StackHawk MCP Server

StackHawk MCP Server

The StackHawk Model Context Protocol (MCP) server bridges the gap between AI assistants and a comprehensive security scanning platform. By exposing StackHawk’s analytics, YAML tooling, and threat surface analysis as MCP resources, the server lets developers embed deep security insight directly into their AI‑powered workflows. This integration removes the need to manually run scans or interpret results, enabling real‑time feedback during code review, continuous integration, or IDE usage.

At its core, the server offers three value‑driven capabilities. First, security analytics aggregates vulnerability data across organizations and applications, presenting it in a structured format that LLMs can query for risk summaries or remediation guidance. Second, the YAML configuration tools allow automated creation, validation, and schema reference of configuration files, while an anti‑hallucination layer ensures that generated YAML adheres to known schemas and avoids misleading suggestions. Third, sensitive data and threat surface analysis maps repositories and applications to potential exposure points, giving assistants the context needed to flag accidental leaks or insecure patterns.

These features translate into concrete use cases. In a CI pipeline, an AI assistant can invoke the MCP to run a vulnerability scan after each commit and surface actionable findings before code merges. In an IDE, the assistant can validate a Kubernetes manifest against its schema and immediately correct syntax errors or missing fields. During pair programming, the assistant can analyze a repository’s threat surface and suggest least‑privilege configurations or data‑handling best practices. Because the MCP presents these operations as simple, well‑defined tools, developers can compose them in prompts or workflows without dealing with raw API calls.

Integration is straightforward for teams already using MCP‑compatible LLMs. The server registers resources, tools, and prompts that the assistant can discover at runtime. Developers configure environment variables for authentication (e.g., ) and launch the server as a background process. Once registered, AI assistants can call methods such as , , or and receive structured JSON responses that can be fed back into the conversation or displayed inline in the editor.

Unique to StackHawk’s MCP implementation is the anti‑hallucination mechanism for YAML. By cross‑checking generated fields against a live schema reference, the server reduces the risk of AI assistants producing syntactically valid but semantically incorrect configurations—an essential safeguard in security‑critical environments. Additionally, the server’s custom header ensures that all outbound API traffic is traceable back to the specific MCP version, simplifying auditability and support. Together, these advantages make the StackHawk MCP Server a powerful addition to any developer’s AI‑enhanced security toolkit.