Can I check the source of a specific call using the agent?

Yes! Use the `get_call_details` tool with the Call ID. Your agent will fetch the detailed metadata, which typically includes the `source` or `tracking_number` associated with the call.

How do I list all the leads from my website forms?

Simply ask the agent to `list_form_submissions`. It will retrieve the latest leads captured through web forms from your CallRail account, including names and submission times.

Does the integration allow creating a new tracking number?

The current toolset focuses on querying and monitoring (Read-Only) for marketing attribution analysis. Provisioning new numbers or changing core routing should be managed via the CallRail administrator dashboard.

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

Name: CallRail
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 CallRail 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 CallRail "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in CallRail?"
    )
    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 CallRail MCP Server

Connect your CallRail account to any AI agent and orchestrate your call tracking, lead management, and marketing attribution workflows through natural conversation.

Pydantic AI validates every CallRail 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

Call Oversight — List all tracked phone calls and retrieve detailed metadata, including durations, tracking numbers, and statuses.
Lead Management — Access leads generated via web forms and monitor their conversion journey directly from your workspace.
Company Coordination — List and retrieve detailed profiles for all companies and clients managed within the account.
Tracker Oversight — Monitor all active tracking numbers and their respective sources to ensure data accuracy.
User & Team Management — Access your directory of users and agents to maintain visibility across your organization.
Alert Monitoring — Retrieve and monitor active account alerts to stay on top of critical issues.

The CallRail 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 CallRail to Pydantic AI via MCP

Follow these steps to integrate the CallRail 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 CallRail with type-safe schemas

Why Use Pydantic AI with the CallRail MCP Server

Pydantic AI provides unique advantages when paired with CallRail 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 CallRail integration code

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

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

CallRail + Pydantic AI Use Cases

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

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

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

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

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

CallRail MCP Tools for Pydantic AI (10)

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

get_account_info

Retrieve core account information

get_call_details

Get details of a specific phone call

get_company_details

Get details of a specific company

list_alerts

List active account alerts

list_calls

List all tracked phone calls

list_companies

List all companies associated with the account

list_form_submissions

List leads generated via web forms

list_tags

List all lead and call tags

list_trackers

List all tracking numbers and sources

list_users

List all users in the account

Example Prompts for CallRail in Pydantic AI

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

"List all my calls from today in CallRail."

"Show the details for form submission with ID 99283."

"List all the companies in my CallRail account."

Troubleshooting CallRail MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

CallRail + Pydantic AI FAQ

Common questions about integrating CallRail 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 CallRail MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.