How to Use the Gandi.net (Domain Registration & Hosting API) MCP in Pydantic AI

Type-safe domain and DNS control for your Pydantic AI projects with this MCP Server.

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Connect Gandi.net (Domain Registration & Hosting API) MCP to Pydantic AI

Create your Vinkius account to connect Gandi.net (Domain Registration & Hosting API) 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 Gandi.net (Domain Registration & Hosting API) with Pydantic AI

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Typed DNS operations in Pydantic AI

Stop worrying about malformed API responses. This MCP Server provides strictly typed outputs that Pydantic AI validates against your models in real-time. When you use `list_dns_records`, the agent checks the structure immediately. If the API returns garbage, the agent stops before that data touches your database.

Mailbox and alias management via Pydantic AI

Deploy new mailboxes using `create_mailbox` with full type verification. Pydantic AI ensures every parameter meets your schema requirements before the request leaves your machine. It catches logic errors early. If you try to create an invalid alias, the agent flags it as a validation error instead of failing silently.

Organization tracking with Pydantic AI

Keep your domain ownership clean by using `list_organizations` to verify account scopes. Pydantic AI treats these objects as first-class citizens in your agent's decision tree. It ensures every action is performed within the correct organizational context. You get full visibility and type safety for every Gandi API call.

Setup guide

Set up Gandi.net (Domain Registration & Hosting API) 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": {
        "gandinet-domain-registration-hosting-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Gandi.net (Domain Registration & Hosting API) tools.",
)

result = await agent.run("List recent Gandi.net (Domain Registration & Hosting API) transactions")
print(result.output)

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Common questions about Gandi.net (Domain Registration & Hosting API) MCP in Pydantic AI

Pydantic AI does this automatically. When you call a tool like `get_domain`, the framework validates the JSON response against your defined models at runtime.

Yes. If the server returns unexpected fields or corrupted data, the agent triggers a validation error. This prevents corrupted data from entering your production state.

Yes, the toolset supports both Streamable HTTP and SSE transports. Configure your `MCPToolset` to point to the server URL.

Yes. The type-safety layer is independent of your underlying LLM. You can swap between OpenAI, Anthropic, or local models without breaking your tool integration.

Every request requires your specific endpoint token. The data exchanged—domain names, DNS records, and mailbox settings—is validated against your Pydantic schemas to ensure only expected data is processed.

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