How to Use the DeepInfra (Serverless LLM Inference) MCP in Pydantic AI

Ensure type-safe inference with DeepInfra integrated into your Pydantic AI agent workflows.

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Connect DeepInfra (Serverless LLM Inference) MCP to Pydantic AI

Create your Vinkius account to connect DeepInfra (Serverless LLM Inference) 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 DeepInfra (Serverless LLM Inference) with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Typed `create_chat_completion` responses

Every message interaction is validated against your Pydantic models. If the model output doesn't match your schema, the agent catches the error immediately. This prevents bad data from tricking your application logic. You get strict runtime safety for every inference request.

Validate `create_embedding` results

Ensure your vector data is perfectly formatted before it hits your database. The server provides the output, and your agent verifies the structure. It eliminates silent failures. If the API format changes, your agent throws a validation error instead of processing junk.

Run models with `run_native_inference`

Invoke specialized inference models while maintaining strict type enforcement. You define the expected input and output structures for your models. Your agent won't execute if the tool response deviates from your requirements. It keeps your production pipeline predictable and clean.

Setup guide

Set up DeepInfra (Serverless LLM Inference) 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": {
        "deepinfra-serverless-llm-inference-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent DeepInfra (Serverless LLM Inference) transactions")
print(result.output)

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Common questions about DeepInfra (Serverless LLM Inference) MCP in Pydantic AI

Yes, the framework runtime checks every response against your Pydantic models. You define the structure, and it enforces it for every tool call.

It integrates as a standard toolset. You pass the server to your agent, and the framework handles the communication and validation automatically.

The agent treats API errors as standard Python exceptions. You can catch these in your code to retry or handle failures gracefully.

The MCP protocol provides the bridge, and your Pydantic models provide the type safety. They work together to ensure your agent only sees valid data.

Your request data never touches persistent storage. We process the inference in a memory-isolated container, providing a clean slate for every transaction.

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