Can I send an SMS using the agent?

Yes! Use the `send_sms` tool with the recipient's phone number and your message content. Your agent will trigger the dispatch through CallFire instantly.

How do I check the status of a specific call?

Simply ask the agent to `get_call_details` and provide the Call ID. It will retrieve the latest status, such as 'finished' or 'failed', directly from CallFire.

Does the integration allow creating a new voice broadcast?

The current toolset is focused on individual actions and querying (Read-Only/Individual Dispatch) for operational control. Massive broadcasts should be configured via the CallFire web dashboard.

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

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

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

Connect your CallFire account to any AI agent and orchestrate your voice and SMS communications, campaign tracking, and contact engagement through natural conversation.

Pydantic AI validates every CallFire 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

SMS Dispatch — Send single or bulk SMS messages to your customers directly from your workspace.
Voice Call Management — Trigger individual voice calls and monitor the status of sent calls in real-time.
Campaign Oversight — List and retrieve metadata for your campaign sounds and verify your communication setups.
Keyword Management — List and monitor your rented keywords for SMS interaction.
Automation Tracking — Access and monitor your active webhooks to ensure your integrations are healthy.
Account Insights — Retrieve core profile and site information straight from your workspace.

The CallFire 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 CallFire to Pydantic AI via MCP

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

Why Use Pydantic AI with the CallFire MCP Server

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

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

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

CallFire + Pydantic AI Use Cases

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

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

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

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

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

CallFire MCP Tools for Pydantic AI (10)

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

get_account_info

Retrieve core account information

get_call_details

Get details of a specific call

get_sms_details

Get details of a specific SMS

list_calls

List made voice calls

list_campaign_sounds

List configured campaign sounds

list_keywords

List rented keywords

list_sent_sms

List sent SMS messages

list_webhooks

List active webhooks

send_sms

Send an SMS message

send_voice_call

Trigger a single voice call

Example Prompts for CallFire in Pydantic AI

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

"Send an SMS to +1234567890 saying 'Hi from Vinkius'."

"List my last 5 voice calls in CallFire."

"Show the details for SMS with ID 99283."

Troubleshooting CallFire MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

CallFire + Pydantic AI FAQ

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