How to Use the GPTBots MCP in Pydantic AI

Use GPTBots with Pydantic AI for type-safe interaction with your agents, workflows, and knowledge bases.

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Connect GPTBots MCP to Pydantic AI

Create your Vinkius account to connect GPTBots 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 GPTBots with Pydantic AI

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Validate bot interactions with Pydantic

Every response from the server is checked against Pydantic models. When you call `get_conversation` or `list_knowledge_documents`, the data is validated immediately to prevent runtime surprises. This gives you total confidence in the data your agent receives. If the structure is wrong, the agent stops before it can make a bad decision.

Trigger workflows with strict schemas

Invoke `trigger_workflow` and handle the return values as typed objects. You avoid loose dictionary parsing by using Pydantic AI to enforce the expected output format of every workflow execution. It makes your integration code predictable. You define the model, the agent expects that model, and the MCP server provides the data that matches it.

Manage knowledge documents safely

Create and track documents using `create_knowledge_document` within your type-safe pipeline. You ensure that all inputs and outputs are verified, maintaining the integrity of your knowledge base. This approach avoids silent errors. Your Pydantic AI agent remains stable because it never processes malformed data from the server.

Setup guide

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

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

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

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Common questions about GPTBots MCP in Pydantic AI

If a tool call fails, Pydantic AI raises a validation error or a standard exception. You handle these in your agent logic to maintain system uptime.

The server provides raw data that you map to your Pydantic models. As long as your model definition matches the server output, the integration works perfectly.

Security relies on the Vinkius endpoint token you provide. The server validates your identity before allowing any access to the tools, keeping your data private.

Yes, the MCP server is model-agnostic. Pydantic AI handles the communication between your local model and the server tools regardless of the model provider.

Your conversation logs are secured via tokenized access. Only requests containing your valid endpoint credentials can access `get_conversation` records, ensuring your chat data remains isolated.

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