Context7
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제작자가 쓴 설명(영어 원문)
Search rangefind static indexes: full-text, geo, autocomplete, facets - no server, just files.
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": {
"rangefind": {
"command": "npx",
"args": [
"-y",
"rangefind-mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add rangefind -- npx -y rangefind-mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"rangefind": {
"command": "npx",
"args": [
"-y",
"rangefind-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": {
"rangefind": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"rangefind-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 7일 점검
레지스트리 이름이 GitHub 계정 xjodoin 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(0.3.2)은 4개월 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
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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.
Rangefind is a static search engine for large sites that cannot or should not run a search server.
It builds a sparse inverted index into static files, then lets the browser query that index with HTTP Range requests. The core idea is simple: keep the logical search shards small, but pack them into a small number of static files so deploys and cold starts stay practical.
This repository is the standalone extraction of a production prototype built for a large thesis corpus.
The list below is the compact overview. The [complete feature guide](docs/features.md) explains every supported search lane, build mode, runtime, integration, feature combination, fallback, and deliberate boundary with runnable examples.
rangefind build ./dist indexes built HTML directly, with data-rangefind-* attributes for body scoping, metadata, facets, and sorts.terms/packs/*.bin.gz.dist/runtime.browser.js.rangefind/node) for MCP servers, CLIs, and SSR: same query engine over local index directories (positional file reads) or remote HTTP(S) indexes, with browser-equivalent caching — immutable content-addressed objects cached on disk and in a memory LRU, manifests revalidated with ETags.rangefind/mobile) for React Native/Hermes and other embedded JS hosts: fully offline search over a bundled or downloaded index via positional file reads, or remote HTTP(S) indexes with memory + persistent caching — see docs/mobile.md (covers WebView, Flutter, and native apps too).terms/directory-root. .bin.gz and terms/directory-pages/*.bin.gz.Range coalescing and bounded overfetch.rangefind/route): a CRP/MLD route graph built from OSM road networks into range-addressed cell and overlay objects — point-to-point driving routes with geometry and named steps, travel-time matrices, and multi-stop itinerary optimization, all exact versus a full-graph Dijkstra and served from static files with a bounded fetch set per query; sharded builds share one top overlay and route identically to monolithic ones — see docs/route-graph.md.analysis config): per-document language detection or an explicit language field, per-language light stemmers and stopwords for 20+ languages, script-aware Unicode folding (Latin, Greek, Cyrillic, Arabic, Hebrew, Devanagari), dictionary-free CJK bigram tokenization that is deterministic across Node and every browser, cross-language query expansion with index-evidence base-plan selection, and the whole profile frozen into the manifest so query analysis always matches the index.rangefind build --update adds small delta generations over an existing index — unchanged pack bytes keep their content-addressed names (and CDN cache entries), replaced documents tombstone their old version, and the runtime merges every lane (text, sorted browse, geo, vector, hybrid, suggest, facets) across generations with scores that stay exactly comparable. rangefind build --compact folds generations back into a single index when they pile up.addr:interpolation ranges, structured forward and reverse geocoding, bounded multi-provider civic/postal augmentation (including the Québec RQA adapter), reusable rangefind/osm and rangefind/osm/node integration APIs, and exhaustive geo oracles.opening_hours evaluation; route-aware place search; compact place details; and simplified encoded area geometry for real polygon/line map rendering.scoringStats config + rangefind/scoring-stats), a tiny sharded root manifest (rangefind/shards), lazy shard engines with bbox geo routing and expanding nearest-first search, and merged lanes whose rankings match a monolithic build of the same corpus exactly — see docs/sharded-osm.md.linkRank): the static-site crawler resolves internal links into a document graph and folds a build-time PageRank prior into a sortable numeric doc-value. Well-linked pages win near-ties in ranking (an opt-out multiplicative boost, score *= 1 + boost*linkRank, applied as a bounded window rerank with no change to the scoring hot loop), you can sort by authority, and there is zero query-time graph traversal. Any corpus that knows its own edges can opt in with the turnkey examples/link-graph-enrich.mjs enricher (or the exported rangefind/link-graph helper); see docs/link-graph.md.Most browser search libraries assume the browser downloads a whole index, or they optimize for smaller sites. Rangefind is for the case where you want:
Demos: https://rangefind.dev/ — an OpenStreetMap map search backed by the rolling public sharded index (autocomplete, viewport geo queries, and nearest-neighbor) and the minimal example, both served as pure static files.
The already-generated planet-scale OSM index is free to query directly from browsers or Node—no account or API key required:
import { createSearch } from "rangefind";
const engine = await createSearch({ baseUrl: "https://osm.rangefind.dev/" });
See its live coverage and freshness at https://osm.rangefind.dev/. It is a best-effort public service; self-host the index when you need guaranteed capacity, version pinning, custom fields, or an operational SLA. OSM result UIs must display © OpenStreetMap contributors.
npm install
npm run build:example npm run test:smoke npm run serve:example
Open http://localhost:5178/.
Start with the guide that matches the job:
| Guide | Use it for |
|---|---|
| [Feature guide](docs/features.md) | Complete capability catalog, examples, supported combinations, compatibility, and boundaries. |
| [Reference](docs/reference.md) | Configuration properties, builder/runtime APIs, tuning knobs, UI component, and deployment requirements. |
| [Architecture](docs/architecture.md) | Static file layout, range directories, posting retrieval, geo, authority, and vector internals. |
| [Autocomplete guide](docs/autocomplete.md) | Search-as-you-type patterns, measured per-keystroke costs, hydrated previews, instant selection, and the anti-patterns. |
| [OSM example](examples/osm-geo/README.md) | Map search, autocomplete, geocoding, constraints, route corridors, geometry, extraction, and RQA. |
| [Replace Google Maps APIs](https://rangefind.dev/google-maps-api-alternative/) | Free public OSM index, migration adapter, API mapping, complete use cases, production checklist, attribution, and parity boundaries. |
| [Node](docs/node-runtime.md) / [mobile](docs/mobile.md) | Local, remote, cached, embedded, and offline runtimes. |
| [Sharded OSM](docs/sharded-osm.md) | Planet-scale regional builds, root routing, and exact score comparability. |
| [Route graph](docs/route-graph.md) | Static routing, travel-time matrices, and itinerary planning over range-addressed road-network cells and overlays. |
| [Production maps benchmark](docs/osm-maps-benchmark.md) | Google Maps-style workload coverage, quality assertions, and phone budgets. |
If you already have a built static site (from any generator), point Rangefind at the output directory and it indexes the HTML directly — no JSONL to author:
npx rangefind build ./dist
This crawls every .html/.htm file under ./dist, extracts the title, headings, and main body text, and writes the index to ./dist/rangefind (change it with --output). Result URLs default to site-root paths (--base-url sets a prefix or origin, e.g. --base-url https://example.com/). Deploy the site and
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요