What problem does it solve?
Developers struggle to locate definitions, usages, and architectural relationships across large, multi-language codebases, leading to slow refactoring and limited context for AI assistance.
Core Features & Use Cases
- Semantic code indexing across Ruby, Java, Kotlin (full support) and partial support for Python, TypeScript, JavaScript.
- Go‑to‑definition and find‑usages powered by a graph database, enabling impact analysis before changes.
- Architecture visualization through generated graph queries and HTTP API.
- MCP integration that exposes tools such as list_projects, search_codebase_definitions, get_references, and repo_map for AI assistants.
Quick Start
Ask the assistant to index the repository with GKG and then retrieve the definition of a symbol using the get_definition tool.