How to Use the HCL AppScan MCP in Pydantic AI

Q: Can I trigger a secure DAST scan in HCL AppScan using Pydantic AI?

Yes. Your agent can call startdastscan to launch a dynamic test, and then use getscan to verify the execution state while ensuring all returned data matches your schemas.

Build type-safe security workflows with Pydantic AI validating every HCL AppScan vulnerability and scan result.

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Connect HCL AppScan MCP to Pydantic AI

Create your Vinkius account to connect HCL AppScan 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 HCL AppScan with Pydantic AI

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Type-safe dynamic scan execution

Run your web application tests with absolute type safety by triggering HCL AppScan runs with `start_dast_scan`. Your Pydantic AI agent triggers runs and monitors their lifecycle using `get_scan` using the MCP Server, validating every status update against strict schemas. If the AppScan API returns unexpected format changes during a run, the Pydantic AI framework catches it immediately. You don't have to worry about silent failures or corrupted state breaking your automated pipelines.

Strict validation for vulnerability data

Parsing AppScan security findings is highly reliable when your agent retrieves vulnerabilities using `list_issues`. Your agent calls this tool to retrieve vulnerabilities and uses `get_issue` to extract specific details like severity and CVSS scores. Every field returned by the AppScan server is parsed into a Pydantic model. If an AppScan vulnerability payload contains malformed data, your Pydantic AI agent catches the validation error instantly instead of passing bad data to your developers.

Reliable inventory auditing

Track your AppScan software assets without worrying about unexpected null values by querying your inventory with `list_apps`. The agent queries your inventory and gets deep configuration details for specific targets using `get_app` within your Pydantic AI workflows. By validating the AppScan application metadata at the runtime layer, Pydantic AI ensures that your automated reporting scripts always receive clean, well-formed database records.

Setup guide

Set up HCL AppScan 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": {
        "hcl-appscan-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about HCL AppScan MCP in Pydantic AI

The framework wraps the MCP server tools, validating the JSON returned by endpoints like `get_issue` and `get_scan` against strict Python type definitions at runtime.

Yes. Your agent can call `start_dast_scan` to launch a dynamic test, and then use `get_scan` to verify the execution state while ensuring all returned data matches your schemas.

You register the MCP toolset with your agent and ask it to list your assets. The agent will call `list_apps` and return a typed list of applications, ready for safe processing in your code.

The framework will raise a clear validation error immediately. This prevents your agent from making decisions based on corrupted or hallucinated vulnerability details.

Validation happens locally within your secure python environment. The MCP Server runs inside an ephemeral, zero-trust sandbox that never persists your `get_issue` payloads or credentials.

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