How to Use the BotGenuity MCP in Pydantic AI

Run type-safe support agents using Pydantic AI and the BotGenuity MCP Server.

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Connect BotGenuity MCP to Pydantic AI

Create your Vinkius account to connect BotGenuity 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 BotGenuity with Pydantic AI

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Validate support data at runtime with Pydantic AI

The `get_chatbot_details` tool returns structured metadata about your active bots. Pydantic AI validates this payload against strict Python types, ensuring your agent never processes corrupted or unexpected fields. If your support schema changes, the runtime fails immediately instead of letting the agent hallucinate. This strict validation makes it safe to run automated support pipelines.

Type-safe lead ingestion via MCP

The `list_captured_leads` tool fetches customer contact information directly from your BotGenuity account. The framework parses these leads into typed Python objects before your agent even sees them. You can write clean, type-hinted code to handle these leads. There is no need to manually parse JSON or worry about missing dictionary keys.

Run multi-model support audits

The `list_chatbots` tool lists all available support bots. Because this framework is model-agnostic, you can use local models or commercial APIs to audit your configurations. You configure the MCP toolset once using the unified class. Your agent can then safely call `list_chatbot_conversations` across different LLM providers without code changes.

Setup guide

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

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

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

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Common questions about BotGenuity MCP in Pydantic AI

Install the framework and initialize the toolset pointing to your Vinkius endpoint. Pass this MCP server toolset to your Agent instance to start calling support tools.

The framework throws a validation error immediately. This prevents your agent from making decisions based on malformed support transcripts or lead data.

Yes. The framework works with any model provider, allowing you to run tools like `chat_with_bot` using local LLMs for cost savings.

Yes, the framework natively supports async operations. Tools like `get_conversation_history` run asynchronously to prevent blocking your main application loop.

All customer conversation logs and captured leads are handled through zero-trust, ephemeral V8 isolates on Vinkius. Your API keys and support data are never stored or logged on our servers.

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