sun-tzu-mcp

An MCP server that finds the Sun Tzu quote for a situation. Built for workplace use: describe what is happening ("the PM keeps adding scope two days before release") and get back verses from The Art of War with their chapter citation, so the quote you paste is the quote Sun Tzu actually wrote.

Tools

There is also a sun-tzu://corpus resource holding all 383 verses.

The card is written to a temp file and put on the clipboard. Clipboard copying is macOS only; on other platforms the PNG is still written and the tool says so instead of failing.

caption sets one italic line above the verse, for framing the quote (On being told "good job"). It is taken verbatim, shrunk to fit, then clipped with an ellipsis. Omit it for a bare card.

Related MCP server: Hilanet MCP

Install

Needs Node 20 or newer. Nothing else: no API key, no network, no database.

claude mcp add sun-tzu -- npx -y sun-tzu-mcp

Or, for any MCP client that takes a JSON config:

{
  "mcpServers": {
    "sun-tzu": {
      "command": "npx",
      "args": ["-y", "sun-tzu-mcp"]
    }
  }
}

The server runs locally as a subprocess of the client and talks stdio. Nothing is hosted, nothing phones home.

From a clone

pnpm install
pnpm build
claude mcp add sun-tzu -- node /absolute/path/to/sun-tzu-mcp/dist/index.js

How matching works

No embeddings, no network, no API key. The corpus ships with the server and matching is deterministic:

1. The situation is normalized, tokenized and stemmed.

2. A lexicon maps working-life vocabulary onto the theme vocabulary, so reorg reaches verses tagged adaptability and micromanage reaches delegation without sharing a word with the text.

3. Verses score on three fields: matched themes (weight 3), curated situation hints (2), and verse text (1). A theme that several query terms agree on counts for more, which keeps an incidental word from steering the result.

The tradeoff: paraphrase far outside the lexicon matches less well than embeddings would. In exchange it is instant, free, offline and debuggable, and the calling model is already good at rewriting a situation into words the lexicon knows.

Data

• data/source/pg132.txt — the Giles 1910 translation, Project Gutenberg #132, public domain.

• data/verses.json — generated. 383 verses parsed out of the source by pnpm corpus. Do not edit by hand.

• data/tags.json — hand-curated. Themes and situation hints for the verses that carry over to working life. Kept separate from verses.json so re-parsing never wipes it.

• data/themes.json — the theme vocabulary. tags.json is validated against it at server startup, so an unknown theme fails loudly instead of silently never matching.

Parsing notes

The Gutenberg plain text interleaves Giles' commentary with the verses and its brackets are not reliable: blocks are missing opening brackets, missing closing brackets, and one verse (V.9) is missing the period after its number. Character-level bracket counting silently swallows whole chapters. The parser therefore works paragraph by paragraph, uses verse numbers as hard boundaries, and falls back on markers of Giles' voice (commentator names, dates, "says:") where the brackets fail. pnpm corpus prints verse coverage per chapter and flags any gap in the numbering, so a regression is visible.

Tests

pnpm build && pnpm test

Covers the corpus (verse count, citation format, no commentary leakage) and the matcher (a table of situations that must each reach a specific verse in the top 3). Add a row to EXPECTED in test/match.test.mjs when you extend the lexicon.

Licence

The translation is public domain. The code and the curation are MIT.