SearchCode (by Black Duck)

Overview

SearchCode, developed by Black Duck, provides code intelligence specifically designed for Large Language Models (LLMs). It enables the analysis, search, and retrieval of code from public Git repositories, aiming to reduce token usage and improve efficiency for AI agents.

Key Features

• Code Analysis — provides language breakdowns, complexity metrics, tech stack detection, code quality, and security scanning for repositories.
• Code Search — offers full-text code search capabilities with boolean queries, regex, and structural filters for declarations, usages, strings, and comments.
• File Retrieval — allows retrieval of full files, specific line ranges, or symbols by name, including adjacent declarations for context.
• Batch File Retrieval — facilitates the retrieval of up to 10 files in a single call for cross-file analysis.
• File Tree Exploration — generates directory structures with language, path, and depth filters, alongside a fuzzy file finder.
• Findings Drildown — enables filtering of code quality and security findings by severity and category.

Who It's For

SearchCode is primarily designed for developers and AI engineers working with LLMs, particularly those who need to integrate code intelligence into AI agents like Claude Code, Cursor, Codex, and Copilot. It also serves human users who require rapid understanding, evaluation, and debugging of public codebases without extensive setup.

Notable Strengths

The platform's primary strength lies in its ability to significantly reduce token consumption for LLMs when analyzing code, claiming a 99% token reduction. Its integration as an MCP (Model Control Protocol) server allows for direct use by various LLMs and AI assistants. The provision of structured, pre-computed data through specialized tools enables LLMs to focus on reasoning rather than parsing raw code.