How to Use the Chatsistant MCP in Pydantic AI

Q: Can I use Pydantic AI to list Chatsistant webhooks?

Yes, invoke listwebhooks as a standard tool. The output is validated against your Pydantic model just like any other server response.

Use Pydantic AI with Chatsistant for type-safe agent operations that validate every response against your schema.

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Connect Chatsistant MCP to Pydantic AI

Create your Vinkius account to connect Chatsistant 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 Chatsistant with Pydantic AI

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Validate Chatsistant data with Pydantic AI

Every tool call like `get_bot` runs through Pydantic models before the agent processes the result. If the data structure deviates from your expected format, the process stops immediately. This gives you a runtime guarantee that the information from your bots matches your code's requirements. You won't deal with silent data corruption.

Access bot knowledge via Pydantic AI

Trigger `query_bot` to retrieve information from your knowledge base. The response is immediately cast to a type-safe object, ensuring your agent logic only runs on clean data. `list_data_sources` provides the list of available sources. Your agent validates these against your internal registry to prevent unauthorized access.

Track conversations using Pydantic AI

Run `get_conversation` to import chat logs into your agent's memory. Pydantic AI enforces the schema for every message field, keeping your input strictly defined. `list_conversations` allows you to sort logs by bot ID. You'll know exactly what you're getting because the response matches your defined model every time.

Setup guide

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

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

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

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Common questions about Chatsistant MCP in Pydantic AI

Install the slim MCP package, then initialize an MCPToolset with your server endpoint. Pass this toolset into your Agent definition to enable tool access.

The agent will raise a validation error immediately. This stops the execution flow so you can handle the bad data before it impacts your system.

You keep the server running externally. The agent connects via streamable HTTP or SSE, ensuring a stable link between your code and the bot data.

Yes, invoke `list_webhooks` as a standard tool. The output is validated against your Pydantic model just like any other server response.

Your data is only accessed through authenticated calls. We use runtime type checking to ensure your agent doesn't leak or mishandle the raw knowledge base strings.

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