How to Use the Chaport MCP in Pydantic AI

Q: How do I set up the MCP Server for Pydantic AI?

Install the slim package, define your MCPToolset, and pass it to the agent. It works over both SSE and HTTP.

Use Pydantic AI for type-safe interaction with your Chaport messaging data.

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Connect Chaport MCP to Pydantic AI

Create your Vinkius account to connect Chaport 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 Chaport with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Validate Chaport responses in Pydantic AI

Call `get_visitor_details` to retrieve visitor info. Pydantic AI checks the return against your defined schemas at runtime. This prevents your agent from breaking when the API structure changes. If the data is malformed, you get a clean validation error instead of a crash.

Automate messaging with this MCP Server

Trigger `send_agent_message` to reply to users. Pydantic AI ensures the parameters you pass match the required types before the message sends. It guarantees your agent logic is sound. You never send garbage data to your customer chat interface.

Audit operators through Pydantic AI

Fetch the full list of staff using `list_chaport_operators`. Pydantic AI maps these records to your internal objects automatically. This makes monitoring your team straightforward. Your code always knows exactly who is online and what their status is.

Setup guide

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

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

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

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Common questions about Chaport MCP in Pydantic AI

No. The MCP server provides the definitions, and Pydantic AI validates the responses against those schemas automatically.

You can. The framework is model-agnostic, so it works with local or cloud-based LLMs using the same toolset.

The agent raises a validation error immediately. You avoid silent failures and keep your agent's state predictable.

Install the slim package, define your `MCPToolset`, and pass it to the agent. It works over both SSE and HTTP.

Data is encrypted in transit and handled by a isolated runtime. We do not cache your PII or chat content outside the session.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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