code-outline-graph 0.2.10

Symbol-level code indexer MCP server — token-efficient AI editing with confirm-before-read flow

code-outline-graph

Symbol-level code indexer and MCP server. Parses your codebase with tree-sitter, stores symbols in SQLite + vector DB, and exposes a confirm-before-read protocol so AI assistants read only the symbols they need — not whole files.

10x–50x fewer tokens compared to reading files directly.

Install

pip install code-outline-graph

Quick Start

# Index your project (writes .mcp.json automatically)
cd your-project
code-outline-graph build .

# MCP server auto-configures via .mcp.json
# Supported clients: Claude Code, Cursor, Codex, any MCP-compatible client

CLI Commands

Command	Description
code-outline-graph build [path]	Index project + write MCP configs for all clients
code-outline-graph update [path]	Reindex changed files only
code-outline-graph search <query>	Search symbols by keyword
code-outline-graph outline <file>	List all symbols in a file
code-outline-graph status [path]	Show index stats
code-outline-graph serve [path]	Start MCP server (stdio)
code-outline-graph install-skill	Install Claude Code skill to ~/.claude/skills/

MCP Tools

The server exposes 9 tools to AI assistants:

Tool	Description
resolve_edit_target	NL description → top-5 symbol candidates (signatures only, no body)
read_symbol_body	Read source lines for one symbol only
list_outline	All symbols in a file with line ranges
get_outline_summary	Compressed signatures-only outline
get_file_header	Imports + top-level constants only
get_symbol	Exact symbol metadata by name
find_by_keyword	Keyword search across all symbol names
get_line_range	Read arbitrary line slice from a file
index_project	Index a directory and start file watcher

Confirm-Before-Read Protocol

1. resolve_edit_target({"description": "user login handler"})
   → [{name: "login", file: "views/auth.py", start: 45, end: 89, signature: "def login(...)"}]

2. AI picks correct candidate from signatures (no body read yet)

3. read_symbol_body({"name": "login", "file": "views/auth.py"})
   → 44 lines instead of 300-line file

Supported Languages

Python, JavaScript, TypeScript, TSX, Go, Rust, Java, C, C++, C#, Ruby, PHP, Swift, Kotlin, JSON, YAML, TOML, INI

Architecture

cli.py          CLI entry point — build/update/search/outline/status/serve
server.py       MCP server — 9 tools, file watcher lifecycle
indexer.py      Orchestrates parse → checksum → DB upsert → embeddings
parser.py       tree-sitter parsing → Symbol extraction per language
db.py           SQLite + sqlite-vec — symbols table + FTS5 + vector index
search.py       FTS search, keyword search, vector search, resolve_edit_target
watcher.py      watchdog file watcher — debounced reindex + git HEAD tracking
embeddings.py   fastembed vector embeddings for semantic search
paths.py        Per-project DB path resolution (~/.cache/code-outline-graph/)

Each project gets its own SQLite DB at ~/.cache/code-outline-graph/<hash>/vectors.db. The watcher reindexes files on save and reindexes the whole project on git branch switches.

MCP Configuration

build auto-configures all supported clients in one shot:

Client	MCP config	SessionStart hook
Claude Code / Cursor	.mcp.json	.claude/settings.json
Codex CLI	.codex/config.toml	.codex/hooks.json
Gemini CLI	.gemini/settings.json	.gemini/settings.json

It also appends usage instructions (sentinel-bounded, safe to re-run) to AGENTS.md and GEMINI.md so clients that read those files know to use the MCP tools.

The SessionStart hook runs code-outline-graph update . at the start of every AI session, keeping the index fresh without manual intervention.

Claude Code Skill

build automatically installs the Claude Code skill to ~/.claude/skills/code-outline-graph/ (SKILL.md + examples.md). The skill teaches Claude the confirm-before-read protocol and tool reference.

To install manually or update after upgrading:

code-outline-graph install-skill

Development

pip install -e ".[dev]"
pytest                        # run all tests
pytest tests/test_parser.py   # run single test file

License

MIT