CallFire MCP Server for Pydantic AIGive Pydantic AI instant access to 10 tools to Get Call, Get Campaign, Get Contact, and more
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.
Ask AI about this App Connector for Pydantic AI
The CallFire 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
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())
* 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 manage your voice and SMS communication workflows 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
- Contact Management — List all contacts and retrieve individual contact profiles with phone numbers and metadata
- Call Tracking — Browse all inbound and outbound calls with duration, status, and call recording details
- SMS History — Review sent and received text messages with delivery status and timestamps
- Campaign Monitoring — List all broadcast campaigns (voice and text) and inspect individual campaign configurations and performance
- Webhook Management — View all configured webhooks and inspect their delivery settings and event triggers
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.
All 10 CallFire tools available for Pydantic AI
When Pydantic AI connects to CallFire through Vinkius, your AI agent gets direct access to every tool listed below — spanning sms-marketing, voice-broadcast, call-tracking, 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 a specific call
Get a specific broadcast campaign
Get a specific contact
Get a specific text message
Get a specific webhook
List all calls
List all broadcast campaigns
List all contacts
List all text messages
List all webhooks
Connect CallFire to Pydantic AI via MCP
Follow these steps to wire CallFire into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
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
Example Prompts for CallFire in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with CallFire immediately.
"Show me all active broadcast campaigns and their delivery rates."
"List all text messages sent in the last 24 hours and highlight any that failed delivery."
"How many contacts do I have and are there any with missing phone numbers?"
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
pip install --upgrade pydantic-aiCallFire + Pydantic AI FAQ
Common questions about integrating CallFire MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.