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

Project Memory MCP

de kaaustubhversión 1.6.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.kaaustubh/project-memory-mcp

Instalar

Resumen

Descripción del creador (original en inglés)

Persistent memory for AI coding agents — past bugs, decisions, and your corrections, in your repo.

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": {
    "project-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@kaaustubh/project-memory-mcp"
      ],
      "env": {
        "PROJECT_MEMORY_ROOT": "your-value"
      }
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add project-memory --env PROJECT_MEMORY_ROOT=your-value -- npx -y @kaaustubh/project-memory-mcp

Completa los valores vacíos antes de ejecutarlo.

Abrir en Cursor

O copia la configuración tú

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

{
  "mcpServers": {
    "project-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@kaaustubh/project-memory-mcp"
      ],
      "env": {
        "PROJECT_MEMORY_ROOT": "your-value"
      }
    }
  }
}

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": {
    "project-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@kaaustubh/project-memory-mcp"
      ],
      "env": {
        "PROJECT_MEMORY_ROOT": "your-value"
      }
    }
  }
}

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.

Valores que debes completar

  • PROJECT_MEMORY_ROOT env
    Absolute path to the folder that holds your projects (the memory root). Defaults to the parent of the script if unset, so set it when running via npx.

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 kaaustubh, 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 7251 servidores de esta categoría
  • Publicado en el registro más recientemente que el 19% de los otros 7251 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 7251 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 Project Memory MCP?

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 (1.6.0) 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.

project-memory MCP server

A small, local MCP server that gives AI agents (Claude Code, Cursor, VS Code / GitHub Copilot, …) a shared, persistent memory of the projects in a code folder — what each project is, decisions made, and every bug/issue faced during development.

It is stateless: every tool reads/writes plain files on disk, so multiple clients (and multiple machines) share one source of truth.

The model

LayerLives inAuto-loaded into context?For
Project memory /AGENTS.md✅ yes (via CLAUDE.md → @AGENTS.md)identity, stack, run cmds, concise decisions/learnings — keep lean
Issue log /issues.jsonl❌ nohigh-volume bug/issue history — fetched on demand

Design rule: durable, low-volume facts go in AGENTS.md (auto-loaded). High-volume history (bugs) goes in issues.jsonl (queried via search_issues). This keeps the always-loaded context small while keeping everything searchable.

Works even where MCP is locked down

Some orgs disable third-party MCP servers via policy (e.g. GitHub Copilot's MCP allowlist enforcement). Because the memory is plain files, not a service, the core value survives that:

  • The memory itself is just files. AGENTS.md is auto-loaded by the editor reading it — no MCP call involved — so a project's identity, decisions, learnings, and preferences still land in the agent's context.
  • The policy is Copilot-scoped and per-client. It doesn't affect the same server in Claude Code or Cursor, and orgs running *allowlist / registry-only* mode can permit it — this server is published to the official MCP Registry (io.github.kaaustubh/project-memory-mcp).

Only the interactive tools (log_issue, search_issues, …) go over the MCP channel; the file-based memory keeps working without it.

Tools

  • list_projects, get_project, search_memory — read project memory
  • append_decision, append_learning — append a dated bullet to AGENTS.md
  • remember_preference — turn a correction / stated habit into a remembered pattern (## Preferences in the root AGENTS.md for a global habit, or a project's for a local one); rides the auto-load, so it comes back next session
  • log_issue — record a bug/problem → issues.jsonl
  • search_issues — "have we hit this before?" across all projects (field-scoped; optional tags filter)
  • list_open_issues, resolve_issue — track / close bugs
  • sync_registry — reconcile the root AGENTS.md projects table with what's on disk (adds rows for new projects, flags stale ones)
  • find_by_file — given a file path, surface the issues + decisions/learnings that touch it ("why is this code like this?")
  • start_initiative, get_initiative, list_initiatives, update_initiative — track a named, multi-session effort (a codename, a plan, an evolving todo list) so it's resumable from any future session by name, not just within the one that started it; see Initiatives below
  • check_in, check_out — stamp the start/end of the working day and record a standup summary composed from evidence harvested off disk (git commits across every repo, uncommitted WIP, memory writes, initiative progress); the next morning's check_in hands the summary back — that's your standup. Print it any time, no agent needed: npx -y @kaaustubh/project-memory-mcp standup

You don't call these directly — you talk to your agent in natural language and it picks

the tool. See Using it day to day below for what to actually say.

Using it day to day

Most of it runs itself: opening a project auto-loads its AGENTS.md (the agent already knows the project), and capture is proactive (plus the optional Stop hook). Your job is mainly to pull memory at the right moments. Just talk to your agent:

WhenSay something likeWhat fires
Before debugging anything*"Have we hit this before? "*search_issues across all projects
Starting something you've done elsewhere*"How did I do Stripe webhook verification in any project?"*search_memory (cross-project)
Landing on confusing code*"Why is index.js like this? Check the memory."*find_by_file
You made a real decision / fixed a real bug*(nothing — it logs on its own and tells you)*append_decision / log_issue
You correct how the agent works*"No, always run the typecheck before committing — remember that."*remember_preference (global or per-project)
Triage*"What's still open across my projects?"*list_open_issues
A bug is fixed*"Resolve pulse_stripe-004 — fixed by …"*resolve_issue
Added a new project*"Sync the registry."*sync_registry

The one habit that matters: make *"have we hit this before?"* reflexive before every debugging session. That's where a memory tool earns its keep; the rest the system handles.

Capture is confirming, not silent — when the agent logs something it tells you in one line. Correct it freely: *"don't log that"*, or *"actually, log this too."*

Escape hatches: PROJECT_MEMORY_HOOK=off silences the Stop hook for one session; uninstall-hook removes it entirely.

Install (npm — recommended)

From your code/projects folder, run:

cd ~/code            # the folder that holds your projects

npx -y @kaaustubh/project-memory-mcp install

That registers the server, using the current directory as your projects root, with every client that has an MCP config location on this machine:

ClientConfig written
Claude Codeuser scope, via claude mcp add
Cursor~/.cursor/mcp.json
VS Code / GitHub Copilot Chatuser-profile mcp.json (applies to every workspace)
GitHub Copilot CLI~/.copilot/mcp-config.json (or $COPILOT_HOME)
JetBrains Copilot plugin (IntelliJ, PyCharm, WebStorm, …)~/.config/github-copilot/intellij/mcp.json
Visual Studio (Windows)%USERPROFILE%\.mcp.json — global, all solutions
Kimi Code CLI~/.kimi-code/mcp.json (or $KIMI_CODE_HOME)
Gemini CLI~/.gemini/settings.json
OpenAI Codex CLI~/.codex/config.toml (the only non-JSON client — merged as TOML)
Windsurf~/.codeium/windsurf/mcp_config.json

Each write merges into the existing file (other MCP servers you've already configured are left alone) and is independently best-effort — a client that isn't installed on this machine is silently skipped, the rest still get registered. Restart whichever app(s) you use, then ask your agent *"set up project memory for this folder"* to scaffold AGENTS.md for each project.

Copilot surfaces (VS Code, CLI, JetBrains, Visual Studio): tools only run in

Agent mode, and config changes need a restart to take effect.

No clone, no global install — the MCP config just runs npx, which fetches and runs

the latest version on demand.

Team memory (beta signup): want this memory shared across your team instead of

just your machine? Register your interest: https://github.com/kaaustubh/project-memory-mcp/issues/1

From source instead
git clone https://github.com/kaaustubh/project-memory-mcp.git ~/code/.memory-server

cd ~/code/.memory-server && ./install.sh

How it works (after install)

A common question: *"once I install it, does it just start doing things?"* Not quite — the server is passive. Here's the actual flow:

  1. Restart your editor. MCP servers are loaded at startup, so the server only becomes available the next time you launch Claude Code / Cursor / VS Code.
  2. Push layer (automatic, not the server): when you open a project, the editor reads AGENTS.md (via CLAUDE.md → @AGENTS.md) into the model's context for you. This is why the agent "just knows" what your project is — it's a built-in editor feature.
  3. Pull layer (the server, on request): the server announces its tools and then waits. It does nothing on its own. The agent calls a tool only when it's relevant — e.g. you say *"log this bug"* or *"have we hit this before?"*, or the model decides a tool is useful. There's no background process or scanning.

Day one is empty. A fresh setup has no AGENTS.md files yet, so the auto-load has

nothing to load and log_issue will refuse until a project's memory exists. Bootstrap

once by asking your agent: *"set up project memory for this folder"* — it creates the

AGENTS.md files. After that, everything works.

In short: a convention (auto-loaded files) + a tool the agent chooses to use + a one-time setup. No magic, no daemon.

Proactive capture (you don't have to say "log this")

The server ships a standing capture policy (sent to the client on connect, plus directive tool descriptions), so the agent records things on its own instead of waiting for you to ask:

  • Before debugging a reported error → it checks search_issues for a prior fix.
  • After fixing a non-trivial bug → it calls log_issue.
  • After a real decision or a durable gotcha → append_decision / append_learning.
  • After you correct how it works or state a habit → remember_preference, so the one-time correction becomes a pattern it brings back next session.

It's proactive but not silent: the agent tells you in one line what it recorded, asks when unsure rather than logging noise, and skips trivia and secrets. You can always override — "log this", or "don't bother". The standing policy is best-effort (it depends on the model following it); for a hard guarantee, add the opt-in Stop hook below.

Guaranteed capture (opt-in Stop hook)

The standing policy can be forgotten mid-session. The Stop hook makes capture non-optional: when the agent tries to end a turn, it runs once and blocks the stop to ask for one capture pass when either (a) real work happened (file edits or a commit) and nothing was written to project memory, or (b) you corrected how it works and no preference was saved. If memory was already written, or nothing changed and you didn't correct it, the hook stays silent and lets the turn end.

npx -y @kaaustubh/project-memory-mcp install-hook    # turn it on (then restart Claude Code)

npx -y @kaaustubh/project-memory-mcp uninstall-hook # turn it off

  • Off by default — plain install does not add it; you enable it explicitly.
  • No loops — it fires at most once per turn (guarded by stop_hook_active), then lets the agent stop.
  • Per-session kill switch — set PROJECT_MEMORY_HOOK=off to disable without uninstalling.
  • Cost — it adds one extra model turn only on sessions that changed code but logged nothing, or where you corrected the agent and no preference was saved; silent otherwise.

Automatic recall (opt-in UserPromptSubmit hook)

Capture is only half the loop — the other half is *remembering to look*. The recall hook closes it: every time you submit a prompt, it matches your request against your issue history and decisions/learnings/preferences, and silently injects the strongest hits as context. So a prior fix or decision surfaces without you (or the agent) remembering to search — the "have we hit this before?" habit becomes automatic.

npx -y @kaaustubh/project-memory-mcp install-recall    # turn it on (then restart Claude Code)

npx -y @kaaustubh/project-memory-mcp uninstall-recall # turn it off

  • Semantic matching (when available) — if the optional embeddings model (@xenova/transformers) is installed, recall matches by meaning, so *"the build is broken"* still surfaces an issue logged as *"compile failure"* even

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

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