How to Use the Telebroad MCP in Pydantic AI

Q: Can I check call history using Telebroad and Pydantic AI?

Yes. Calling listcallhistory returns a structured list of records that Pydantic AI validates, guaranteeing the format is correct for your downstream processes.

Q: What data privacy measures are in place with Telebroad and Pydantic AI?

This server touches user profile data (getprofile). The use of Pydantic AI ensures that even when handling sensitive records, the retrieved fields are strictly typed and validated.

Q: Does Pydantic AI guarantee correct SMS sending via Telebroad?

It does. When your agent calls sendsms, Pydantic validates all required arguments—the recipient, the message body—making sure no field is missing before execution.

Q: Is listphonedestinations reliable when using Telebroad and Pydantic AI?

Extremely. Any data returned by listphonedestinations must conform to your schema, eliminating the risk of receiving malformed or unexpected phone numbers.

Guaranteed data correctness for Telebroad using Pydantic AI.

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Connect Telebroad MCP to Pydantic AI

Create your Vinkius account to connect Telebroad to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Telebroad with Pydantic AI

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Perplexity

Structured Call Management

The `list_call_history` tool retrieves call logs, and the `hangup_call` function ends active connections. These actions are critical for maintaining a clean workflow. Because of Pydantic AI’s validation, you'll never get corrupted data fields or unexpected status codes; the agent fails loud if the API response doesn't match your expected schema.

Identifying Contacts and Extensions

Use `get_profile` to fetch a user’s details, which is validated against Pydantic models. For internal routing, run `list_phone_destinations` to get all extensions. This type-safe approach means that when your agent builds a call sequence, it knows the recipient's format and data structure for sure.

Messaging Across Channels

The server allows sending texts with `send_sms` or documents via `send_fax`. You can also check what you sent using `list_faxes`. This consistency is key. If the API returns a message object, Pydantic AI validates that it has all necessary fields before your agent processes it.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Telebroad. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Telebroad MCP in Pydantic AI

The core benefit is data validation. When an MCP tool runs, say `list_sms_conversations`, the response is automatically checked against your defined Pydantic model before your agent reads it.

Yes. Calling `list_call_history` returns a structured list of records that Pydantic AI validates, guaranteeing the format is correct for your downstream processes.

This server touches user profile data (`get_profile`). The use of Pydantic AI ensures that even when handling sensitive records, the retrieved fields are strictly typed and validated.

It does. When your agent calls `send_sms`, Pydantic validates all required arguments—the recipient, the message body—making sure no field is missing before execution.

Extremely. Any data returned by `list_phone_destinations` must conform to your schema, eliminating the risk of receiving malformed or unexpected phone numbers.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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