How to Use the Unkey API Management MCP in Pydantic AI

Q: Can I programmatically list all APIs using Unkey API Management with Pydantic AI?

Yes. Your agent executes listapis, and because of Pydantic validation, you receive a Python object that strictly matches the structure you defined.

Q: Does Unkey API Management support key deletion via Pydantic AI?

Absolutely. You can call revokeapikey, and even this action's success or failure is returned through a structured, validated object that your agent can trust.

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Connect Unkey API Management MCP to Pydantic AI

Create your Vinkius account to connect Unkey API Management 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 Unkey API Management with Pydantic AI

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Validate and read structured key details

When an agent calls `get_api_details`, the response is instantly validated against your defined Pydantic model. If Unkey API Management returns unexpected fields, the agent fails loudly—you never get silent corruption. Similarly, when listing keys via `list_api_keys`, you're guaranteed a predictable schema for immediate consumption.

Control and update credentials reliably

Need to change an old key? The agent calls `update_api_key`. Because the output is validated, you know exactly what structure the updated credential will have. This makes building production code simple. It also supports creating new keys with `create_api_key`, ensuring that even initial credentials fit your expected data model.

Monitor key status and project scope

If you need to check if a key is still active, the agent uses `verify_api_key`. This result is validated, giving you a clean boolean or structured error. The type safety makes your code rock solid. Furthermore, using `list_apis` ensures that every API name and detail you get back conforms exactly to your Python types.

Setup guide

Set up Unkey API Management 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": {
        "unkey-api-management-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Unkey API Management transactions")
print(result.output)

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Common questions about Unkey API Management MCP in Pydantic AI

The MCP Server's output is automatically validated against your Pydantic schema at runtime. This means that whether the agent calls `get_api_details` or `list_apis`, you get guaranteed, type-safe data.

You're managing credentials and associated usage analytics. The Pydantic framework ensures that the resulting Python objects representing this operational metadata are always correct.

Yes. Your agent executes `list_apis`, and because of Pydantic validation, you receive a Python object that strictly matches the structure you defined.

Absolutely. You can call `revoke_api_key`, and even this action's success or failure is returned through a structured, validated object that your agent can trust.

This server touches user API key credentials and usage analytics. The agent processes this sensitive operational metadata while maintaining strict type safety throughout the workflow.

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