TokenKnows

Name: TokenKnows
Author: johnnywuj81

by johnnywuj81version 0.3.0i★ –iRuns on your computer

Registry nameio.github.johnnywuj81/tokenknows

Install

Overview

Description from the maker (original English)

Distill AI coding sessions into weekly reports, ADRs and a knowledge graph - self-hosted

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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: TOKENKNOWS_API_TOKEN

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: TOKENKNOWS_API_BASE, TOKENKNOWS_API_TOKEN, TOKENKNOWS_DEFAULT_PROJECT, TOKENKNOWS_WEB_BASE

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

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "tokenknows": {
      "command": "uvx",
      "args": [
        "tokenknows-mcp"
      ],
      "env": {
        "TOKENKNOWS_API_BASE": "your-value",
        "TOKENKNOWS_API_TOKEN": "your-value",
        "TOKENKNOWS_DEFAULT_PROJECT": "your-value",
        "TOKENKNOWS_WEB_BASE": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add tokenknows --env TOKENKNOWS_API_BASE=your-value --env TOKENKNOWS_API_TOKEN=your-value --env TOKENKNOWS_DEFAULT_PROJECT=your-value --env TOKENKNOWS_WEB_BASE=your-value -- uvx tokenknows-mcp

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": {
    "tokenknows": {
      "command": "uvx",
      "args": [
        "tokenknows-mcp"
      ],
      "env": {
        "TOKENKNOWS_API_BASE": "your-value",
        "TOKENKNOWS_API_TOKEN": "your-value",
        "TOKENKNOWS_DEFAULT_PROJECT": "your-value",
        "TOKENKNOWS_WEB_BASE": "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": {
    "tokenknows": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "tokenknows-mcp"
      ],
      "env": {
        "TOKENKNOWS_API_BASE": "your-value",
        "TOKENKNOWS_API_TOKEN": "your-value",
        "TOKENKNOWS_DEFAULT_PROJECT": "your-value",
        "TOKENKNOWS_WEB_BASE": "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

TOKENKNOWS_API_BASE env
Self-hosted TokenKnows backend URL
TOKENKNOWS_API_TOKEN secret env
API token (tkk_...) or JWT bearer for the backend; create one in the web UI under Project Settings - MCP Access (optional for default local deployments)
TOKENKNOWS_DEFAULT_PROJECT env
Default project_id for event submission and distill
TOKENKNOWS_WEB_BASE env
Web UI base URL used to build view_url links returned by distill/list tools (login required to open them)

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account johnnywuj81, 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 7254 servers in this category
Published to the registry more recently than 16% of the other 7254 servers in this category
Basic checks equal to or better than 100% of the other 7254 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 TokenKnows 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: TOKENKNOWS_API_TOKEN

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 (0.3.0) 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 Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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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.

TokenKnows

Distill AI coding sessions into living knowledge — weekly reports, ADRs, incident reviews, books, agent skills, and a knowledge graph.

English | 简体中文

What is TokenKnows?

You spend hours pair-programming with Claude Code, Codex, and Cursor. The decisions, bug hunts, and design trade-offs from those sessions evaporate the moment the terminal closes. TokenKnows captures them automatically and distills them into structured, evidence-linked knowledge assets:

capture (6 collectors) → distill (5-stage LLM pipeline) → assets (7 document types) → review / redact / publish

📡 Captures everything — Claude Code, Codex, Cursor, VS Code, GitHub PRs/commits/issues, and local docs, all via local file watchers and API polling. No webhooks, no tunnels.
📝 Seven asset types — weekly reports, tech designs, ADRs, incident reviews, long-form books, reusable agent skills (SKILL.md), and an entity knowledge graph.
🔗 Evidence-linked — every paragraph traces back to the original PR / conversation / commit, ranked by cosine × trust × recency across ≥2 sources.
🔒 Local-first, zero egress by default — a three-layer LLM egress gate (instance ∧ project ∧ task) with full audit logging. Pair it with Ollama and run the whole pipeline with zero cloud keys.

Demo

Workbench	Document page

Evidence drawer	Publish receipt + version diff

▶ Full walkthrough: engineering_handoff/walkthrough.mp4 (5 min, Chinese narration + subtitles)

All 12 screens

1 Workbench	2 Event drawer	3 Document list	4 Document page

5 Evidence drawer	6 Regenerate dialog	7 Review	8 Redaction

9 Publish dialog	10 Publish receipt + diff	11 LLM egress	12 Admin

Install the plugin

Prerequisite: the TokenKnows backend at http://localhost:8001 and the web UI at `http://localhost:5173` (see Quick start), plus uv (the plugin pulls the MCP server from PyPI via uvx). All plugin env vars have working local defaults — export TOKENKNOWS_API_BASE / TOKENKNOWS_API_TOKEN / TOKENKNOWS_DEFAULT_PROJECT / TOKENKNOWS_WEB_BASE only for non-default setups. Register/login in the web UI and create an API token under Project Settings → MCP 接入 when your backend requires auth.

Platform	How
Claude Code	`/plugin marketplace add johnnywuj81/tokenknows` → `/plugin install tokenknows@tokenknows` — full walkthrough in tokenknows-plugin/README.md (5-minute quickstart)
Codex	`codex plugin marketplace add johnnywuj81/tokenknows` → `codex plugin add tokenknows@tokenknows` (loads skills, commands and the MCP server; local-clone alternative in codex-plugin/README.md)
Cursor	Add the tokenknows MCP block to `~/.cursor/mcp.json` (uvx config example in code/tokenknows-mcp/README.md)
VS Code	Download the `.vsix` from Releases → `code --install-extension tokenknows-vscode-*.vsix`

The plugin gives your AI tool MCP tools (submit_session_events, distill_document, list_assets, get_asset, get_asset_chapters, search_entity) plus slash commands like /tokenknows:weekly and /tokenknows:adr.

Quick start

# 1. (Optional but recommended) Ollama — fully local inference, zero cloud keys

ollama serve & ollama pull minimax-m2:cloud # or gpt-oss:20b, qwen2.5, ...

2. Backend (FastAPI + SQLite persistence + 3-layer LLM egress gate)

cd code/tokenknows-api python3 -m venv .venv && .venv/bin/pip install -e ".[dev]" cp .env.local.example .env.local # defaults to Ollama; edit to add cloud providers .venv/bin/uvicorn app.main:app --host 127.0.0.1 --port 8001

3. Frontend (React 19 + Vite)

cd code/tokenknows-web npm install npm run dev

open http://localhost:5173 — talks to the real backend (mocks are opt-in via ?msw=1)

(Optional) seed demo data

./engineering_handoff/demo-seed.sh

Platform support: macOS — full experience (collectors auto-start via launchd). Linux — backend, frontend, and collectors all run manually (python3 plugins/ /sync.py --watch); the launchd scripts don't apply. Windows — untested; WSL2 recommended.

Data collectors

All local — no ngrok, no public webhooks. On macOS they restart on crash and on reboot (launchd).

Collector	Source	Mode
claude-code	`~/.claude/projects/*.jsonl`	30s polling, incremental offsets
codex	`~/.codex/sessions/*/rollout-.jsonl`	30s polling, incremental offsets
cursor	Cursor's `state.vscdb` (read-only SQLite)	60s polling
github	GitHub REST API · PRs / issues / commits	5min polling (`gh auth` token)
vscode	VS Code extension `onDidSaveTextDocument`	buffered, 10s flush
local-docs	`~/Documents` `.md` `.txt` `.pdf` (watchdog)	realtime, 2s debounce

./scripts/launchd/install.sh          # macOS: install all 5 Python collectors as LaunchAgents

launchctl list | grep com.tokenknows tail -f ~/Library/Logs/tokenknows/*.log

Every event carries a trust score (0.6 × source_authority + 0.4 × extraction_confidence); the evidence stage ranks citations by 0.6 × cosine + 0.25 × trust + 0.15 × recency and enforces ≥2 distinct sources.

Architecture

Collectors feed an event store (SQLite). A five-stage pipeline (collect → outline → content → evidence → assess) turns events into assets. The LLM Gateway unifies four providers (Anthropic / OpenAI / MiniMax / Ollama) with per-task routing and fallback chains — and refuses any cloud call unless all three egress switches are on.

CI

Workflow	Runner	Trigger
`ci.yml`	ubuntu-latest (GitHub-hosted)	push to main + every PR
`ci-macos.yml`	self-hosted macOS ARM64	maintainer pushes to main only — never runs external PR code

Privacy & local-first

Zero egress by default — cloud LLM calls require the instance and project and task switches all on
Bring your own keys; the audit log never leaves your machine
One-click kill switch drops the instance into fully-offline mode

Details: PRD §6.7 data residency & egress control (Chinese).

Documentation

Topic	Doc
Product requirements, user journeys	PRD (zh)
Technical design, API, schema	TDD (zh)
Macro architecture & milestones	Architecture (zh)
Per-screen engineering decisions	TaskTechDesign (zh)
Pixel-level UI mockups	mockups/ — open in a browser

Most in-depth docs are in Chinese (the project's working language). Code comments are predominantly Chinese too; issues and PRs in English or Chinese are both welcome.

Community

CONTRIBUTING · Roadmap · Code of Conduct · Security policy · Issues

TokenKnows

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

What is TokenKnows?

Demo

Install the plugin

Quick start

2. Backend (FastAPI + SQLite persistence + 3-layer LLM egress gate)

3. Frontend (React 19 + Vite)

open http://localhost:5173 — talks to the real backend (mocks are opt-in via ?msw=1)

(Optional) seed demo data

Data collectors

Architecture

CI

Privacy & local-first

Documentation

Community

License