How to Use the Goodcall MCP in Pydantic AI

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Connect Goodcall MCP to Pydantic AI

Create your Vinkius account to connect Goodcall 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 Goodcall with Pydantic AI

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Perplexity

Type-safe tool execution for Pydantic AI

Every response from the Goodcall server is validated against your Pydantic schemas. If the API returns unexpected data, the agent stops immediately. This prevents the common issue of agents trying to parse broken strings. You get clean, validated objects every time you call `get_call`.

Reliable receptionist management in Pydantic AI

Use `list_agents` and `update_agent` with full confidence. Because you use Pydantic models, you know your agent's configuration is always in a valid state. This is essential when you're building systems that need to stay online 24/7. Your phone receptionist stays configured exactly how you want it.

Streamlined booking flow for Pydantic AI

Fetch appointments with `list_bookings` and have them parsed into your local Python objects. It makes scheduling logic trivial and safe. Your agent can now check your availability and confirm times without risking data corruption. It's the most reliable way to sync your phone with your calendar.

Setup guide

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

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

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

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Common questions about Goodcall MCP in Pydantic AI

It adds a layer of runtime validation. If Goodcall returns a malformed transcript, your Pydantic AI agent catches it before it can cause downstream errors.

Install the MCP toolset package and pass it to your Agent instance. It's designed to work with standard HTTP or SSE transports.

Yes. Every tool response is checked against your defined models. `get_analytics` data will always match the schema you expect.

The server doesn't store your data; it acts as a gateway. Your Pydantic AI agent processes call transcripts and metadata directly in your own memory space.

Your agent will throw a validation error instead of crashing silently. You'll know immediately that your schema needs an update.

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