How to Use the Anyscale MCP in Pydantic AI

Build type-safe agent pipelines connecting Pydantic AI to Anyscale infrastructure tools.

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Connect Anyscale MCP to Pydantic AI

Create your Vinkius account to connect Anyscale 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 Anyscale with Pydantic AI

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Type-safe completion with Pydantic AI

Invoke `chat_completion` and have the response validated against your Pydantic models. If the model output deviates from your expected schema, the agent triggers a validation error immediately. This keeps your agent logic clean and prevents bad data from propagating through your system. You define the shape of the answer and the server ensures the model complies.

Embedding generation for Pydantic AI

Use `generate_embeddings` to process text into vectors within your agent flow. The tool output is strictly typed, so your agent knows exactly what the vector data looks like before processing it further. It eliminates guessing games about data formats. You get predictable, validated inputs for your RAG logic every single time.

Infrastructure visibility in Pydantic AI

Call `list_models` and `list_services` to pull infrastructure status into your agents. The responses are validated automatically, so you always know the current state of your Anyscale deployment. Your agent reacts to this data with confidence. If a service is down or a model is missing, the validation layer alerts you immediately.

Setup guide

Set up Anyscale 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": {
        "anyscale-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Anyscale MCP in Pydantic AI

Install the slim MCP package and initialize an MCPToolset with your server URL. Pass this toolset into your agent definition to start using the tools.

Because it forces runtime validation on every tool call. You get an error if the model returns garbage, which keeps your production code stable.

Yes, if the tool response doesn't match your defined model, Pydantic AI raises a validation exception. You can handle this gracefully in your agent logic.

You can include as many tools as you need in the toolsets list. The agent will orchestrate them as part of its execution plan.

The communication is handled over encrypted channels. We only process the structured model responses and do not store any of your Pydantic-validated payloads.

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