How to Use the Bland AI MCP in Pydantic AI

Build type-safe voice agents with Pydantic AI and this Bland AI MCP Server.

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Connect Bland AI MCP to Pydantic AI

Create your Vinkius account to connect Bland AI 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 Bland AI with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Strict call typing with Pydantic AI

Every response from `get_call_details` is validated against your Pydantic models. If the API format changes, your agent crashes safely rather than using bad data. This ensures your voice agents always act on reliable information. You won't have to worry about silent errors or corrupted call variables.

Automated dispatch via Pydantic AI

Use `send_batch` to fire off your telephony tasks. You define the payload structure in code, and the server executes the calls. Your agent checks the response against your defined models. It keeps your bulk operations predictable and type-checked from start to finish.

Audit call history with Pydantic AI

Fetch the full list of calls using `list_calls`. Your model parses the entries into clean Python objects. It makes organizing historical data simple. You can iterate through the list and perform checks on every single interaction without writing custom parsers.

Setup guide

Set up Bland AI 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": {
        "bland-ai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Bland AI MCP in Pydantic AI

It works perfectly. Every tool response is validated at runtime to match your model structure.

Yes. When the server returns unexpected data, Pydantic AI throws a validation error immediately. You can catch this and handle it in your agent loop.

The implementation supports async natively. You can integrate it into your agent's toolset without blocking the main event loop.

Install the slim package and pass the server URL to the MCPToolset. It connects your agent to the tools in a few lines of code.

We enforce strict schema validation to prevent injection. Your call metadata, including phone numbers and transcript snippets, is stored in a sandboxed environment and accessed only via your unique API credentials.

Use it with your favorite AI tools

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