How to Use the Zenkit MCP in Pydantic AI

Q: What kind of operations can I run with Zenkit using Pydantic AI?

You can perform full CRUD operations: listing items (listentries), reading details (getlistdetails), creating new records (createentry), updating existing data, and deleting entries.

Q: Can I check the full scope of workspaces with Zenkit and Pydantic AI?

Absolutely. You use listworkspaces first, then you can drill into specifics using getworkspacedetails. This gives your agent a complete map of where data is stored.

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Connect Zenkit MCP to Pydantic AI

Create your Vinkius account to connect Zenkit 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 Zenkit with Pydantic AI

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Perform reliable data writes using the MCP Server

When your agent needs to write data, it calls `create_entry` or `update_entry`. Because of Pydantic validation, if the API sends back anything unexpected, the whole process fails loud and clear—no silent corruption. This makes your agent reliable. It also supports deleting records with `delete_entry`, giving you a clean way to remove bad data without risking partial updates.

Audit list structures using Zenkit for Pydantic AI

Need to know what fields an API expects? The agent calls `list_elements` and gets the full schema. This structured output is perfect for Pydantic validation, ensuring your model knows exactly how to read it. You can also check overall list parameters with `get_list_details`. It verifies that every piece of data you try to process fits the defined rules.

Manage context and scope using Zenkit

The agent uses `list_workspaces` to map out all available areas. If it needs details on a specific area, it calls `get_workspace_details`. This helps the system keep track of its operational boundaries. It also provides methods like `get_list_details` and `list_entries`, giving the agent verifiable context about the data it's reading before it attempts any write operations.

Setup guide

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

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

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

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Common questions about Zenkit MCP in Pydantic AI

The MCP Server exposes Zenkit tools, and your agent calls them. Because the response is type-checked against Pydantic models, you get immediate runtime validation for every piece of data received.

You can perform full CRUD operations: listing items (`list_entries`), reading details (`get_list_details`), creating new records (`create_entry`), updating existing data, and deleting entries.

It guarantees correctness. If the external API returns malformed data, your agent fails hard with an explicit validation error. You don't risk silent bugs or hallucinated fields.

The server touches 'list entries' data. When designing workflows, use `list_elements` to narrow down exactly which fields are necessary for the operation, minimizing exposure.

Absolutely. You use `list_workspaces` first, then you can drill into specifics using `get_workspace_details`. This gives your agent a complete map of where data is stored.

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