A structure-aware chapter search MCP server.
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# chapter-mcp `chapter-mcp` gives coding agents a compact way to search a project by useful sections instead of opening whole files. It indexes a workspace locally, splits files into "chapters", and exposes MCP tools for searching, listing, and reading those chapters. The goal is simple: help an assistant find the right part of a repo before it burns context on raw file reads. It is intentionally boring in a good way: local SQLite FTS5, deterministic full-text search, no embeddings, no vector database, and no external service. ## What it is good for Use `chapter-mcp` when an assistant needs project context but does not yet know which file or section matters. Good fits: - project README sections - `AGENTS.md`, `CLAUDE.md`, instructions, and local notes - ADRs and design docs - Markdown/TXT documentation - source files where top-level classes or functions make useful chapters - focused reads after a search result points to the right line range Not a good fit: - exact code matching with punctuation, operators, routes, or config keys - symbol references, definitions, diagnostics, or safe edits - fuzzy semantic search across very different wording For those, keep using the sharper tool: `rg` for exact text, Serena or another language-aware tool for symbols, raw file reads for known ranges, and vector/RAG tooling for real semantic retrieval. ## How chapters work `chapter-mcp` turns files into smaller units: Markdown by headings, Python by top-level classes/functions, and other text files by paragraphs. Search results point to chapter names and line ranges. Reads can return a whole chapter or a limited slice of chapter content. By default, if no explicit config is provided, `chapter-mcp` discovers visible top-level project entries and indexes them. In a Git repo it uses tracked files, skips hidden top-level entries and `.chapter-mcp`, respects `.gitignore`, and applies `.aiignore` rules. That means the happy path is: add the MCP server to your assistant, star…
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