How to Use the Avochato MCP in Pydantic AI

Use Pydantic AI to enforce strict data types when your agent interacts with Avochato.

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Connect Avochato MCP to Pydantic AI

Create your Vinkius account to connect Avochato 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 Avochato with Pydantic AI

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Perplexity

Type-safe messaging with Pydantic AI

Every message sent via `send_message` gets validated against your Pydantic models. If the schema is wrong, the agent stops before it sends junk data. This ensures your Avochato interactions remain consistent. You get clear errors instead of silent failures when your agent makes a mistake.

Sync contacts through this MCP Server

Update your records using `update_contact` with guaranteed data structures. Your agent populates fields only if they match your predefined types. Use `get_contact` to pull records that the agent then cleans or parses. The framework rejects any response that doesn't fit your schema.

Audit Avochato broadcasts with Pydantic AI

List your past campaigns with `list_broadcasts` and have the agent summarize them. The typing ensures that broadcast timestamps and IDs are always valid. Create new broadcasts with `create_broadcast` while the agent checks your inputs. It keeps your messaging pipeline clean and predictable.

Setup guide

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

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

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

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Common questions about Avochato MCP in Pydantic AI

Install the slim package and define an MCPToolset with your HTTP endpoint. Add it to your agent's toolset list to enable the typed tools.

The agent throws a validation error immediately. This stops the execution and forces the agent to handle the bad data safely.

You can. The framework is model-agnostic, so you can run the Avochato tools against any local model that supports tool calling.

The Vinkius platform handles the auth and sandbox security. Your agent only sees the data it explicitly requests through the validated tool interface.

It touches contact lists and message content logs. All of this is encrypted in transit and handled within the ephemeral V8 sandbox.

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