How to Use the Vectorizer AI MCP in Pydantic AI

Q: Can I use Vectorizer AI with Pydantic AI for multiple outputs?

Yes. You can request SVG, EPS, PDF, and DXF formats using vectorizeimage. The benefit here is that the entire collection of output data types is validated by Pydantic when the response arrives.

Q: What's the best way to use Vectorizer AI with Pydantic AI?

Use it in a sequence where you call getaccountstatus first. Since all responses are validated, you can trust that the credit data received is accurate before initiating costly vectorization jobs.

Q: If I need to clean up old files, how do I use Vectorizer AI with Pydantic AI?

You call deleteimage. The framework validates the deletion outcome payload. This means you'll get a predictable, strongly-typed confirmation that the asset was successfully removed.

Type-safe vectorization with Pydantic AI's MCP Server integration.

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Setup Vectorizer AI with Pydantic AI

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Vectorize images via the MCP Server

The `vectorize_image` tool converts JPG or PNG bitmaps into vectors like SVG, EPS, PDF, and DXF. Since your framework requires absolute correctness, the Pydantic validation ensures that the API response structure for the vector data is exactly what you expect. This means when the conversion happens, if the server delivers an unexpected payload, your agent fails with a clear validation error—no silent bugs.

Handling asset downloads and cleanup

When you call `download_image`, Pydantic validates that the returned file data structure is correct before passing it to your code. Similarly, using `delete_image` ensures the deletion operation was acknowledged with the right status payload. It adds a necessary layer of type safety around asset management tasks.

Checking service status for Pydantic AI

The `get_account_status` tool provides subscription status and credit counts. By validating this response against a Pydantic model, you guarantee that the resource metrics—like remaining credits—are always correctly parsed by your agent. This prevents runtime failures because of unexpected data types from the MCP Server.

Setup guide

Set up Vectorizer AI 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": {
        "vectorizer-ai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Vectorizer AI MCP in Pydantic AI

The server provides the raw vectorization capability. Your agent calls `vectorize_image`, and then the Pydantic framework validates that the response structure matches your defined schema, guaranteeing data integrity.

Yes. You can request SVG, EPS, PDF, and DXF formats using `vectorize_image`. The benefit here is that the entire collection of output data types is validated by Pydantic when the response arrives.

Use it in a sequence where you call `get_account_status` first. Since all responses are validated, you can trust that the credit data received is accurate before initiating costly vectorization jobs.

You call `delete_image`. The framework validates the deletion outcome payload. This means you'll get a predictable, strongly-typed confirmation that the asset was successfully removed.

This server handles JPG and PNG bitmap image tokens. These are the specific input types for `vectorize_image`, making sure your agent knows exactly what raw data is being processed during conversion.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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