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

KIP

de ldclabsversión 1.0.1i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.ldclabs/KIP

Instalar

Resumen

Descripción del creador (original en inglés)

Knowledge Graphs to enable memory persistence, knowledge evolution, explainable interaction.

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
  • Debes completar: KIP_BACKEND_URL, KIP_API_KEY

Instalación

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

Antes de instalar

Los valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.

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": {
    "kip": {
      "command": "npx",
      "args": [
        "-y",
        "@ldclabs/kip-mcp-server"
      ],
      "env": {
        "KIP_BACKEND_URL": "your-value",
        "KIP_API_KEY": "your-value"
      }
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add kip --env KIP_BACKEND_URL=your-value --env KIP_API_KEY=your-value -- npx -y @ldclabs/kip-mcp-server

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": {
    "kip": {
      "command": "npx",
      "args": [
        "-y",
        "@ldclabs/kip-mcp-server"
      ],
      "env": {
        "KIP_BACKEND_URL": "your-value",
        "KIP_API_KEY": "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": {
    "kip": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@ldclabs/kip-mcp-server"
      ],
      "env": {
        "KIP_BACKEND_URL": "your-value",
        "KIP_API_KEY": "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

  • KIP_BACKEND_URL obligatorio env
    HTTP URL for the KIP backend endpoint
  • KIP_API_KEY secreto env
    Your API key for the service

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 6 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub ldclabs, 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 7235 servidores de esta categoría
  • Publicado en el registro más recientemente que el 1% de los otros 7235 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 7235 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 KIP?

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?

Debes completar: KIP_BACKEND_URL, KIP_API_KEY

¿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 (1.0.1) llegó al registro hace 10 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 hace 3 días
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 hace 44 días
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.

KIP 2.0 — Cognitive State Protocol for Agent Memory

[English](./README.md) | [中文](./README_CN.md)

Status

Default development version: KIP 2.0 — normative draft. The repository root contains the current protocol and tooling. KIP v1.0-RC11 is frozen for historical reference and migration; changing the default does not upgrade existing deployments or declare the 2.0 protocol stable.

  • The scope is frozen. A new contract enters the draft only together with engine evidence; corrections, simplifications and new evidence do not need that gate (Specification Status).
  • Names are stable; revisions are digests. There is one memory package, kip://profiles/cognitive-memory@2.0.0, one general domain package, kip://domains/general@1.0.0, one standard projection policy, kip:memory-default, one standard strength policy, kip:strength-half-life-30d, and schema IDs urn:kip:2.0:schema:*. A draft revision is identified by its content digest; earlier draft packages are not retained.
  • Both reference engines pass the whole engine suite — 431 cases in 26 fixtures, none pending. The suite manifest records the verified engine revision.
  • No learning result is claimed. Engine runs, bounded models and structural tests are separate evidence from measured Brain behavior, which stays not_run until a Brain evaluation measures it.

What the latest revision changed, and how it was validated, is in the revision record.

Start here

  • Agent integration: Memory Interface and the Agent memory card.
  • Protocol implementation: Specification, syntax reference, and conformance checks.
  • Running a Brain: Anda Brain, a memory service that serves the Memory Interface; see Implementations.
  • Developer tools: TypeScript language toolkit (@ldclabs/kip-lang) and VS Code extension, both targeting KIP 2.0.
  • Existing v1 users: frozen archive, deployment migration, and repository path changes.

The archived MCP server and Skill implement v1 interfaces; native v2 integration starts with the current Interface and wire contracts above.

An interactive version — pan, zoom, relationship tracing, five guided views — is a self-contained file: download it and open it in a browser.

The one idea

A memory that cannot separate *what was said* from *what is true* will eventually lie to its owner with total confidence. KIP 2.0 refuses to collapse three questions:

Meaning      what can be represented at all

Belief what the Brain currently accepts Authority who may read, write, project, or elevate

So a stored Proposition is truth-neutral. An Assertion carries one actor's stance toward it, with mode, confidence, validity and evidence. Belief is not a stored field but an Epistemic Projection computed at read time over the eligible assertions:

Proposition exists  ≠  Proposition is true  ≠  Brain accepts Proposition

Change never rewrites the past. Three kinds of change keep three different histories:

  • The world changed. One new Assertion from the time of the change. Temporal succession closes the actor's older value at that point, so a formerly true claim is ended rather than declared wrong, and historical queries still answer with it. Two inferences never succeed one another.
  • A claim was wrong. A new Assertion supersedes the old one, which stays on record.
  • The Brain misrecorded it. Recording repair invalidates the wrong extraction; the source bytes and everyone's stance stay as they were.

Time is stated as honestly as it is known: asserted_at is when a claim was made, not when it was recorded; a claim with no stated start began no later than that; and a coarse date is a time bound, not an invented instant. A preference is an ordinary claim that changes within its kind — there is no Preference type. Where claims of one slot still conflict, the standard memory policy kip:memory-default resolves them the same way on every engine: context specificity first, then first-person testimony, then recency.

Evidence, provenance and schema get the same treatment: Evidence and Activity are first-class elements rather than metadata bags, and schema lives in versioned, digest-pinned packages. A Brain that meets a relation the Profile lacks drafts it into the Space's draft vocabulary with DEFINE, which only adds; promoting a draft onto an installed symbol is a Schema migration that only a Principal holding manage_schema performs. Retrieval composes with belief in one query through the Search Pattern: search finds candidates, and BELIEF decides what is accepted.

The proactivity layer

A memory that only answers when asked is half a memory. The other half is what the Brain does when nobody is talking to it — and it must not be a cron job. KIP 2.0 gives that half four mechanisms, none of which decides policy on the Brain's behalf:

MechanismWhat it is
WatchDurable attention state. A declared condition under which a change — or the absence of one — deserves attention. Armed watches are evaluated against committed Change Envelopes: a delta watch fires on a matching committed change; a *silence* watch fires once due_at has passed and the evaluator has consumed the change stream through that deadline without a match — the clock alone proves nothing. Proactivity becomes a state differential rather than a blind schedule. A fired Watch grants nothing: it creates attention, never an action, and reaches the business Agent through the Memory Interface's attention recall.
action_gateAn Activity class recording what the gate decided — act, ask, defer, or silence — with the inputs it weighed. Restraint is the hardest thing to justify after the fact, so deliberate silence is written down like any other outcome.
LIST DEPENDENTSBounded reverse traversal of provenance. Revise a root and the cognition compiled from it — insights, preference summaries, skills, the self-model — becomes discoverable in one operation instead of quietly stale. §57.5 makes the rule explicit: a revised root must not auto-retract its dependents, and must leave them reviewable. Whether one survives is a review decision, not a protocol rule.
PURGE PAYLOADDestroy an Evidence element's observed bytes while keeping the record: its digest, class, observation time, source, and the citations that depend on it. Data minimization that costs no provenance — corroboration and independence counting keep working on the surviving digest. Distinct from element purge, which destroys the record itself.

Supporting state lives in the Cognitive Memory Profile: WorkingState is the consolidated resume digest stamped with its basis_seq, so a Brain wakes from compiled state plus a delta rather than re-reading scrollback; computed dependency validity makes a revised root visible at the next read; and MnemonicState.utility holds the admission bet — how useful this memory is expected to be — kept deliberately separate from salience, from memory_strength, and from epistemic confidence. Strength itself is computed at read time from a base, an anchor and a pinned decay policy; nothing sweeps it, and reading never reinforces it.

The consequence channel

Everything above makes the system watch the world better. The consequence channel is how the world watches back.

Outcome Evidence records what actually happened after a decision, action, or trialed procedure — written by instrumentation (telemetry, verifiers, test harnesses, human review), never by the actor whose action it grades. An actor's own account is agent_statement, citable as context only; the separation is a conformance invariant, enforced as auditability — engine origin always records who wrote what, and Governance can restrict who may write outcomes — because an open protocol can make self-grading visible even where it cannot make it impossible.

Each outcome carries a task family that selects candidate consequences. Sharing the family establishes neither attribution nor baseline membership: under the optional Validated Learning companion, TrialRecord explicitly freezes comparable baseline attempts and outcomes. Treatment observations link through the instrument's outcome_observation Activity to an attempt and the action_gate decision that applied the exact Skill revision. Its DecisionRecord distinguishes retrieval from actual use, and grading counts independent attempts assigned before execution. A SkillRevision must name its task family before trial; a pattern that nothing could prove wrong is not procedural memory. On the channel sits the Skill lifecycle:

proposed → trialed → adopted → revoked

Lifecycle changes commit with a validated immutable EvaluationRecord on a lifecycle_verdict Activity and a guarded update (Validated Learning §7). Auditors replay exact inputs against the immutable TrialRecord; a Skill's current_trial only selects it, and its GradingState is a computed view. Without the companion, Skills stay unproven candidates — useful, recallable, never promoted. Only trialed → adopted promotes through a comparative verdict, and revocation is never harder than adoption. Same-state monitoring may retain standing under authorized policy without claiming new improvement; policy withdrawal may have zero outcom

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

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