Semgrep MCP Server

Semgrep MCP Server

The Semgrep MCP server bridges the gap between advanced static analysis tooling and AI‑powered assistants. By exposing Semgrep’s scanning engine over the Model Context Protocol, it allows developers to embed automated code‑review capabilities directly into conversational workflows. This means an AI assistant can ask for a security audit, run targeted rule sets against the current project, and return concise, actionable findings—all without leaving the chat interface.

Semgrep is renowned for its fast, language‑agnostic pattern matching and extensive rule library. The MCP server wraps this functionality in a lightweight, stateless service that accepts code snippets or repository references and returns structured results. For developers, this translates into a single point of integration: the AI can invoke scans on demand, filter by severity or rule family, and even trigger custom rules defined in the local . The server’s design prioritizes speed and low overhead, making it suitable for continuous integration pipelines, IDE extensions, or on‑demand security checks in a coding session.

Key capabilities include:

• Rule selection and customization – choose from Semgrep’s curated rule sets or supply bespoke rules to focus on project‑specific concerns.
• Granular filtering – narrow results by severity, language, or rule identifier to surface only the most relevant findings.
• Structured output – results are returned as JSON objects that map to MCP’s type, enabling downstream processing or display in a developer‑friendly format.
• Scalable execution – the server can run locally or be deployed as a container, supporting high‑volume scans across large codebases.

Real‑world use cases span security hardening, compliance verification, and continuous code quality monitoring. A data engineer can ask the assistant to “scan for hard‑coded secrets” and receive a list of files with line numbers, while a QA lead can request “run the performance rule set” to catch inefficient patterns before release. In CI/CD pipelines, the MCP server can be invoked as a step that feeds findings back into pull‑request comments or issue trackers, ensuring that code never slips past automated checks.

Integration with AI workflows is seamless: the server exposes a simple HTTP interface that MCP‑enabled assistants can call. Once invoked, the assistant presents the findings in a conversational tone, offers remediation suggestions, and can even trigger follow‑up actions such as opening an issue or applying a fix via a GitHub Action. This tight coupling between static analysis and conversational AI empowers developers to maintain higher standards of code quality with minimal friction, making the Semgrep MCP server a valuable addition to any modern development environment.