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

KIP

by ldclabsversion 1.0.1i★ –iRuns on your computer
Registry nameio.github.ldclabs/KIP

Install

Overview

Description from the maker (original English)

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

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
  • You need to fill in: KIP_BACKEND_URL, KIP_API_KEY

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).
  • You fill in yourself: KIP_BACKEND_URL, KIP_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

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

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

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

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

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

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.

Values you fill in

  • KIP_BACKEND_URL required env
    HTTP URL for the KIP backend endpoint
  • KIP_API_KEY secret env
    Your API key for the service

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 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account ldclabs, 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 7249 servers in this category
  • Published to the registry more recently than 1% of the other 7249 servers in this category
  • Basic checks equal to or better than 100% of the other 7249 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 KIP 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?

You need to fill in: KIP_BACKEND_URL, KIP_API_KEY

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 (1.0.1) reached the registry 10 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 today
Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 20 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.

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

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

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