searchcode.com — **Code intelligence for LLMs**

Read any GitHub repo. Without reading any GitHub repo.

Paste a URL for an instant code intelligence dossier—languages, complexity, tech stack, code quality. Or add the MCP server so Claude Code, Cursor, Codex, and Copilot can do the same from inside your editor.

Free during beta. No signup. No API key.

## Capabilities

Six specialized tools. Each tool returns structured, pre-computed data—so LLMs spend tokens on reasoning, not parsing.

- **code_analyze**: Language breakdowns, complexity metrics, tech stack detection, code quality, and security scanning. Scope to a subdirectory for large monorepos.

- **code_search**: Full-text code search with boolean queries, regex, and structural filters—declarations, usages, strings, comments.

- **code_get_file**: Retrieve full files, line ranges, or specific symbols by name. Include adjacent declarations for context in a single call.

- **code_get_files**: Batch retrieve up to 10 files in a single call—perfect for cross-file analysis.

- **code_file_tree**: Directory structure with language, path, and depth filters, plus optional file stats. Fuzzy file finder to locate files by partial name.

- **code_get_findings**: Drill into code quality and security findings filtered by severity and category.

## Built for LLMs. Works for humans too.

Understand a new codebase in one call. Skip cloning, skip setup. Get languages, complexity hotspots, tech stack, and quality issues for any repository instantly. The full picture in seconds, not hours of poking around.

Give AI agents structured code context. Purpose-built MCP tools that feed LLMs code intelligence in structured format. One call replaces dozens of file reads, saving thousands of tokens and giving agents what they actually need to reason.

Under the hood: scc for code counting & complexity analysis, and cs for code search with smart ranking—two battle-tested open-source tools by the same author. Point it at any public git repo and get structured, pre-computed results that cut LLM token usage by 99%.