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Korg Introspect MCP

by New1Directionversion 0.1.1i★ –iRuns on your computer
Registry nameio.github.New1Direction/korg-introspect-mcp

Install

Overview

Description from the maker (original English)

Wrap any korg:introspect@v1-aware binary as a Claude Code tool, honoring declared side-effects.

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: Developer tools

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).
  • The registry lists no key to fill in.

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": {
    "korg-introspect": {
      "command": "npx",
      "args": [
        "-y",
        "@korgg/introspect-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add korg-introspect -- npx -y @korgg/introspect-mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "korg-introspect": {
      "command": "npx",
      "args": [
        "-y",
        "@korgg/introspect-mcp"
      ]
    }
  }
}

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": {
    "korg-introspect": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@korgg/introspect-mcp"
      ]
    }
  }
}

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.

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 9, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account New1Direction, 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 Developer toolsi

  • More stars than 0% of the other 5565 servers in this category
  • Published to the registry more recently than 16% of the other 5565 servers in this category
  • Basic checks equal to or better than 100% of the other 5565 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 Korg Introspect MCP 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

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 (0.1.1) reached the registry 5 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: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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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.

korg

A tamper-evident, SHA-256-chained ledger for AI agents. *Every step your agent takes is recorded in a hash chain anyone can verify — edit one event and the chain names it. No trust in the producer, no blockchain.*

English · 简体中文 · 繁體中文


30-second quickstart

Build the standalone verifier (~10s cold — it compiles only the ledger crates), verify a recorded Claude Code session, then rewrite one event and watch the chain catch it:

git clone https://github.com/New1Direction/korg && cd korg

cargo build --release -p korg-verify cp spec/korg-ledger-v1/web/samples/session.jsonl /tmp/session.jsonl

./target/release/korg-verify /tmp/session.jsonl

✓ journal VALID — 6 events, hash-chain + DAG intact

sed -i.bak '4s/return a - b/return a + b/' /tmp/session.jsonl # rewrite the agent's edit

./target/release/korg-verify /tmp/session.jsonl

✗ journal INVALID — 1 problem(s):

- seq 4: entry_hash mismatch (content was tampered) (exit 1)

No Rust toolchain? Drop the same file into the in-browser verifier — it runs locally and sends nothing.


Validated across three vendors (N=3)

The same korg-ledger@v1 format ingests traces from three architecturally different production agents, with causal structure (spec §2a) preserved through the round-trip:

AgentTransportTool shapeAdapterTests
Claude CodeJSONL on disk + live hooksAnthropic tool_use / tool_result blocksadapters/claude-code — smoke-tested on a real 3,141-event session, zero dropped58 ✓
OpenAI Codex CLIWebSocket framesOpenAI function_call + freeform custom_tool_calladapters/codex-ws11 ✓
Grok HeavyNDJSON stream16-agent parallel fan-outadapters/grok-heavy14 ✓

Because all three land in one format, one verifier checks them all — and it has byte-identical Rust, Python and JS implementations, differentially fuzzed against each other with 0 divergences. Known limits are written down rather than hidden: see the Grok Heavy limitations (ingest order vs. wall-clock concurrency, cross-agent chatroom edges).


Full demo: record → verify → rewind


AI agents are black boxes. When they fail, you can't debug. When they succeed, you can't reproduce it.

When they do something wrong, you can't undo it.

Korg fixes this.


What Korg Does

[!NOTE]

Universal Ingestion Integration Mode:

Korg v1 is an MCP-callable audit sink. Any MCP-compatible coding agent (Claude Code, Codex, etc.) can call korg's tools to record its session as a causally-linked, replayable, rewindable ledger. The agent must be instructed to log its actions — typically via system prompt or MCP server configuration. Fully passive auditing without agent cooperation is on the roadmap for future versions.

[!WARNING]

Trust Boundary & Deployment Scope:

Korg v1 is designed strictly for local, single-user workspaces. Multi-tenant and networked deployments require cryptographic authentication and permission bounds that are not yet shipped. Running the server on an untrusted or public network exposes workspace read/write access.

Korg is a cognitive hypervisor — a runtime layer that sits beneath your AI agents and governs every decision they make.

It doesn't replace your LLM. It governs what the LLM does.

Foundation Model          →  predicts, suggests, generates

──────────────────────────────────────────────────────────── Korg Cognitive Runtime → schedules, validates, isolates, reconciles, replays, heals, governs

Every agent action is:

  • Appended to an immutable, cryptographically-signed ledger
  • Ordered with Hybrid Logical Clocks (causal, deterministic, globally consistent)
  • Replayable — rebuild exact state at any point in history
  • Reversible — rewind the ledger to any prior sequence point

Try the Time-Travel Demo

You can run the built-in sandbox demo to see cognitive time-travel in action. The demo sets up a temporary workspace with a buggy Python script, lets a simulated coding agent make a wrong edit, catches the test failure, rewinds the workspace and ledger to before the edit, and speculatively commits the correct fix:

cargo run -- demo

You will see the complete, colorized time-travel sequence:

⚡ STARTING KORG COGNITIVE TIME-TRAVEL DEMO ⚡

──────────────────────────────────────────────────────────────────────────────── [korg] Initializing sandboxed demo environment... [korg] Created temporary workspace with math_utils.py (subtraction bug present).

🚀 PHASE 1: AGENT INITIATES RUN (WRONG PATH) [seq 390] actor: agent:claude-code@0.2.29 | tool: user_prompt | prompt: "Fix subtraction bug and verify tests pass" [seq 391] actor: agent:claude-code@0.2.29 | tool: Read | file: math_utils.py [seq 392] actor: agent:claude-code@0.2.29 | tool: Edit | result: "Modified return a + b (wrong fix)" [seq 393] actor: agent:claude-code@0.2.29 | tool: Bash | command: "pytest" -> ❌ FAILED (2 tests failed)

📊 LEDGER STATE (BEFORE REWIND): Before rewind: events 390-393 (prompt, read, edit-wrong, test-failed) ├── seq 390 (user_prompt) -> triggered_by: None ├── seq 391 (Read) -> triggered_by: Some(390) ├── seq 392 (Edit) -> triggered_by: Some(391) ├── seq 393 (Bash) -> triggered_by: Some(392)

⏳ PHASE 2: INITIATING REVERSIBLE REWIND TO SEQ 391 [korg] Truncating journal ledger to sequence ID 391... [korg] Restoring workspace snapshot via git read-tree (O(1))... [korg] Reset math_utils.py file state back to sequence 391 bug state. [korg] Rebuilding 3 read-model projections...

📊 LEDGER STATE (AFTER REWIND): After rewind: events 390-391 (prompt, read) ├── seq 390 (user_prompt) -> triggered_by: None ├── seq 391 (Read) -> triggered_by: Some(390)

🚀 PHASE 3: AGENT DIVERGES DOWN CORRECT PATH (SPECULATIVE REPLAY) [seq 392] actor: agent:claude-code@0.2.29 | tool: Edit | result: "Modified return a - b (correct fix)" [seq 393] actor: agent:claude-code@0.2.29 | tool: Bash | command: "pytest" -> ✓ PASSED (2 passed)

📊 LEDGER STATE (AFTER DIVERGENT RUN): After new run: events 390-393 (prompt, read, edit-right, test-passed) ├── seq 390 (user_prompt) -> triggered_by: None ├── seq 391 (Read) -> triggered_by: Some(390) ├── seq 392 (Edit) -> triggered_by: Some(391) ├── seq 393 (Bash) -> triggered_by: Some(392)

✓ DEMO COMPLETE: Time-travel execution succeeded! Ledger truncated, workspace rolled back, and a different future was successfully committed.

*No other AI agent runtime lets you do this.*


Core Architecture

Korg is built on the same theoretical foundations that make databases and operating systems reliable — applied to AI cognition for the first time.

InvariantWhat it means
Append-only WALEvery cognitive event is a ledger entry. Nothing is mutated, only appended. Like a database WAL, but for AI thought.
HLC Causal OrderingHybrid Logical Clocks guarantee globally consistent, causally ordered event streams — even across distributed swarm workers.
Deterministic ReplayAny campaign can be replayed byte-for-byte from the ledger. Same inputs, same outputs, every time.
Speculative BranchesFork execution into parallel hypothetical paths. Preview before committing. Discard freely.
Execution CheckpointsSnapshot the entire runtime state: ledger offset, projection views, lease maps, workspace tree. Restore in O(1).
Micro-HealingTransient failures (lock conflicts, stale state) are automatically healed at the effect level, with full retry audit trails.
Semantic GovernanceSwarm actions are validated against BERT embedding cosine similarity — semantic alignment, not keyword matching.
┌────────────────────────────────────────────────────────────────┐

│ korg v0.1.0 │ session: 019e5333-efc9-7c70 │ ● ACTIVE │ ├───────────────────────────────┬────────────────────────────────┤ │ SWARM PLAN │ LIVE MERKLE LEDGER │ │ ├─ [●] Captain [PLANNING] │ (tx_00)→(tx_01)→[tx_02]→... │ │ ├─ [●] Harper [RESEARCH] │ │ │ ├─ [●] Benjamin [SYNTHESIS] │ TELEMETRY │ │ └─ [○] Lucas [IDLE] │ ├─ Velocity 85.2 t/s ▇▆▄▂█ │ │ │ ├─ Entropy 0.451 ▄▃▂▃▄ │ │ GOVERNANCE GATES │ └─ Progress 68.7 % ▂▃▄▅▆▇ │ │ ├─ 🟡 Amber Security [IDLE] │ │ │ ├─ 🟢 Consensus [ACTIVE] │ LEDGER STREAM │ │ └─ 🔵 Steering Fork [IDLE] │ [tx_03] Benjamin: patch auth │ └───────────────────────────────┴────────────────────────────────┘


Quick Start

Build from source

The crate is not yet published to crates.io; install from source:

git clone https://github.com/New1Direction/korg

cd korg cargo build --release ./target/release/korg --help

Python bridge (for korgex / korgchat)
cd crates/korg-bridge

maturin develop # builds the PyO3 extension into the active venv python3 -c "import korg_bridge; print(korg_bridge.version)"

Run your first campaign
# Interactive TUI dashboard

korg campaign --tui --prompt "Refactor the auth layer to use JWTs"

Web cockpit at localhost:8080

korg campaign --web --prompt "Optimize the database connection pool"

Pure autonomous goal mode (--goal is a top-level flag). Each persona works in

its own git worktree and runs a real cargo check, so on a large repo a cold

run takes minutes; --speculative pre-warms a shared target dir.

korg --goal "Write and validate a full test suite for src/parser.rs"

Run the full multi-persona swarm on a REAL local model — every persona

(Captain, Harper, Benjamin, Lucas, Evaluator) runs as a real worker

subprocess doing real, measured, attested work. Defaults to a hermetic

deterministic provider; --provider ollama makes it live.

korg --goal "Fix the failing test in src/lib.rs" --provider ollama --model qwen2.5:7b

Preview without committing (dry-run; --preview is a top-level flag)

korg --preview "Refactor the main event loop"

Rewind & Verify
# Rewind the capability journal (.korg/capability_journal.json) to a prior

sequence point. It is populated by campaigns (korg --goal, korg campaign);

on a fresh checkout there is nothing to rewind yet — korg demo shows the

full rewind flow in a self-contained sandbox.

korg rewind --seq 4

Drive the honest pipeline on a fixture and emit a verifiable ledger

korg run-once "Fix the add function in src/lib.rs so it adds"

Same pipeline, but with a REAL local model (ollama) on an arbitrary task —

the model writes the patch, Korg applies it, measures the real git diff +

cargo check, and attests only what actually changed.

korg run-once "Fix the bug in src/lib.rs: max() returns the minimum. Output the COMPLETE corrected src/lib.rs: \\\rust $(cat your-repo/src/lib.rs) \\\"

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

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