How to Use the HackerOne MCP in Pydantic AI

Rigorous HackerOne integration for Pydantic AI. Type-safe report management with runtime validation.

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Connect HackerOne MCP to Pydantic AI

Create your Vinkius account to connect HackerOne 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 HackerOne with Pydantic AI

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Type-safe report handling in Pydantic AI

Every response from `get_report` is validated against your Pydantic models. If the API returns garbage, your agent fails immediately instead of guessing. This prevents silent corruption in your security pipeline. You get predictable, structured data every time the agent makes a call.

Program validation for Pydantic AI

Use `list_programs` to fetch your program list. The MCP server ensures the response matches the schema your agent expects. You eliminate hallucinated fields. The agent only works with the data structures you explicitly define in your code.

Secure bounty operations with Pydantic AI

The `award_bounty` tool requires exact parameters. Pydantic AI checks these inputs before sending the request to the API. You avoid invalid payouts caused by malformed data. It is the safest way to handle financial actions in an automated system.

Setup guide

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

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

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

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Common questions about HackerOne MCP in Pydantic AI

Add the dependency and initialize the MCPToolset. It connects to the server and validates all responses at runtime.

It will throw a validation error. This is a feature, not a bug, protecting your logic from bad API responses.

Absolutely. The tool is type-checked, so your agent can process payment history safely.

Yes. It supports both SSE and Streamable HTTP, giving you flexibility in how you connect to the server.

The server handles raw vulnerability reports and bounty data. Since you use Pydantic models, you control exactly which data points are parsed and stored.

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