How to Use the Halo Security MCP in Pydantic AI

Q: How do I configure Halo Security inside Pydantic AI?

You use the MCPToolset class initialized with your Vinkius HTTP endpoint URL. Pass this toolset directly into your Agent constructor to expose all eleven MCP tools to your model.

Bring type-safe security monitoring to your Pydantic AI agents with runtime validation of Halo Security vulnerability data.

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Connect Halo Security MCP to Pydantic AI

Create your Vinkius account to connect Halo Security 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 Halo Security with Pydantic AI

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Validate Halo Security Vulnerabilities with Pydantic AI

The `list_issues` tool retrieves active security vulnerabilities, and `get_issue` pulls granular details for a specific exposure. Using Pydantic AI ensures that every vulnerability payload conforms to strict Python type schemas before your agent processes it. If the underlying API structure changes, your agent raises a validation error immediately instead of failing silently or hallucinating fields. This guarantees that your automated security triages are built on reliable MCP data.

Run Validated Halo Scans via This MCP Server

The `trigger_scan` tool starts targeted security assessments, while `add_target` registers new assets for continuous monitoring. This MCP server enforces structured inputs, meaning your agent cannot send malformed domain strings or invalid parameters to the API. By wrapping these actions in Pydantic models, you catch input errors before they are sent over the network. This saves API tokens and prevents execution failures during critical scanning windows.

Audit Halo Certificates and DNS with Type Safety

The `list_certificates` tool monitors SSL/TLS expiration dates, while `list_dns_records` and `list_open_ports` track external infrastructure configurations. Your Pydantic AI agent parses these records into validated Python objects for immediate analysis. You can easily write custom validators to flag certificates expiring in less than 30 days or open ports that violate your corporate policy. The framework handles the parsing, so you can focus on writing remediation logic.

Setup guide

Set up Halo Security 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": {
        "halo-security-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Halo Security MCP in Pydantic AI

You use the `MCPToolset` class initialized with your Vinkius HTTP endpoint URL. Pass this toolset directly into your `Agent` constructor to expose all eleven MCP tools to your model.

The framework will raise a validation error at runtime. This prevents your agent from acting on corrupt or incomplete vulnerability data, protecting your downstream automation from processing bad inputs.

Yes, this setup is completely model-agnostic. You can connect your validated security toolset to OpenAI, Anthropic, Gemini, or even a locally hosted model running through Ollama.

Yes, the framework supports both Streamable HTTP and SSE transports. This ensures fast, low-latency execution when querying large datasets like `list_open_ports` or `list_targets`.

Your tech stack profiles and open port listings are handled inside an isolated, zero-trust V8 sandbox. No sensitive infrastructure data is logged or stored, ensuring your external attack surface details remain secure.

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