Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Repository intelligence, code graph, history, papertrail, and cross-agent memory for coding agents.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"rag-rat": {
"command": "npx",
"args": [
"-y",
"@rag-rat/bin",
"mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add rag-rat -- npx -y @rag-rat/bin mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"rag-rat": {
"command": "npx",
"args": [
"-y",
"@rag-rat/bin",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"rag-rat": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@rag-rat/bin",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 cq27-dev 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.24.1)은 22일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
What a repository knows about itself. rag-rat is a local repo-intelligence index and MCP server for coding agents. It keeps source files read-only, writes only its own SQLite database, and answers with provenance on every result — current source, the code graph, git/GitHub history, and durable, source-anchored repo memories that persist across sessions and agents.
Explore the live VS Code Lens demo — no installation required. It surfaces clone classes, repo memories, and issue/decision context alongside the code; press Ctrl+Alt+R to reveal clone overlays.
Every coding harness already has grep and file reads. rag-rat adds the layer they do not provide: source-anchored *rationale*. It connects the code an agent is about to touch to its callers, callees, tests, git/GitHub history, prior decisions, invariants, risks, and duplicate-code signals — and labels every result with confidence and coverage, so an agent can judge it instead of trusting it.
sequenceDiagram
participant Repo as Repository participant Engine as rag-rat engine participant Agent as Coding agent
Repo->>Engine: Source · git/GitHub · repo memories Engine->>Engine: Index → graph → (opt) SCIP oracle → reconcile Agent->>Engine: where / why / who-calls / impact? Engine-->>Agent: source + call paths + papertrail + memories (with provenance) Agent->>Engine: record a finding Engine->>Repo: persist a source-anchored repo memory
Invariant, Decision, Risk, …) that survive refactors and surface automatically during future queries — the signal grep can't give you. They are *not* assistant memory: they are versioned, local, source-anchored facts about this repository that any future agent retrieves with evidence.Compiler confidence and ranks the load-bearing symbols.For Claude Code, Codex, and opencode, install the plugin. It registers the MCP server, adds the hooks, and fetches a version-matched rag-rat binary on first run, which the MCP server exposes as ~/.local/bin/rag-rat when it starts — see Running the CLI (the Claude Code and Codex bundles also add the skills; on opencode add them with npx @rag-rat/skills):
# Claude Code
claude plugin marketplace add cq27-dev/rag-rat claude plugin install rag-rat@rag-rat
codex plugin marketplace add cq27-dev/rag-rat codex plugin add rag-rat@rag-rat
opencode plugin @rag-rat/plugin-opencode
After installing, approve the plugin so its tools and hooks run (opencode loads plugins without an approval step — nothing to do there):
~/.claude/settings.json with "permissions": { "allow": ["mcp__plugin_rag-rat_rag-rat__*"] } (plugin tools are named mcp__plugin_rag-rat_rag-rat__ ; a server added by hand with claude mcp add uses mcp__rag-rat__* instead).codex session started *inside the repo* (the plugin ships grep-augmentation, clone-check, and session-digest hooks that run outside the sandbox). Choose "Trust all and continue" to enable them. For unattended commands such as codex review, also allow the plugin's MCP tools in ~/.codex/config.toml so the run cannot stall on a per-tool approval prompt: [plugins."rag-rat@rag-rat".mcp_servers.rag-rat]
default_tools_approval_mode = "approve"
This trusts every current and future MCP tool exposed by the installed rag-rat plugin. Only enable it when you trust the plugin's source and installation origin, then restart Codex.
Then open the repository and ask:
Set up rag-rat in this repo.
The init-rag-rat skill scans the repo, explains the material choices, previews rag-rat.toml, writes and indexes only after confirmation, and offers to set up the git hooks that keep the index fresh. The MCP server starts dormant in an unconfigured repo; when setup finishes, reconnect it so it restarts fully active against the new index — in Claude Code run /mcp and reconnect rag-rat; in Codex and opencode, start a new session.
Then put it to work — the loop rag-rat is built for is in Try it.
Manual installation and other agents
Use this path for the standalone CLI, agents without plugin support, or building from source.
The prebuilt package needs no Rust toolchain and supports Apple Silicon macOS, glibc ≥2.38 Linux (x86-64 and arm64), Windows x64, and Android/Termux arm64:
npm install -g @rag-rat/bin
npx @rag-rat/bin --help
@rag-rat/bin fetches the full binary from the matching GitHub release. FastEmbed's ONNX Runtime is statically linked.
To build from source instead:
cargo install rag-rat
cargo install --path crates/rag-rat-cli --bin rag-rat
The default source build needs glibc ≥2.38 and is unavailable for Intel macOS and musl/Alpine. On those platforms, including Ubuntu 22.04, use the pure-Rust embedder:
cargo install rag-rat --no-default-features --features model2vec
--no-default-features alone produces a smaller hash-only build without real embeddings. SQLite is bundled; see Platform support for toolchain details.
cd /path/to/your/repo
rag-rat init
init is an interactive terminal wizard: it scans the repo, guides language and embedding choices, writes rag-rat.toml, and builds the initial index. For a non-interactive setup use rag-rat init --yes, which takes the defaults and installs the git maintenance hooks (add --no-hooks to skip them; --dry-run previews the config without writing). --yes never replaces an existing rag-rat.toml unless you add --force — rerun the wizard to reconfigure instead. Configuration reference: docs/config.md.
Install the skills for Claude Code, Codex, Cursor, and 70+ other detected agents:
npx @rag-rat/skills
That installs using-rag-rat, dream-review, init-rag-rat, and configure-rag-rat-dream. See skills/README.md for per-agent flags and update, list, and remove.
The MCP server uses STDIO: the client launches rag-rat mcp from the repository so it discovers the correct rag-rat.toml and repository scope in the consolidated machine-global store.
claude mcp add --scope project rag-rat -- rag-rat mcp
codex mcp add rag-rat -- rag-rat mcp
Or add the equivalent project configuration:
{
"mcpServers": { "rag-rat": { "command": "rag-rat", "args": ["mcp"] } } }
rag-rat init prints the registration command but does not register the server itself. Pass rag-rat mcp --json if the client must parse JSON; tool text defaults to TOON. Full tool schemas: docs/mcp-tools.md.
Claude Code tool permissions
Claude Code asks once before each rag-rat MCP tool first runs. Choose "Yes, don't ask again," or allow the tool namespace in ~/.claude/settings.json:
{ "permissions": { "allow": ["mcp__rag-rat__*"] } }
Do not pin a global server to one repository's config. A user-scoped server with
--config /some/repo/rag-rat.tomlserves that repository everywhere. Register MCP per project and
let the process discover the config from its working directory.
Once the repo is indexed, the code graph, symbols, git history, semantic search, and clone detection are ready — these answer on the first query. Repo memories start empty: they accrue as agents record findings with memory_create and then surface automatically in later answers. (Tracker issue/PR rationale needs a rag-rat papertrail sync.)
Ask your MCP client:
impact_surface on the function I'm about to edit — its callers, callees, tests, and recent commits."semantic_search over source and docs.important_symbols.find_clones (and the write-time hook warns as you write it).parse_config: reload must not allocate after the scheduler starts." — memory_create writes your first repo memory; it then rides along in future impact_surface / symbol_lookup results.Or from the CLI:
rag-rat query "where is config reload handled?"
rag-rat important-symbols --limit 20 rag-rat brief --mode spine rag-rat clusters --limit 10 rag-rat tools impact-surface --symbol parse_config
The point isn't the tool catalog — it's the loop an agent runs *around* an edit, so it changes code with the callers, tests, rationale, and prior art in front of it instead of guessing:
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요