Semgrep MCP Server Integration

What does “Semgrep” MCP Server do?

The Semgrep MCP (Model Context Protocol) Server is a powerful tool that connects AI assistants with the Semgrep code analysis engine, enabling advanced code scanning for security vulnerabilities. Acting as a bridge between large language models (LLMs) and the Semgrep toolset, it allows AI-driven workflows to automatically analyze source code, identify security issues, and integrate these results into broader development or security pipelines. By exposing Semgrep’s scanning capabilities through the MCP protocol, developers and security teams can automate vulnerability detection, enforce code quality standards, and seamlessly incorporate security checks into continuous integration and AI-assisted coding environments.

List of Prompts

No prompt templates are documented in the available files or repository.

List of Resources

No specific resources are documented in the available files or repository.

List of Tools

No tools are explicitly listed in server.py or other available files in the repository listing.

Use Cases of this MCP Server

• Automated Security Scanning: Integrate Semgrep’s scanning engine into your CI pipelines to identify code vulnerabilities early in the development process, reducing the risk of security breaches.
• AI-Augmented Code Review: Enable AI assistants to perform security-focused code reviews by leveraging Semgrep’s rules and detection capabilities, improving the thoroughness and consistency of code inspections.
• Continuous Compliance: Enforce security and compliance policies across large codebases by regularly running Semgrep scans and surfacing actionable findings to developers via AI interfaces.
• Developer Training & Awareness: Use Semgrep findings through AI assistants to educate developers about secure coding practices and common vulnerabilities within their codebases.

How to set it up

Windsurf

1. Ensure you have Node.js and the required dependencies installed.
2. Open your Windsurf configuration file.
3. Add the Semgrep MCP Server in the mcpServers section:

   {
     "mcpServers": {
       "semgrep-mcp": {
         "command": "npx",
         "args": ["@semgrep/mcp-server@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking that the Semgrep MCP Server is available as a tool.

Claude

1. Confirm prerequisites such as Node.js are installed.
2. Locate and open the Claude configuration file.
3. Add the Semgrep MCP Server to the MCP servers configuration:

   {
     "mcpServers": {
       "semgrep-mcp": {
         "command": "npx",
         "args": ["@semgrep/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Ensure the server is running and accessible.

Cursor

1. Install system dependencies (e.g., Node.js).
2. Navigate to the Cursor configuration file.
3. Insert the following snippet:

   {
     "mcpServers": {
       "semgrep-mcp": {
         "command": "npx",
         "args": ["@semgrep/mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Check for Semgrep MCP server availability.

Cline

1. Install Node.js and any other prerequisites.
2. Open your Cline configuration file for editing.
3. Add the Semgrep MCP server:

   {
     "mcpServers": {
       "semgrep-mcp": {
         "command": "npx",
         "args": ["@semgrep/mcp-server@latest"]
       }
     }
   }
   

4. Save the file and restart Cline.
5. Confirm that the server is recognized by your client.

Securing API Keys

To secure sensitive API keys, use environment variables in your configuration:

{
  "mcpServers": {
    "semgrep-mcp": {
      "command": "npx",
      "args": ["@semgrep/mcp-server@latest"],
      "env": {
        "SEMGREP_API_KEY": "${SEMGREP_API_KEY}"
      },
      "inputs": {
        "apiKey": "${SEMGREP_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "semgrep-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “semgrep-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Additional Capabilities

Based on the information above, the Semgrep MCP server provides a clear overview and setup instructions, but lacks public documentation of prompts, resources, or tools in the repository. Given these gaps, the MCP server scores moderately on usability and feature completeness for AI/LLM integration.

MCP Score