StackHawk MCP Server

Current Version: 1.2.1 Requires Python 3.10 or higher

A Model Context Protocol (MCP) server for integrating with StackHawk's security scanning platform. Provides security analytics, YAML configuration management, sensitive data/threat surface analysis, and anti-hallucination tools for LLMs.

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Table of Contents

• Features
• Installation
• Usage
• Configuration
• Available Tools & API
• YAML & Anti-Hallucination
• Sensitive Data & Threat Surface
• Testing & Development
• Example Configurations
• Contributing
• License
• Integrating with LLMs and IDEs

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Features

• Security Analytics: Organization, application, and vulnerability tools
• YAML Configuration Tools: Creation, validation, schema reference, anti-hallucination field validation
• Sensitive Data & Threat Surface Analysis: Repository, application, and data exposure mapping
• Custom User-Agent: All API calls include a versioned User-Agent header
• Comprehensive Test Suite: Automated tests for all major features

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Installation

1. Install via pip (make sure you have write permission to your current python environment):

   > pip install stackhawk-mcp
   # Requires Python 3.10 or higher
   

Or Install via pip in a virtual env:

> python3 -m venv ~/.virtualenvs/mcp
> source ~/.virtualenvs/mcp/bin/activate
> (mcp) pip install stackhawk-mcp
# Requires Python 3.10 or higher

Or Install via pip using pyenv:

> pyenv shell 3.10.11
> pip install stackhawk-mcp
# Requires Python 3.10 or higher

Or Install locally from this repo:

> pip install --user .
# Run this command from the root of the cloned repository

2. Set your StackHawk API key:

   > export STACKHAWK_API_KEY="your-api-key-here"
   

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Usage

Running the MCP Server

python -m stackhawk_mcp.server

Running the HTTP Server (FastAPI)

python -m stackhawk_mcp.http_server

Running Tests

pytest

Integrating with LLMs and IDEs

StackHawk MCP can be used as a tool provider for AI coding assistants and LLM-powered developer environments, enabling security analytics, YAML validation, and anti-hallucination features directly in your workflow.

Cursor (AI Coding Editor)

• Setup:
  • Follow the installation instructions above to install stackhawk-mcp in your python environment.
  • In Cursor, go to Cursor Settings->Tools & Integrations->MCP Tools
  • Add a "New MCP Server" with the following json, depending on your setup:
    • Using a virtual env at ~/.virtualenvs/mcp:

      {
        "mcpServers": {
          "stackhawk": {
            "command": "/home/bobby/.virtualenvs/mcp/bin/python",
            "args": ["-m", "stackhawk_mcp.server"],
            "env": {
              "STACKHAWK_API_KEY": "${env:STACKHAWK_API_KEY}"
            },
            "disabled": false
          }
        }
      }
      

    • Using pyenv:

      {
        "mcpServers": {
          "stackhawk": {
            "command": "/home/bobby/.pyenv/versions/3.10.11/bin/python3",
            "args": ["-m", "stackhawk_mcp.server"],
            "env": {
              "STACKHAWK_API_KEY": "${env:STACKHAWK_API_KEY}"
            },
            "disabled": false
          }
        }
      }
      

    • Or use python directly:

      {
        "mcpServers": {
          "stackhawk": {
            "command": "python3",
            "args": ["-m", "stackhawk_mcp.server"],
            "env": {
              "STACKHAWK_API_KEY": "${env:STACKHAWK_API_KEY}"
            }
          }
        }
      }
      

    • Then make sure the "stackhawk" MCP Tool is enabled
• Usage:
  • Use Cursor's tool invocation to call StackHawk MCP tools (e.g., vulnerability search, YAML validation).
  • Example prompt: Validate this StackHawk YAML config for errors.

OpenAI, Anthropic, and Other LLMs

• Setup:
  • Deploy the MCP HTTP server and expose it to your LLM system (local or cloud).
  • Use the LLM's tool-calling or function-calling API to connect to the MCP endpoint.
  • Pass the required arguments (e.g., org_id, yaml_content) as specified in the tool schemas.
• Example API Call:

  {
    "method": "tools/call",
    "params": {
      "name": "validate_stackhawk_config",
      "arguments": {"yaml_content": "..."}
    }
  }
  

• Best Practices:
  • Use anti-hallucination tools to validate field names and schema compliance.
  • Always check the tool's output for warnings or suggestions.

IDEs like Windsurf

• Setup:
  • Add StackHawk MCP as a tool provider or extension in your IDE, pointing to the local or remote MCP server endpoint.
  • Configure environment variables as needed.
• Usage:
  • Invoke security analytics, YAML validation, or sensitive data tools directly from the IDE's command palette or tool integration panel.

General Tips

• Ensure the MCP server is running and accessible from your LLM or IDE environment.
• Review the Available Tools & API section for supported operations.
• For advanced integration, see the example tool usage in this README or explore the codebase for custom workflows.

GitHub Copilot Agents

StackHawk can be added to the GitHub Coding Agent as an MCP server or as its own GitHub Custom Agent.

Add to GitHub Coding Agent

You can add StackHawk MCP to the GitHub Copilot Coding Agent. This gives the agent all the stackhawk/ tools.

StackHawk MCP installation into the Coding Agent

General instructions on GitHub

For StackHawk MCP, the MCP Configuration JSON should look something like this:

{
  "mcpServers": {
    "stackhawk": {
      "type": "local",
      "tools": [
        "*"
      ],
      "command": "uvx",
      "args": [
        "stackhawk-mcp"
      ],
      "env": {
        "STACKHAWK_API_KEY": "COPILOT_MCP_STACKHAWK_API_KEY"
      }
    }
  }
}

Then in the Repository's Settings->Environments->copilot->Environment Secrets, add COPILOT_MCP_STACKHAWK_API_KEY with your StackHawk API Key.

Installation verification instructions

StackHawk Onboarding Agent as a GitHub Copilot Custom Agent

You can the StackHawk Onboarding Agent as a custom agent at the enterprise, organization, or repository level in GitHub. When added, the StackHawk Onboarding Agent becomes a selectable option in the Copilot Agent Chat with context to help with onboarding, plus it installs stackhawk-mcp so the agent has access to all of those tools.

StackHawk Onboarding Agent installation

The general approach is to take the StackHawk Onboarding Agent defintion and apply it to either the desired repository, enterprise, or organization in GitHub.

• Instructions for installing into a repository on GitHub
• Instructions for installing into an enterprise on GitHub
• Instructions for installing into an organization GitHub

Note that the mcp-servers block in the StackHawk Onboarding Agent definition references an environment variable called COPILOT_MCP_STACKHAWK_API_KEY. Go to the Repository's Settings->Environments->copilot->Environment Secrets, add COPILOT_MCP_STACKHAWK_API_KEY with your StackHawk API Key.

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Configuration

• All HTTP requests include a custom User-Agent header:

  User-Agent: StackHawk-MCP/{version}
  

• The version is set in stackhawk_mcp/server.py as STACKHAWK_MCP_VERSION.
• Set your API key via the STACKHAWK_API_KEY environment variable.

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Available Tools & API

Security Analytics

• Organization Info: Get details about StackHawk organizations
• Application Management: List/search applications with security status
• Vulnerability Search: Search for vulnerabilities across applications
• Security Dashboard: Generate executive dashboards
• Vulnerability Reporting: Generate detailed reports and analysis
• Trend Analysis: Analyze vulnerability trends
• Critical Findings: Get high-priority findings
• Executive Summaries: Generate executive-level summaries

YAML Configuration Management

• Create Config: Generate StackHawk YAML config files
• Validate Config: Validate YAML against the official schema
• Schema Reference: Fetch the latest StackHawk schema
• Schema Caching: 24-hour TTL, manual refresh
• Anti-Hallucination: Field validation tools

Sensitive Data & Threat Surface

• Sensitive Data Reporting: Organization, app, and repo-level
• Trend Analysis: Track sensitive data exposure
• Critical Data Findings: Identify high-risk data
• Surface Mapping: Map sensitive data and threat surfaces

Example Tool Usage

# Get organization info
org_info = await server._get_organization_info(org_id="your-org-id")

# Validate a YAML config
result = await server._validate_stackhawk_config(yaml_content="...")

# Get application vulnerabilities
vulns = await server._get_application_vulnerabilities(app_id="your-app-id")

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YAML & Anti-Hallucination

• Field Validation: Prevents LLMs from suggesting invalid fields
• Schema Reference: Always up-to-date with the official StackHawk schema
• AI Suggestions: Use suggest_configuration for YAML recommendations
• YAML Validation: Validate any config with validate_stackhawk_config

Official Schema URL: https://download.stackhawk.com/hawk/jsonschema/hawkconfig.json

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Sensitive Data & Threat Surface

• Data Type Categorization: PII, PCI, PHI
• Risk Assessment: Risk scoring, levels, and factors
• Exposure Mapping: Application and repository analysis
• Trend Analysis: Time-based, app, repo, and data type trends
• Surface Mapping: Entry points, risk heatmap, exposure analysis

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Testing & Development

Running All Tests

pytest

Running Individual Tests

pytest tests/test_sensitive_data.py
pytest tests/test_repository_analysis.py

Code Formatting

black stackhawk_mcp/

Type Checking

mypy stackhawk_mcp/

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Example Configurations

Basic Configuration

app:
  applicationId: "12345678-1234-1234-1234-123456789012"
  env: "dev"
  host: "http://localhost:3000"
  name: "Development App"
  description: "Local development environment"

Production Configuration with Authentication

app:
  applicationId: "87654321-4321-4321-4321-210987654321"
  env: "prod"
  host: "https://myapp.com"
  name: "Production App"
  description: "Production environment"
  authentication:
    type: "form"
    username: "your-username"
    password: "your-password"
    loginUrl: "https://myapp.com/login"
    usernameField: "username"
    passwordField: "password"

hawk:
  spider:
    base: true
    ajax: false
    maxDurationMinutes: 30
  scan:
    maxDurationMinutes: 60
    threads: 10
  startupTimeoutMinutes: 5
  failureThreshold: "high"

tags:
  - name: "environment"
    value: "production"
  - name: "application"
    value: "myapp"

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Contributing

Contributions are welcome! Please open issues or pull requests for bug fixes, new features, or documentation improvements.

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License

Apache License 2.0. See LICENSE for details.

Release and Version Bumping

Version bumps are managed via the "Prepare Release" GitHub Actions workflow. When triggering this workflow, you can select whether to bump the minor or major version. The workflow will automatically update version files, commit, and push the changes to main.

Note: The workflow is protected against infinite loops caused by automated version bump commits.

GitHub Actions Authentication

All CI/CD git operations use a GitHub App token for authentication. The git user and email are set from the repository secrets HAWKY_APP_USER and HAWKY_APP_USER_EMAIL.

Workflow Protections

Workflows are designed to skip jobs if the latest commit is an automated version bump, preventing workflow loops.

How to Trigger a Release

1. Go to the "Actions" tab on GitHub.
2. Select the "Prepare Release" workflow.
3. Click "Run workflow" and choose the desired bump type (minor or major).
4. The workflow will handle the rest!