How to Use the Crafter CMS MCP in Pydantic AI

Q: Can I use Pydantic AI to publish Crafter CMS content?

Call publishcraftercontent to migrate changes to your delivery engine. Because you use Pydantic, the agent verifies the workflow bounds are met before execution. This ensures safe deployments.

Validate your Crafter CMS content workflows with Pydantic AI and strict type-safe execution.

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Connect Crafter CMS MCP to Pydantic AI

Create your Vinkius account to connect Crafter CMS 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 Crafter CMS with Pydantic AI

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Type-safe Crafter CMS tools in Pydantic AI

Every response from `execute_graphql_payload` is validated against your Pydantic models. If the schema shifts, the agent fails immediately. This stops silent data corruption in its tracks. You get exact field matching for every query you run against the engine.

Validate XML writes in Pydantic AI

Use `write_studio_xml` to provision new content nodes with strict type checking. Your agent ensures the payload matches the expected XML structure before sending. You avoid invalid deployments by catching structure errors early. It keeps your Git-based content consistent across the entire platform.

Audit content state with Pydantic AI

Use `get_studio_tree` to pull folder structures into your agent. Pydantic validates the response against your defined hierarchy models. This prevents the agent from hallucinating content paths. You maintain a strictly typed view of your studio environment at all times.

Setup guide

Set up Crafter CMS 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": {
        "crafter-cms-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Crafter CMS. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Crafter CMS MCP in Pydantic AI

The framework validates every MCP response at runtime. If the server returns unexpected fields, the agent throws a validation error. You get immediate feedback on schema mismatches.

Yes, this MCP server must run externally. You connect to it using the MCPToolset and an HTTP or SSE transport. The agent then communicates with the live studio instance.

The server access is restricted to the tools you expose. We protect your data by validating every request against your defined schemas. No unauthorized XML nodes are written.

Call `publish_crafter_content` to migrate changes to your delivery engine. Because you use Pydantic, the agent verifies the workflow bounds are met before execution. This ensures safe deployments.

The agent fails loudly if the API returns data that violates your Pydantic models. This prevents corrupted state from reaching your database. You can catch these errors in your Python code.

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