pipelines will never break.

Setup guide

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

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

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

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Common questions about BatchDialer MCP in Pydantic AI

Install pydantic-ai-slim with the mcp extra, then initialize MCPToolset with our Vinkius HTTP URL. Pass this toolset into your Agent constructor to instantly expose all ten dialer tools.

The framework will raise a ValidationError immediately instead of letting your agent hallucinate. This keeps your outbound campaigns safe from buggy API responses or missing fields.

Yes. The agent uses `list_phone_numbers` to fetch your managed lines as strictly typed Python objects, letting you route calls safely.

This Vinkius server supports both Streamable HTTP and SSE transports. Pydantic AI connects to our hosted endpoint seamlessly using the unified MCPToolset wrapper.

Every API request flows through an ephemeral V8 sandbox hosting the MCP Server. Your lead lists and campaign configurations are never cached or written to persistent storage.

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