How to Use the Clever Cloud (Developer PaaS API) MCP in Pydantic AI

Q: How do I configure the Clever Cloud (Developer PaaS API) MCP Server with Pydantic AI?

Install pydantic-ai-slimmcp. Initialize an MCPToolset with your Vinkius HTTP endpoint. Add that toolset to your Agent definition.

Q: Can Pydantic AI validate data from the Clever Cloud (Developer PaaS API)?

Yes. Every response from tools like getapplicationlogs or listinstances is checked at runtime. The agent throws a validation error if the schema mismatches.

Q: Does the Clever Cloud (Developer PaaS API) work with local models in Pydantic AI?

The integration is totally model-agnostic. You can trigger createapplication using OpenAI, Anthropic, or a locally hosted model. The validation layer remains identical.

Q: What transport method should I use for this MCP server?

Use the unified MCPToolset with a standard HTTP string. Vinkius handles the routing. The old MCPServerHTTP class is deprecated and should be avoided.

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Connect Clever Cloud (Developer PaaS API) MCP to Pydantic AI

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Setup Clever Cloud (Developer PaaS API) with Pydantic AI

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Claude

Perplexity

Type-checked infrastructure via MCP Server

Silent failures in infrastructure code cost money. Connecting this MCP toolset to Pydantic AI ensures every API response matches your exact schema. You pass the Vinkius URL to `MCPToolset` and the framework handles the validation. If Clever Cloud changes a payload structure, your agent fails loudly. Calling `get_application` returns data that gets verified before the LLM even sees it. Hallucinated fields are mathematically impossible.

Provision add-ons with strict validation

Spinning up databases requires precise configuration. The agent uses `provision_addon` to request new resources. Pydantic guarantees the region, plan, and provider strings match your predefined literal types. Swapping models requires zero rewrites. Running a local Llama or Claude 3.5 makes no difference, because the agent calls `list_addons` exactly the same way. The framework abstracts the model differences completely.

Manage organizations and deployments

Auditing access means pulling exact user records. The agent runs `get_current_user` and `list_organizations` to map out permissions. Every ID and email address gets parsed into a strict Python object. Tearing down environments is dangerous. When your code triggers `delete_application`, you know the target ID was validated by your Pydantic rules. The execution stops immediately if the parameters look wrong.

Setup guide

Set up Clever Cloud (Developer PaaS 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": {
        "clever-cloud-developer-paas-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Clever Cloud (Developer PaaS API) transactions")
print(result.output)

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Common questions about Clever Cloud (Developer PaaS API) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize an `MCPToolset` with your Vinkius HTTP endpoint. Add that toolset to your `Agent` definition.

Yes. Every response from tools like `get_application_logs` or `list_instances` is checked at runtime. The agent throws a validation error if the schema mismatches.

The integration is totally model-agnostic. You can trigger `create_application` using OpenAI, Anthropic, or a locally hosted model. The validation layer remains identical.

Use the unified `MCPToolset` with a standard HTTP string. Vinkius handles the routing. The old `MCPServerHTTP` class is deprecated and should be avoided.

Your infrastructure tokens remain secured within the Vinkius vault. The sandbox processes your add-on details and deployment logs in isolated memory. Once the Pydantic script finishes, the runtime environment self-destructs.

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