How to Use the Certifier MCP in Pydantic AI

Q: How does Pydantic AI validate calls to the Certifier MCP server?

Pydantic AI validates inputs before the call, like for createcredential. It then parses the response from the Certifier MCP server into a strict Pydantic model, which fails loudly if the data is malformed.

Q: Can I prevent my Pydantic AI agent from changing an issued Certifier certificate?

Yes. The updatecredential tool only works on drafts. If your agent tries to update an issued credential, the API will return an error, and Pydantic AI will translate that into a clear exception in your code.

Q: What's the safest way to get template details with Certifier and Pydantic AI?

Have your agent call listcredentialtemplates, which Pydantic AI will parse into a list of safe objects. Then, use getcredentialtemplate with a specific ID. The returned template object is guaranteed to match your model's schema.

Q: How do I add the Certifier tools to my Pydantic AI agent?

Simply create an MCPToolset with your Vinkius URL and pass it in the toolsets list when you initialize your agent. Pydantic AI handles the discovery and model generation automatically.

Issue type-safe digital credentials from any LLM. Pydantic AI validates every Certifier API call so your agent never fails silently.

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Setup Certifier with Pydantic AI

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Create and Issue with Confidence

Your agent can now create draft credentials using `create_credential`. Pydantic AI ensures the recipient name, email, and template ID you provide match the required schema before the request is even sent. No more guesswork. When it's time to go live, your agent calls `issue_credential`. The response, including the new credential ID and verification URL, is automatically parsed into a Pydantic model. If the API returns anything unexpected, your agent will raise a `ValidationError` immediately.

Type-Safe Credential Management

Let your agent manage existing credentials without corrupting data. When it calls `search_credentials` or `list_credentials`, the entire list of results is validated against your Pydantic models. You get clean, predictable objects every time. Need to change something? The `update_credential` tool only works on drafts, and the API enforces this. Pydantic AI makes handling the resulting API error clean, preventing your agent from accidentally trying to modify a locked, issued certificate.

One MCP Toolset, Zero Surprises

Integrating this MCP server is just one line. You create an `MCPToolset` with your Vinkius server URL and add it to your agent. Pydantic AI discovers the tools and automatically generates models for their inputs and outputs. This means you don't have to write boilerplate validation code. Whether your agent is calling `send_credential` or `get_design_template`, you can trust that the data flowing in and out of your application is always correct.

Setup guide

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

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

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

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Common questions about Certifier MCP in Pydantic AI

Pydantic AI validates inputs before the call, like for `create_credential`. It then parses the response from the Certifier MCP server into a strict Pydantic model, which fails loudly if the data is malformed.

Yes. The `update_credential` tool only works on drafts. If your agent tries to update an issued credential, the API will return an error, and Pydantic AI will translate that into a clear exception in your code.

Have your agent call `list_credential_templates`, which Pydantic AI will parse into a list of safe objects. Then, use `get_credential_template` with a specific ID. The returned template object is guaranteed to match your model's schema.

Simply create an `MCPToolset` with your Vinkius URL and pass it in the `toolsets` list when you initialize your agent. Pydantic AI handles the discovery and model generation automatically.

The server processes only the recipient's name and email to create a certificate. Your connection is secured by a Vinkius token, and each server runs in a temporary, isolated sandbox. Pydantic AI adds a layer of safety by ensuring only expected data types are handled by your agent.

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