How to Use the Bandwidth MCP in Pydantic AI

Q: How do I add the Bandwidth MCP Server to my Pydantic AI agent?

Install pydantic-ai-slimmcp and initialize an MCPToolset with the HTTP endpoint. Pass this toolset directly into your agent's toolsets parameter to expose the messaging tools.

Q: How are errors from the Bandwidth API handled in Pydantic AI?

When tools like listmessages fail, the server raises structured exceptions that Pydantic AI captures. This lets your agent catch validation or API errors explicitly and handle them without crashing.

Build type-safe telecom pipelines in Pydantic AI with runtime-validated Bandwidth messaging and campaign management tools.

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Connect Bandwidth MCP to Pydantic AI

Create your Vinkius account to connect Bandwidth 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 Bandwidth with Pydantic AI

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Type-Safe SMS and MMS Delivery

`send_sms` dispatches text messages through your Pydantic AI agent with strict runtime validation on every input phone number. The framework ensures that payload schemas match exactly before executing `send_mms` or passing data to the API. If the Bandwidth API returns unexpected response fields, your agent fails immediately with a validation error rather than passing corrupt data down the line. This type-safety prevents silent failures in your production messaging systems.

Validated Toll-Free Verification Audits

`get_toll_free` pulls verification records directly into your Pydantic AI agent to confirm your phone numbers are cleared for traffic. The agent parses the returned schema against your strict internal models, ensuring compliance data is structured correctly. By validating the verification status at runtime, your agent can safely decide whether to route traffic through a specific toll-free number. This programmatic check keeps your messaging pipelines compliant with zero manual overhead.

Schema-Validated Media Management via MCP Server

`list_media` retrieves your stored media files and validates the file metadata against your Pydantic AI schemas. The agent uses `delete_media` to purge old attachments, ensuring that your storage footprint stays within defined limits. Using this MCP Server ensures that your agent never operates on malformed media records. The structured tool definitions guarantee that every file ID and size attribute matches your expected types before your application processes them.

Setup guide

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

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

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

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Common questions about Bandwidth MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and initialize an MCPToolset with the HTTP endpoint. Pass this toolset directly into your agent's toolsets parameter to expose the messaging tools.

When tools like list_messages fail, the server raises structured exceptions that Pydantic AI captures. This lets your agent catch validation or API errors explicitly and handle them without crashing.

Yes, Pydantic AI is model-agnostic. You can run these messaging tools with local LLMs, OpenAI, or Anthropic, as long as the model supports tool calling.

Yes, our managed endpoints support both SSE and Streamable HTTP transports, allowing your agent to maintain persistent connections for long-running message monitoring.

Your phone numbers and message logs are processed entirely in memory within an isolated V8 sandbox. We enforce strict data isolation policies, meaning your communication logs are never logged to disk or shared across tenants.

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