How to Use the Bot9 MCP in Pydantic AI

Q: Can I use Bot9 tools for bot management in Pydantic AI?

You can use createbot and listbots as standard tools. Pydantic AI ensures the parameters you pass to these functions are valid before execution.

Ensure type-safe support automation by pairing the Bot9 MCP Server with Pydantic AI.

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Connect Bot9 MCP to Pydantic AI

Create your Vinkius account to connect Bot9 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 Bot9 with Pydantic AI

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Type-safe bot interaction

Every response from `send_message` gets validated against your Pydantic models. If the server returns a malformed structure, Pydantic AI catches the error before it hits your business logic. This prevents the silent failures common in loosely typed agent systems. You get a clean, validated object every time your agent talks to a user.

Validate knowledge sources

When you call `list_data_sources`, the result is automatically cast to your defined schema. This makes managing URLs for your bots predictable and robust. Use `add_data_source` to push new training links into the system. Since it's all type-checked, you know your input matches the server requirements before the request leaves your environment.

Strict conversation tracking

Retrieve logs with `get_conversation_history` and let Pydantic AI enforce the structure of the message list. This is vital for maintaining an audit trail of support interactions. Use `list_conversations` to pull a validated list of active sessions. It ensures your agent always has the correct data types when deciding which conversation to handle next.

Setup guide

Set up Bot9 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": {
        "bot9-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Bot9 tools.",
)

result = await agent.run("List recent Bot9 transactions")
print(result.output)

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Common questions about Bot9 MCP in Pydantic AI

Yes, the tools are designed to return predictable data that maps perfectly to your Pydantic models. You define the schema, and the agent enforces it.

Use the MCPToolset class and point it to your server URL. The agent will then expose all eight tools with full type validation.

The agent will fail with a validation error. This prevents corrupted data from entering your support pipeline and keeps your system state consistent.

You can use `create_bot` and `list_bots` as standard tools. Pydantic AI ensures the parameters you pass to these functions are valid before execution.

We enforce strict schema validation on all inputs and outputs. Your bot configuration and conversation history are isolated and processed only through secured channels.

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