How to Use the Acunetix 360 MCP in Pydantic AI

Build type-safe agents with Pydantic AI that validate every Acunetix 360 MCP Server vulnerability report at runtime.

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Connect Acunetix 360 MCP to Pydantic AI

Create your Vinkius account to connect Acunetix 360 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 Acunetix 360 with Pydantic AI

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Type-safe vulnerability extraction via MCP Server

Security automation fails when agents hallucinate finding details or severity scores. By connecting this MCP Server, your Pydantic AI agent executes `list_vulnerabilities` and forces the output into strict data models. The framework validates every field before the agent even sees the response. If the API returns an unexpected format or missing severity metric, the system fails loudly with a validation error. You get exact, verified security flaws instead of silent data corruption inside your triage pipeline.

Predictable scan initiation

Starting a security test requires precise parameters. Your agent calls `launch_scan` to test specific web applications, but only after validating the target URL against your predefined Pydantic schemas. This strict typing prevents agents from sending malformed requests or targeting out-of-scope domains. The model-agnostic setup means you don't have to rewrite your validation logic when swapping between OpenAI and Anthropic.

Reliable scan monitoring

Tracking test progress requires consistent data structures. When the agent uses `list_scans`, it expects specific integer percentages and status strings. Pydantic AI ensures the response matches those exact requirements. Your agent processes these verified status updates to determine the next step in your CI/CD pipeline. You build a system that prioritizes absolute correctness over speed when handling active security testing.

Setup guide

Set up Acunetix 360 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": {
        "acunetix-360-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Acunetix 360 tools.",
)

result = await agent.run("List recent Acunetix 360 transactions")
print(result.output)

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Common questions about Acunetix 360 MCP in Pydantic AI

Install the slim package with the MCP extra. Initialize an MCPToolset with the Vinkius URL and pass it in the toolsets array to your Agent.

The framework throws a runtime validation error immediately. It refuses to pass malformed security data to the underlying LLM, preventing downstream hallucinations.

You can use any supported model, including local ones. The type-safety guarantees apply regardless of which LLM you choose to power your security agent.

No. You should use the unified MCPToolset approach. It handles the Streamable HTTP connection to Vinkius perfectly.

The connection handles sensitive vulnerability lists and target URLs. Vinkius secures this pipeline by executing requests inside a stateless V8 sandbox that retains zero memory of your security flaws after execution.

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