Structured Repo Context FAQs

Question 1

What search capabilities does SRC offer?

Accepted Answer

SRC provides a powerful hybrid search combining vector similarity, BM25 keyword matching, and RRF fusion for optimal results. It also supports dedicated vector (semantic) and full-text search (keyword) modes to suit different query types.

Question 2

What programming languages does SRC support?

Accepted Answer

SRC offers full Abstract Syntax Tree (AST) support for 18 languages using Tree-sitter, ensuring deep structural understanding. Additionally, it provides intelligent text splitting for over 50 languages, offering broad compatibility for diverse projects.

Question 3

How does SRC enhance AI's understanding of my code?

Accepted Answer

SRC transforms raw code into AI-ready context by generating call graphs, resolving cross-file context (like imports and aliases), and providing semantically rich code chunks. This overcomes limitations of keyword search and small snippets, giving AI a holistic view.

Question 4

What is Structured Repo Context (SRC)?

Accepted Answer

Structured Repo Context (SRC) is an MCP server and CLI tool that indexes your codebase into semantic, searchable chunks using AST parsing, Tree-sitter, and embeddings. This makes your code truly comprehensible for AI assistants and LLMs.

Question 5

How does SRC integrate with AI assistants and LLMs?

Accepted Answer

SRC functions as an MCP (Model Context Protocol) server, allowing seamless integration with MCP-compatible AI assistants like Claude Desktop and Cursor. It provides these tools with on-demand, semantically rich codebase context to answer complex questions effectively.

Structured Repo Context

Structured Repo Context

소개

주요 기능

사용 사례

소개

주요 기능

사용 사례