How to Use the Modal (Serverless AI Infrastructure) MCP in Pydantic AI

Q: How does Pydantic AI validate data from Modal (Serverless AI Infrastructure)?

The framework wraps the MCPToolset and parses every JSON response against strict Pydantic schemas. If a tool like getapp returns unexpected fields, validation catches it immediately.

Q: What is the setup process for Modal (Serverless AI Infrastructure) in Pydantic AI?

Install pydantic-ai-slimmcp and initialize the unified MCPToolset with your Vinkius HTTP endpoint. Pass this toolset directly into the Agent constructor.

Q: How does this server protect my infrastructure credentials?

Secret references fetched via listsecrets only contain key names, never raw values. All API tokens are stored in Vinkius's secure zero-trust environment and injected only during execution.

Ensure type-safe serverless orchestration by validating Modal metadata at runtime using Pydantic AI with our MCP Server.

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Connect Modal (Serverless AI Infrastructure) MCP to Pydantic AI

Create your Vinkius account to connect Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) with Pydantic AI

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Validate app schemas using our MCP Server

This MCP Server provides structured outputs for `get_app` that match your local Pydantic models. If the API schema changes unexpectedly, your agent raises a validation error instantly instead of passing corrupt data to the model. Prevent silent failures in your production pipelines by failing loud and fast. You catch mismatches during the initial tool call before any decisions are made.

Inspect secure configurations with strict types

The `list_secrets` tool returns your workspace secret references under strict type guarantees. Your agent reads these dictionary schemas to confirm that required environment variables are present before booting a container. Pydantic AI validates that the returned structure contains the exact keys your application expects. This guarantees your container never boots with missing API tokens.

Monitor block storage volumes reliably

The `list_volumes` tool retrieves your network disk definitions with guaranteed field types. Your agent parses this structured list to verify that storage allocations meet your minimum database requirements. If a volume size or status field deviates from the expected model, the system rejects the output. You maintain absolute control over your storage assertions.

Setup guide

Set up Modal (Serverless AI Infrastructure) 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": {
        "modal-serverless-ai-infrastructure-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Modal (Serverless AI Infrastructure) transactions")
print(result.output)

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Common questions about Modal (Serverless AI Infrastructure) MCP in Pydantic AI

The framework wraps the `MCPToolset` and parses every JSON response against strict Pydantic schemas. If a tool like `get_app` returns unexpected fields, validation catches it immediately.

Install `pydantic-ai-slim[mcp]` and initialize the unified `MCPToolset` with your Vinkius HTTP endpoint. Pass this toolset directly into the `Agent` constructor.

Yes, this integration is model-agnostic. You can run local models via Ollama or use commercial APIs while maintaining full type safety over your serverless MCP tools.

The agent receives a validated error response from the tool execution block. Pydantic AI ensures the error payload matches your defined schema so your code handles the failure gracefully.

Secret references fetched via `list_secrets` only contain key names, never raw values. All API tokens are stored in Vinkius's secure zero-trust environment and injected only during execution.

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