What information is needed to validate a visitor?

At a minimum, you need the visitor's IP address and User-Agent string. Providing additional info like source or campaign IDs helps Anura provide more contextual fraud detection.

How do I access the fraud reports?

You can use the `get_report_list` tool to see all available reports. Then, use `get_report_details` for a summary or `download_report` to get a link to the full data file.

What do 'Good', 'Warning', and 'Bad' mean in Anura?

'Good' means the visitor is likely human. 'Bad' indicates clear fraud (GIVT/SIVT). 'Warning' means the visitor shows suspicious traits and requires closer monitoring or additional validation.

Anura MCP Server for Pydantic AI 1 tools — connect in under 2 minutes

Name: Anura
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 1 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Anura through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Anura "
            "(1 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Anura?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Anura MCP Server

Protect your advertising spend with the Anura MCP Server. This integration allows your AI agent to interact directly with Anura.io's advanced fraud detection engine. Validate visitor traffic in real-time, monitor campaign health, and retrieve detailed fraud reports without leaving your workspace.

Pydantic AI validates every Anura tool response against typed schemas, catching data inconsistencies at build time. Connect 1 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Real-Time Validation — Check if a visitor (IP + User-Agent) is 'Good', 'Warning', or 'Bad' instantly.
Campaign Monitoring — List monitored campaigns and traffic sources to identify where fraud is coming from.
Detailed Reporting — List, retrieve, and download comprehensive fraud reports for deep analysis.
Fraud Statistics — Get summarized statistics for specific time ranges to track fraud trends over time.
System Health — Check Anura's current system status to ensure reliable monitoring.

The Anura MCP Server exposes 1 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Anura to Pydantic AI via MCP

Follow these steps to integrate the Anura MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 1 tools from Anura with type-safe schemas

Why Use Pydantic AI with the Anura MCP Server

Pydantic AI provides unique advantages when paired with Anura through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Anura integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Anura connection logic from agent behavior for testable, maintainable code

Anura + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Anura MCP Server delivers measurable value.

Type-safe data pipelines: query Anura with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Anura tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Anura and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Anura responses and write comprehensive agent tests

Anura MCP Tools for Pydantic AI (1)

These 1 tools become available when you connect Anura to Pydantic AI via MCP:

get_system_status

Check Anura system status

Example Prompts for Anura in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Anura immediately.

"Validate visitor with IP 1.2.3.4 and User-Agent 'Mozilla/5.0...'"

"Show me the fraud statistics for the last 7 days."

"List my active campaigns in Anura."

Troubleshooting Anura MCP Server with Pydantic AI

Common issues when connecting Anura to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Anura + Pydantic AI FAQ

Common questions about integrating Anura MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Anura MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.