Prototipo · Versión preliminar: todos los servidores del registro oficial, sincronizados cada 30 minutos. Las reseñas y las cuentas llegarán en una etapa posterior.
PopNx MCPServidores MCP para quienes usan IA · explicados con palabras sencillas

Rangefind

de xjodoinversión 0.3.2i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.xjodoin/rangefind

Instalar

Resumen

Descripción del creador (original en inglés)

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

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Búsqueda y memoria de IA

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • npmNode.js instalado (para el comando npx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

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

Ejecuta esto en tu terminal

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

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub xjodoin, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Búsqueda y memoria de IAi

  • Más estrellas que el 0% de los otros 7254 servidores de esta categoría
  • Publicado en el registro más recientemente que el 22% de los otros 7254 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 7254 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Rangefind?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.3.2) llegó al registro hace 4 meses.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: Context7, Codebase Memory, Exa

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Context7

de upstash

Up-to-date docs for AI agents

Computadora o nube★ 64.224MITActualizado hoy
Instalar

Codebase Memory

de DeusData

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

Se ejecuta en tu computadora★ 48.148MITActualizado hace 20 días
Instalar

Exa

de exa-labs

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

Se ejecuta en la nube★ 5213MITActualizado el mes pasado
Instalar

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

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

Solo se muestra el principio; lee el resto en el repo

Pregunta a PopNx