Enables LLMs to perform high-performance code search and analysis across multiple languages using symbol indexing, regex text search, and structural AST pattern matching. It also provides tools for technology stack detection and dependency analysis with persistent caching for optimized performance.
Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.
A Python-based local indexing server that creates semantic search capabilities for codebases using ChromaDB, allowing Cursor IDE to perform vector searches on your code without sending data to external services.
Enables AI agents to write and execute Python code in an isolated sandbox that can orchestrate multiple MCP tool calls, reducing context window bloat and improving efficiency for complex workflows.
High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.