How to Use the ChatBot.com MCP in Pydantic AI

Q: How does Pydantic AI validate ChatBot.com user profiles?

When the agent calls getchatbotuserdetails, the returned JSON is parsed directly into a validated Pydantic model. Any missing or malformed fields trigger an immediate runtime validation error.

Type-safe ChatBot.com MCP integration for Pydantic AI agents that require strict runtime validation.

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Connect ChatBot.com MCP to Pydantic AI

Create your Vinkius account to connect ChatBot.com 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 ChatBot.com with Pydantic AI

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Claude

Perplexity

Validate ChatBot.com schemas with Pydantic AI

The `list_story_interactions` tool pulls raw chat logs into your agent while enforcing strict Pydantic schemas on every single field. If ChatBot.com updates its payload format, your agent catches the mismatch instantly instead of processing corrupted data. This strict validation prevents your LLM from hallucinating user details or story IDs. You get clean, typed Python objects directly from the MCP interface.

Securely inspect ChatBot.com webhooks with this MCP Server

Active integration endpoints are retrieved by the `list_chatbot_webhooks` tool so your agent can verify their status and payload formats. Because Pydantic AI enforces strict typing, you can guarantee that webhook URLs and metadata keys match your expectations before running tests. Running these checks keeps your production integrations stable. If an external service changes its API, your agent flags the configuration error before it disrupts live customer chats.

Structure raw training phrases cleanly

Grabbing raw, unmatched user inputs that require manual training is handled by the `list_training_data` tool. Your agent parses these messy strings, filters out spam, and structures them into validated Pydantic models for safe database insertion. This approach ensures your NLP training pipeline never ingests malformed data. By validating every phrase at the runtime level, you keep your ChatBot.com matching engine running smoothly.

Setup guide

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

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

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

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Common questions about ChatBot.com MCP in Pydantic AI

When the agent calls `get_chatbot_user_details`, the returned JSON is parsed directly into a validated Pydantic model. Any missing or malformed fields trigger an immediate runtime validation error.

Yes. Since Pydantic AI is model-agnostic, you can use tools like `list_chatbot_stories` with local models or alternative APIs while keeping the exact same validation logic.

You register the server's HTTP endpoint using the unified toolset class. The framework registers tools like `list_chatbot_entities` and exposes them to your agent automatically.

Your application will fail loudly with a validation error. This prevents the agent from making decisions based on incomplete or corrupted story structures.

All raw training inputs and NLP entities are processed in memory within our ephemeral Vinkius engine. No customer data or conversational logs are ever written to disk or shared with third parties.

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