Codebase FAQs

Question 1

How does Codebase ensure high performance and scalability?

Accepted Answer

Codebase utilizes PostgreSQL with the pgvector extension for efficient semantic search, tree-sitter for rapid code parsing and chunking, and an async Python architecture. It boasts 60-second indexing for 10,000 files and 500ms p95 search latency, along with comprehensive error handling and structured logging.

Question 2

What is Codebase?

Accepted Answer

Codebase is a production-grade MCP (Model Context Protocol) server designed to index code repositories into PostgreSQL with pgvector. It provides semantic search capabilities tailored specifically for AI coding assistants and includes development task management.

Question 3

What are the core functionalities of Codebase?

Accepted Answer

Codebase offers semantic code search with natural language queries, fast repository indexing using tree-sitter for AST parsing, and robust development task management integrated with Git. It's built for production-grade performance and reliability.

Question 4

What kind of AI tools can integrate with Codebase?

Accepted Answer

Codebase is designed for AI coding assistants that support the MCP (Model Context Protocol), such as Claude Desktop. It provides six focused tools via Server-Sent Events (SSE) and stdio (JSON-RPC) for seamless AI-driven development workflows.

Question 5

What technologies power Codebase?

Accepted Answer

Codebase is built with Python 3.11+, PostgreSQL 14+ with pgvector, Ollama for generating 768-dimensional embeddings (nomic-embed-text model), SQLAlchemy 2.0 (async ORM), and Pydantic V2 for data validation, all within the modern FastMCP framework.

Codebase

Codebase

Key Features

Use Cases

Key Features

Use Cases