MCP Server Semgrep

The MCP Server Semgrep bridges the gap between static analysis tooling and conversational AI assistants. By exposing Semgrep’s powerful pattern‑matching engine through the Model Context Protocol, developers can query codebases for security flaws, style violations, and architectural inconsistencies without leaving their preferred chat interface. This integration turns a traditionally command‑line workflow into an interactive, context‑aware experience where the AI can ask clarifying questions, suggest rule modifications, and explain findings in plain language.

At its core, the server implements a set of high‑level tools that mirror Semgrep’s capabilities: scanning directories for issues, listing available rule sets, creating custom rules, and comparing scans before and after code changes. These actions are wrapped in clean ES‑module handlers that validate paths, manage Semgrep’s runtime detection, and format results for consumption by the AI. The result is a robust, cross‑platform service that requires only Node.js v18+ to run and can be deployed behind any MCP‑compatible client.

For development teams, the value proposition is clear. Continuous scanning of entire repositories surfaces bugs early, enforces coding standards across teams, and reduces technical debt by automating refactoring suggestions. Security teams benefit from automated verification against known vulnerability patterns and the ability to tailor rule sets for project‑specific risks. Meanwhile, reviewers gain a “live” documentation layer: the AI can explain why a fragment is problematic and propose fixes, freeing human reviewers to focus on higher‑level design decisions.

Real‑world scenarios include pre‑deployment security audits, onboarding new contributors with guided rule explanations, and automated compliance checks that run as part of CI pipelines. By integrating with AI workflows, the server enables dynamic interactions—such as “Show me all instances of magic numbers in ” or “Create a rule that flags unused imports”—that would otherwise require manual command execution and result parsing. Its unique advantage lies in the seamless combination of Semgrep’s precise pattern matching with an AI’s conversational context, delivering actionable insights directly within the tools developers already use.