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PopNx MCPMCP servers for people who use AI · explained in plain words

Rangefind

by xjodoinversion 0.3.2i★ –iRuns on your computer
Registry nameio.github.xjodoin/rangefind

Install

Overview

Description from the maker (original English)

Search rangefind static indexes: full-text, geo, autocomplete, facets - no server, just files.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "rangefind": {
      "command": "npx",
      "args": [
        "-y",
        "rangefind-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add rangefind -- npx -y rangefind-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "rangefind": {
      "command": "npx",
      "args": [
        "-y",
        "rangefind-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "rangefind": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "rangefind-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account xjodoin, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7271 servers in this category
  • Published to the registry more recently than 22% of the other 7271 servers in this category
  • Basic checks equal to or better than 100% of the other 7271 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Rangefind safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.2) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday
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Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Rangefind

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.

What Is Implemented

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.

  • JSONL document input.
  • Static-site crawler: rangefind build ./dist indexes built HTML directly, with data-rangefind-* attributes for body scoping, metadata, facets, and sorts.
  • Schema-driven weighted fields.
  • BM25F-style field scoring.
  • Phrase signals for title/heading fields.
  • Adaptive posting segments.
  • Independently compressed posting segments packed into terms/packs/*.bin.gz.
  • Single ESM browser runtime bundle at dist/runtime.browser.js.
  • Node runtime (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.
  • Mobile runtime (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).
  • Lazy paged binary range directories at terms/directory-root. .bin.gz and terms/directory-pages/*.bin.gz.
  • ZFS-inspired object pointers with SHA-256 verification before decompression.
  • Content-addressed immutable pack and directory filenames for CDN caching.
  • Exact compressed-object deduplication during index construction.
  • Locality-ordered doc packs with a tiny doc-id ordinal table and dense pointer records for low-transfer result fetching.
  • Dense binary doc-page payload packs for browse, filter, and sort result pages where original document ids are clustered.
  • Range-packed result payloads with capped display fields.
  • Range-addressable posting-block sidecar for high-df terms.
  • Optional authority sidecar for exact title/entity/alias rescue without changing the main inverted index; a single title authority also supplies bounded prefix autocomplete without a second large build structure.
  • Range-packed binary facet dictionaries for high-cardinality metadata.
  • File-backed build pipeline with immutable posting segments, selected-term spools, raw/compressed document spools, heap-based segment merge, and sampled build telemetry.
  • Minimal runtime manifest with lazy full-manifest and telemetry sidecars.
  • Browser runtime with adaptive HTTP Range coalescing and bounded overfetch.
  • Main-index typo correction that uses bounded vocabulary shard probes and executes only the strongest corrected searches when first-page results are empty or weak.
  • Multi-value keyword facets with lazy dictionary loading.
  • Range-addressed typed numeric, date, and boolean doc-values for filters and sorting.
  • Lazy binary sorted doc-value trees for range-pruned top-k sort and doc-order early-stop browsing.
  • Lucene-style geo point fields with a range-addressed static KD tree: bounding-box and radius filters, exact nearest-neighbor distance sort with early-stop proofs (with or without a text query), text-plus-geo filtering, per-cell filter summaries, and distance boosts.
  • Optional compact geo result capsules and multi-resolution facet-to-cell indexes for one-range map results and category-aware spatial routing, plus a wildcard occupancy lane for arbitrary names and brands.
  • Generic route-corridor search over encoded polylines, coordinate arrays, or GeoJSON: exact point-to-route filtering, multi-resolution corridor cells, progress/direction/viewport ranking, and a rejoin point for every result.
  • Static routing and itinerary planning (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.
  • Search-as-you-type autocomplete inside the range-addressed authority index: diacritic-folded prefix and mid-label token matching, popularity or custom weights, exact best-first top-k with per-shard weight proofs, and precomputed hot lists so a first keystroke costs one small fetch—without a second suggestion pack family or a corpus-sized heap map.
  • Hybrid semantic search from a range-addressed IVF vector index: int8 quantized embeddings, coarse-dimension candidate pages plus a fixed-width full-dimension refine store, cosine top-k, and reciprocal-rank fusion with the text lane — no server, no vector database.
  • Analyzer-consistent snippets and highlight ranges on search results.
  • Multilingual analysis (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.
  • Incremental publishing: 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.
  • Per-query facet counts with exact-or-flagged semantics: dictionary-backed global counts, exact counts over budgeted match sets, and bounded chunk-sampled estimates for very large result sets.
  • OpenStreetMap place and address fixture with canonical zero-posting address lookup, compact 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.
  • OSM typed and natural-language constraints for accessibility, services, payment, seating, reservations, fees, and open-now; conservative client-side opening_hours evaluation; route-aware place search; compact place details; and simplified encoded area geometry for real polygon/line map rendering.
  • Geographic index sharding for planet-scale corpora: independently built and updated per-region shards sharing one frozen scoring-stats artifact (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.
  • Link-graph authority (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.
  • Tiny runnable example.

Why This Exists

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:

  • no backend service,
  • no hosted search provider,
  • static hosting compatibility,
  • measurable retrieval quality,
  • low request count despite many logical shards,
  • and an index format designed around top-k retrieval.

Quick Start

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/.

Documentation

Start with the guide that matches the job:

GuideUse 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.

Crawl A Static Site

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

Only the beginning is shown; read the rest in the repo

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