How to Use the Bland AI MCP in Pydantic AI

Strictly typed phone agent control for Pydantic AI applications.

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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

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Pydantic AI agents with validated phone tools

Every response from this MCP Server passes through your Pydantic models. Use `get_agent_config` to ensure your agent settings match your expected data structure. If the server returns bad data, your agent throws a validation error immediately. This stops corrupted configs from reaching your production phone agents.

Execute calls with type-safe MCP Server tools

Invoke `send_phone_call` with absolute confidence. Your Pydantic schemas enforce the correct arguments for every outbound attempt. Keep track of your active calls using `list_recent_calls`. The output is validated against your models, preventing your agent from misinterpreting the call status.

Manage personas with runtime validation

Use `list_voice_agents` to inventory your active personas. Your Pydantic AI agent verifies the list against your internal schema before taking further action. When you call `update_agent_config`, the result is checked for correctness. This gives you a robust way to modify your agent personas without hidden failures.

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-alternative-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 guarantees that every phone tool call follows your strict data types. You won't deal with silent errors or broken agent states.

Yes, the MCP toolset automatically maps the server response to your models. If the server output doesn't fit, the agent stops and alerts you.

Since the server is model-agnostic, you can swap between models easily. Your Pydantic schemas remain the single source of truth.

The transcript data is passed as a string which your agent validates against a Pydantic model. You define the structure for the summary.

Vinkius encrypts all communication between your agent and the server. Sensitive call metadata is never stored long-term in our managed environment.

Use it with your favorite AI tools

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

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Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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