How to Use the Zenedu MCP in Pydantic AI

Q: How does Zenedu work with Pydantic AI for bot management?

The MCP Server provides the raw data (e.g., calling listbots). Your agent receives this, and Pydantic immediately validates it against your Python model, ensuring structural integrity before processing.

Q: Can I use Zenedu to check product availability with Pydantic AI?

Yes. You call listbotproducts. The resulting list is schema-validated, meaning you know exactly what fields are available and that they are the correct data type for your application logic.

Q: Does Zenedu support complex messaging flows with Pydantic AI?

It supports fetching all necessary components: bots (listbots), funnels, and offers. The value-add of Pydantic is that the relationship data returned is always type-safe.

Q: What if I need to combine multiple list functions with Zenedu and the Pydantic AI?

You just chain the calls—say, listbotsubscribers followed by listbotorders. The combined output is then validated as a whole against your defined schema model.

Get guaranteed data schemas from Zenedu using Pydantic AI.

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Connect Zenedu MCP to Pydantic AI

Create your Vinkius account to connect Zenedu 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 Zenedu with Pydantic AI

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Validate product schema lists with the MCP Server.

Run `list_bot_products` to get a raw list of all available products. Because your framework validates everything against Pydantic models, you're guaranteed that every returned field (like product ID or name) matches the expected type. This prevents runtime errors; if the API sends bad data, your agent fails loudly with an error, not silently corrupting your workflow.

Check funnels and offers schema relationships using the MCP Server.

You can call `list_bot_funnels` and `list_bot_offers`. The resulting data is then immediately validated by Pydantic, ensuring that any relationship fields (like offer IDs) are correctly formatted. This rigor means you trust the data structure completely, which is key for mission-critical agent logic.

Get reliable order and subscriber records with the MCP Server.

Using `list_bot_orders` and `list_bot_subscribers` provides two distinct sets of structured data. Pydantic ensures that each record—whether it's an order or a user profile—adheres strictly to its defined schema. It’s about correctness first. The agent can proceed with high confidence because the data types are guaranteed.

Setup guide

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

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

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

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Common questions about Zenedu MCP in Pydantic AI

The MCP Server provides the raw data (e.g., calling `list_bots`). Your agent receives this, and Pydantic immediately validates it against your Python model, ensuring structural integrity before processing.

Yes. You call `list_bot_products`. The resulting list is schema-validated, meaning you know exactly what fields are available and that they are the correct data type for your application logic.

It supports fetching all necessary components: bots (`list_bots`), funnels, and offers. The value-add of Pydantic is that the relationship data returned is always type-safe.

You just chain the calls—say, `list_bot_subscribers` followed by `list_bot_orders`. The combined output is then validated as a whole against your defined schema model.

This server touches subscriber records and order history. By passing this through Pydantic, you guarantee that even sensitive fields are retrieved, validated, and typed correctly.

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