How to Use the Anura MCP in Pydantic AI

Strictly validate ad traffic in Pydantic AI. Use Anura to ensure your agents only act on verified human visitors.

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Connect Anura MCP to Pydantic AI

Create your Vinkius account to connect Anura 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 Anura with Pydantic AI

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Type-safe traffic validation for Pydantic AI

Every response from `get_system_status` is validated against your Pydantic models at runtime. If the API returns garbage, your agent throws a validation error immediately. This prevents your agent from acting on hallucinated fraud reports. You get a guarantee that the data your agent uses is exactly what the security server provided.

Robust fraud reporting for agents

Your agent calls the status tool to fetch security data. Because you are using Pydantic AI, the output is parsed into a reliable structure you can trust for your logic. Stop worrying about malformed data breaking your agent. The runtime validation acts as a contract between the security tool and your application logic.

Secure agent decision making

You can now use Anura to back your agent's decisions with verified fraud signals. The integration is clean, type-safe, and built for production environments. It plugs into your agent as a standard toolset. You get the same type-checking benefits you expect from the rest of your Python code.

Setup guide

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

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

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

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Common questions about Anura MCP in Pydantic AI

Install the pydantic-ai-slim package and use the MCPToolset class. Pass your Vinkius HTTP endpoint and the toolset will be available to your agent.

Yes. Every tool response is checked against your defined models. If the server returns an unexpected format, the agent fails safely before moving forward.

Yes. The toolset is model-agnostic. Whether you use OpenAI or a local model, your agent will benefit from the same type-safe security checks.

It supports both Streamable HTTP and SSE transports. This ensures a persistent, reliable connection for your agent workflows.

Anura handles only non-sensitive visitor metadata and fraud patterns. Your data is isolated and protected by the Vinkius sandbox, ensuring no unauthorized access occurs.

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