Semgrep MCP Server

Overview

The Stefanskiasan Semgrep MCP Server bridges the gap between static code analysis and AI‑powered development workflows. By exposing Semgrep’s powerful rule engine through the Model Context Protocol, it lets AI assistants query, run scans, and manipulate results without leaving the assistant’s environment. This eliminates the need for manual CLI calls or separate CI pipelines, allowing developers to surface security and style insights directly within their conversational tooling.

Problem Solved

Traditional static analysis tools like Semgrep are command‑line utilities that require developers to install dependencies, manage rule sets, and parse JSON reports manually. When an AI assistant is involved—such as Claude or GPT‑based agents—it can be cumbersome for the assistant to orchestrate these steps, especially in a multi‑project or continuous‑integration setting. The MCP server solves this by providing a standardized, network‑based interface that the assistant can call with simple tool invocations, returning structured results that can be further processed or displayed.

Core Functionality

• Directory scanning () runs a full Semgrep scan on any given path, returning a structured list of findings.
• Rule management (, ) lets assistants discover available rules or add new custom ones on the fly.
• Result analysis (, ) offers high‑level summaries or filtered views based on severity, tags, or file paths.
• Export & comparison (, ) supports multiple formats (JSON, CSV) and side‑by‑side diffing of scan outputs across commits or branches.

These tools are wrapped in the MCP SDK, ensuring consistent authentication, rate‑limiting, and error handling across all operations.

Use Cases

• AI‑driven code reviews: An assistant can scan a pull request, highlight potential issues, and suggest rule updates in natural language.
• Continuous security checks: Integrate the server into CI/CD pipelines where the assistant monitors scan results and triggers alerts or remediation actions.
• Educational tooling: Students can ask an AI tutor to run a Semgrep scan on their assignments and receive explanatory feedback without leaving the chat interface.
• Rule discovery: Developers can query to find relevant security or style rules, then create custom ones with , all via conversational commands.

Integration Workflow

1. Setup: The MCP server runs as a background service, exposing its tools to the AI client through the standard MCP endpoint.
2. Invocation: The assistant sends a tool request (e.g., ) with the target path and any optional parameters.
3. Processing: Semgrep executes locally, and the server packages the results into a JSON payload.
4. Response: The assistant receives the data, can further analyze it (e.g., filter by severity), and present concise summaries or actionable recommendations.

Because the server operates over HTTP, it can be hosted on a local machine, a containerized environment, or a cloud function, giving teams flexibility in how they deploy the analysis engine.

Unique Advantages

• Zero‑touch integration: No need for the assistant to manage Semgrep binaries or rule files; all commands are abstracted behind MCP tools.
• Extensibility: The TypeScript implementation and open‑source license allow teams to add custom tools or modify existing ones without vendor lock‑in.
• Rich result handling: Built‑in export and comparison tools reduce the friction of interpreting raw JSON, enabling richer AI responses.
• Scalable architecture: By leveraging the MCP SDK’s built‑in concurrency controls, the server can handle multiple simultaneous scan requests, making it suitable for large teams or CI pipelines.

In summary, the Stefanskiasan Semgrep MCP Server turns a powerful static analysis tool into an AI‑friendly service, streamlining security reviews, code quality checks, and rule management across diverse development workflows.