How to Use the ArcXP MCP in Pydantic AI

Q: What's the setup for using Pydantic AI with this ArcXP MCP Server?

It's straightforward. After pip install "pydantic-ai-slimmcp", you create an MCPToolset with the Vinkius server URL. Then you pass that toolset to your Pydantic AI Agent during initialization.

Q: Can I use Pydantic AI to prevent my agent from using dangerous ArcXP tools?

Yes. When you create the MCPToolset, you can explicitly list which tool names to include. This lets you create agents with limited permissions, for example, an 'editor' agent that can't use deleteanscontent.

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Setup ArcXP with Pydantic AI

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Enforce Correctness for Every ArcXP Call

This server provides all the tools you need to manage your ArcXP content, from `create_ans_content` to `search_photo_assets`. Your agent can find, create, and modify articles, videos, and author profiles. With Pydantic AI, every response from the ArcXP MCP server is automatically validated. If `get_ans_content` returns a payload that doesn't match your Pydantic model of an article, your agent will raise a `ValidationError` immediately. This prevents corrupted data from propagating through your system.

Model-Agnostic Content Automation with Pydantic AI

Your agent gets access to the full suite of ArcXP tools. It can list authors with `list_content_authors`, fetch specific media with `get_video_asset`, or perform broad searches using `search_ans_content`. Pydantic AI doesn't lock you into one LLM provider. You can use the same type-safe code with an OpenAI, Anthropic, or local model. The logic for calling ArcXP tools remains consistent, validated by Pydantic, no matter which reasoning engine you plug in.

Build Resilient Publishing Agents

This is for building systems where data integrity is non-negotiable. An agent designed to revise articles must be able to trust the data it gets from `get_ans_content` before it constructs an update for `update_ans_content`. Pydantic AI makes this possible. You define your Pydantic models for ArcXP's ANS objects, and the framework handles the rest. If the MCP server ever returns an unexpected structure, your agent stops cold instead of guessing or hallucinating fields, which is exactly what you want in a production workflow.

Setup guide

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

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

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

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Common questions about ArcXP MCP in Pydantic AI

Pydantic AI uses your Pydantic models to structure the agent's output. When calling a tool like `create_ans_content`, the framework helps ensure the arguments match the expected schema, reducing the chance of API errors from ArcXP.

It's straightforward. After `pip install "pydantic-ai-slim[mcp]"`, you create an `MCPToolset` with the Vinkius server URL. Then you pass that toolset to your Pydantic AI `Agent` during initialization.

Yes. When you create the `MCPToolset`, you can explicitly list which tool names to include. This lets you create agents with limited permissions, for example, an 'editor' agent that can't use `delete_ans_content`.

There's a tiny overhead for runtime validation, but it's negligible compared to the network latency of the LLM and ArcXP API calls. The benefit of catching a data corruption issue early far outweighs the microseconds spent on validation.

Yes. Your data, such as the ANS JSON for articles or author profiles, is handled within a dedicated V8 Isolate sandbox for each request. Vinkius manages the secure connection to the ArcXP server, but your actual content is never logged or stored on our platform.

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