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PopNx MCPServidores MCP para quienes usan IA · explicados con palabras sencillas

Sidecrew

de lvlrSajjadversión 0.2.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.lvlrSajjad/sidecrew

Instalar

Resumen

Descripción del creador (original en inglés)

Code changes and unit tests by local MLX models, behind a gate a machine can run.

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: Otros

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

Ejecuta esto en tu terminal

claude mcp add sidecrew -- npx -y sidecrew mcp

Abrir en Cursor

O copia la configuración tú

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

{
  "mcpServers": {
    "sidecrew": {
      "command": "npx",
      "args": [
        "-y",
        "sidecrew",
        "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": {
    "sidecrew": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "sidecrew",
        "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 lvlrSajjad, 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 Otrosi

  • Más estrellas que el 0% de los otros 32558 servidores de esta categoría
  • Publicado en el registro más recientemente que el 48% de los otros 32558 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 32558 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 Sidecrew?

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 (0.2.0) llegó al registro hace 43 días.

¿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: Spec Driven Development, Remote Desktop Commander, FactLot

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

Spec Driven Development

de formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Se ejecuta en tu computadora★ 440MITActualizado hace 14 meses
Instalar

Remote Desktop Commander

de desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Se ejecuta en la nube★ 211Actualizado ayer
Instalar

FactLot

de 9mp96ygnbk-alt

Cited CRE facts and deterministic calculators for agents: rent, CAM, debt sizing, IRR, parcel IDs.

Computadora o nubeActualizado hoy
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.

sidecrew

You ask for a change to your source code, and you get it — made on your Mac, by models that cost you no Claude tokens, and only after a machine has checked it.

Claude Opus plans the work and reviews what came back. Small models running locally (MLX, no network) do the narrow parts. Between them sits a gate a machine can run — for a code change: the diff stayed where it was asked to, tsc is clean, and every test that passed before still passes. Claude never sees anything that did not survive it.

$ sidecrew fix change_plan.json

fix 2026-09-20T09-14-02Z-rename: 1 step, 12 tasks, 13.0 GB free ÷ 6.5 GB per slot = 2; 1 worker up step 1/1 "rename the helper": capturing the baseline rename-01: survived (src/util/asset.util.ts) rename-02: survived (src/util/asset.util.ts, src/util/asset.util.spec.ts) … multi-06: failed at compile · escalating — renamed the symbol 7 times over, 5 compile errors 11/12 survived · 0 → 0 tsc errors · .sidecrew/runs/2026-09-20T09-14-02Z-rename/result.json

*Illustrative shape, not a captured transcript* — it is the real output format with the counts from the React measurement below, and no client's file names. 11 of 12 with a 95 % interval of `[0.615, 0.998]` is the number; the line above is what it looks like while it runs.

Unit tests are workload #1 — the same shape with a mutation-kill gate instead — and the numbers for both are below, each with its interval.

sidecrew needs 24 GB of installed RAM. Below that a 7B cannot sit beside a normal working set (measured), so sidecrew refuses to run and tells you why rather than quietly becoming a different tool (ADR-0073). sidecrew doctor answers this before you plan anything.

And the other half of the ledger, measured 19–20 Sep 2026: the workers are free, the *coordination* is not. Opus planning costs 8,400–22,100 tokens per task, depending almost entirely on how many tasks are in the plan — see *What this costs to run* below. A page that says "the work is free" without saying that is telling you half of it.

Before any number below: how tightly to read it

Every rate on this page is an interval, never a point, and the reason is measured. Replaying a frontier model's own diffs — which the gate finds no defect in — through the gate a second time, it disagreed with itself on 2 of 19 candidates: D = 0.105, 95 % interval [0.013, 0.331] (experiments/gate-error-rate/, against a rule frozen before the first replay). That rule's own verdict at D > 0.10 is UNACCEPTABLE, and its consequence is this paragraph: no survival rate here is published as a bare fraction, D travels beside each of them, and no two of our rates are compared without asking whether their difference survives it.

What that does and does not damage. D counts the gate refusing changes that were fine. Nothing in it is evidence of the gate *admitting* something bad. So:

*The gate admits nothing that fails* — stands.

*The gate refuses only things that fail* — **measured false, at roughly one evaluation in ten, and

unproven at any tighter bound.**

Two of the three known causes are now recorded on every verdict — memory pressure (ADR-0066) and a baseline captured on a different calendar day (ADR-0069). The third is undiagnosed, and both measured disagreements happened on a quiet machine, so it is neither of the first two. D was measured on workload #2a's gate; it says nothing about workload #1's mutation gate, which is a different oracle (§4.4 of the rule).

Workload #2a — behaviour-preserving changes, measured

Against a decision rule frozen before the tool had produced a single verdict (experiments/go-no-go-2a/results/REPORT.md). S is survival, A is a blind reviewer's approval of the diff. Every cell is k/n with its exact 95 % interval; D = 0.105 [0.013, 0.331] applies to every S.

inputS, local 7BS, Haiku controlA, local 7BA, control
fixture3/3 [0.292, 1.000]3/3 [0.292, 1.000]1/3 `[0.008, 0.906]`3/3 [0.292, 1.000]
a commercial Nest/jest codebase12/12 [0.735, 1.000]12/12 [0.735, 1.000]9/10 [0.555, 0.997]10/10 [0.692, 1.000]
a commercial React/jest codebase11/12 [0.615, 0.998]12/12 [0.735, 1.000]9/10 [0.555, 0.997]10/10 [0.692, 1.000]

Read the intervals, not the fractions: at these sample sizes 12/12 and 11/12 are not distinguishable, and neither is the 7B from the control on A. Both real projects passed the frozen rule by a margin of exactly zero, on a sample of ten. The survival rate decided nothing — five of six measurable cells sit at or above 0.917, so the rule's ratio clauses were inert, and the only thing that separated a local 7B from a network model was the approval rate.

What that approval rate is about, measured: the local model makes unrequested cosmetic edits — a deleted docblock, a reworded comment, a stray blank line — in 4 of 23 `[0.050, 0.388]` sampled survivors, against the control's 0 of 23 `[0.000, 0.148]`. confined ∧ compiles ∧ tests pass cannot see any of it, which is the point.

What you gain over just asking Opus

Measured 18–19 Sep 2026 against the thing a colleague actually does today — asking a frontier model directly, one task at a time, with no plan and no gate. Same 19 hand-written tasks, same two codebases, four arms.

Opus aloneSonnet alonesidecrew
tokens, 19 tasks, Nest codebase80,13188,8200
tokens, 19 tasks, React codebase95,33588,6800
output vs Opus, Nest codebase—identical 19/19identical on 18 of 19
changes delivered and verified, Nest19 (unverified)19 (unverified)19/19 `[0.824, 1.000]` survived
changes delivered and verified, React19 (unverified)19 (unverified)15/19 `[0.544, 0.939]` survived; 4 escalated

The token columns are counts and are exact. The survival rows are rates, so they carry their intervals, and D = 0.105 [0.013, 0.331] applies to both of them.

A fourth arm ran the same gate over Opus's own diffs, to separate the gate's contribution from the worker's: 19/19 `[0.824, 1.000]` on the Nest codebase and 18/19 `[0.740, 0.999]` on the React one. Two things follow, and the second is the more useful.

The gate found nothing wrong with a frontier model's work. Its single rejection there is a tsc fragility it rejects from *both* arms — so on these tasks the gate adds no safety on top of Opus. Its entire value is that it makes the free worker usable, not that it second-guesses the expensive one.

And it is what separates a worker defect from a project defect. Of the four React failures, three vanish when Opus writes the diff — genuine worker defects — and one reproduces exactly, because the 7B's output for that task was *byte-identical* to Opus's. Worker-attributable survival is therefore 15/18 `[0.586, 0.964]`, not 15/19. Without that control the small model would have been blamed for a third more failures than it caused — and note that the two intervals overlap almost entirely, so the correction changes the *attribution* rather than the number.

Three things that buys you, and one it costs.

  1. The work is free. Not cheaper — zero worker tokens. Opus plans and reviews survivors; the editing happens on your Mac.
  2. On work a compiler and a suite can check, you give up nothing for it. Eighteen of nineteen diffs were *byte-identical* to what Opus produced. Not "comparable quality" — the same file.
  3. When it is wrong, you get told, loudly, and nothing is applied. The four React failures were a symbol renamed seven times over (5 compile errors), a rename that also hit a type, a module path and a public property key (122 compile errors), a change that renamed the file it was given, and one that a frontier model produced identically. Each printed as escalated, each with the exact errors, none merged. Asking Opus directly gives you a diff and the sentence *"nothing else changed"* — accurate here, and unverified.
  4. It costs wall-clock. ~25 s per task interactively with Opus; 216–271 s with sidecrew (15 s to generate, 200–255 s for the project's own suite and tsc). It is free and unattended, not fast.

What is not claimed. The frozen rule's verdict is WITHHELD: every task in these runs is a rename, and the rule refuses a verdict until at least half are null guards, API migrations or dead-code removal. The measurement vindicated that clause rather than surviving it — two frontier models and a 7B emitted *identical bytes* on 18 of 19 Nest tasks, so that task set cannot rank anything. experiments/status-quo/ has the arms, the protocol frozen before they ran, and the defects.

Requirements
OSmacOS on Apple silicon. The local worker is MLX, which is Apple's. Not a port away — a different inference stack.
RAM24 GB installed or more for the local tier and everything this page measures. The threshold is on *installed* RAM, never free, so a machine that can host a worker never falls back silently.
Node≥ 20. A project whose own engines demands newer is honoured — run sidecrew under the project's Node (ADR-0049 cost a night to learn).
Pythonmlx-lm, installed by you: pip install mlx-lm. Deliberately not bundled — external capabilities are shelled out and reported by doctor, never vendored.
Disk~4 GB for the 7B (downloaded on first sidecrew serve, pinned revision), plus ~500 MB per verification sandbox while a run is in flight.
The projectTypeScript with a green-ish tsc and a test suite that runs. Workload #1 also does Swift/XCTest.

Under 24 GB: sidecrew still runs, on the api tier, with Haiku as the worker billed to your key — and none of the numbers on this page describe that tier, because they have not been measured (Phase 13 §5). Supported, and unpriced. If that matters to you, 24 GB is the line.

Windows and Linux: no. The TypeScript verifier would port, but the worker would not, and memory and pressure detection are sysctl/vm_stat. sidecrew doctor tells you rather than failing oddly.

What to expect on your machine

The check is on installed RAM, never free RAM — free RAM swings by 8 GB when you open Xcode, and a tool that changes what it is based on that is a tool you cannot reason about. The floor is 24 GB and below it sidecrew refuses (ADR-0073).

installedtierworkergates in flightwhat that means for you
under 24 GB—none: sidecrew refuses—A 7B cannot sit beside a normal working set here (measured), and trying anyway means swapping — which makes the gate reject changes that were fine (ADR-0066). You are told your RAM, the floor and the reason. There is an unsupported escape hatch (SIDECREW_TIER=api, Haiku over the network, billed to your key, never measured); it is not what any number on this page describes.
24–32 GBlocal7B on your Mac1Everything on this page was measured here. Worker tokens are zero. One task at a time: ~4 min each, so a 19-task plan is ~70 min, unattended. Memory is the binding constraint — see the false-negative note below.
48–64 GBlocal7B, or a 14B comfortably~3 *(estimated)*The gate stops competing with the model for memory. The same 19-task plan should land nearer ~25 min *(estimated)*, and ADR-0066's whole false-negative class — a starved gate failing closed — stops applying. You can keep using the machine.

Measured vs estimated, because it matters here. The 24–32 GB row is measured: 15 s to generate, 200–255 s of the project's own tsc and suite, per task, on a 32 GB M2 Pro. The 48–64 GB row is an estimate derived from two

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

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