Sarup

Name: Sarup
Author: PHUICMT

by PHUICMTversion 0.2.0i★ –iRuns on your computer

Registry nameio.github.PHUICMT/sarup

Install

Overview

Description from the maker (original English)

Thai-first context compression; shrinks long Thai and English text, originals retrievable.

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: Other

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

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": {
    "sarup": {
      "command": "uvx",
      "args": [
        "sarup"
      ]
    }
  }
}

Run this in your terminal

claude mcp add sarup -- uvx sarup

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "sarup": {
      "command": "uvx",
      "args": [
        "sarup"
      ]
    }
  }
}

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": {
    "sarup": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "sarup"
      ]
    }
  }
}

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account PHUICMT, 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 Otheri

More stars than 0% of the other 32589 servers in this category
Published to the registry more recently than 77% of the other 32589 servers in this category
Basic checks equal to or better than 100% of the other 32589 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 Sarup 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 (0.2.0) reached the registry 10 days 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: Spec Driven Development, Remote Desktop Commander, Detailing Manager

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.

สรุป · Sarup

Thai-first context compression for Claude Code. An MCP server that actually shrinks Thai (50–78% fewer tokens) and caches every original so nothing is ever lost. Measured for what matters too: whether Claude still answers correctly.

*สรุป* means "to summarize." Headroom routes Thai through noop (0% savings) because its

whitespace tokenizer can't find Thai word boundaries. Sarup uses PyThaiNLP segmentation, so it

compresses Thai as well as English — and caches every original so nothing is ever lost.

Highlights
Why it's safe — the two-tier guarantee
How it works
Tools
Compression modes
Measured results
Example
Install
Register with Claude Code
Auto-compression hook
Privacy & data
Configuration
Project structure
Tech stack & techniques
Testing
Roadmap
License

Highlights

🇹🇭 Real Thai compression — PyThaiNLP newmm word segmentation, not whitespace.
♻️ Lossless by guarantee — every compress caches the original; verified: true proves a byte-for-byte round-trip.
🎚️ Five modes: from offline 1 ms TF-IDF to a 78%-savings cascade.
🧠 Optional local LLM — embeddings + rewrite via Ollama, with automatic offline fallback.
📏 Honest metrics: a real tokenizer (tiktoken) at runtime; Claude's own tokenizer and a Thai QA eval for the published numbers (Does Claude still get it right?).
🕳️ Visible gaps: […] marks every place sentences were dropped, so Claude retrieves instead of mistaking a nearby fact for the answer.
🔌 Content-aware — JSON compaction, log dedup, and verbatim code-fence preservation built in.
🛟 Can't break Claude — it's an MCP tool, not an API proxy; if the server is down the tools just go away and Claude keeps working.

Why it's safe — the two-tier guarantee

Tier	What	Guarantee
Compressed view	the shrunk text the model works on	lossy · small · cheap
Retrieval store	the original, keyed by a stable hash	lossless · recoverable

Aggressive lossy compression is safe *because* the original is always one sarup_retrieve(hash) away. This is how "maximum savings" and "100% accuracy" coexist — they live in different tiers.

How it works

Two entry points feed one engine: a cheap compressed view the model reads, and a lossless retrieval store that can restore the original byte-for-byte.

flowchart TD

M["🧑 Manual sarup_compress()"]:::entry --> R A["⚙️ Automatic PostToolUse hook (Read · Bash · Grep)"]:::entry --> R

R{"Sarup compress extractive · semantic · abstractive · pipeline"}:::engine R -- "compressed view 50–78% fewer tokens" --> V["📄 Model context"]:::lossy R -. "cache original" .-> S[("🗄️ Retrieval store hash → original")]:::lossless

V -. "need full detail?" .-> RET["🔑 sarup_retrieve(hash)"]:::lossless RET --> S S == "byte-for-byte ✓" ==> V

classDef entry fill:#e0e7ff,stroke:#6366f1,color:#111 classDef engine fill:#fde68a,stroke:#d97706,color:#111 classDef lossy fill:#fef3c7,stroke:#f59e0b,color:#111 classDef lossless fill:#bbf7d0,stroke:#16a34a,color:#111

Manual — the model calls sarup_compress / sarup_retrieve itself.
Automatic — the hook intercepts large tool outputs, caches the original to SARUP_DB_PATH, and substitutes the compressed view + a retrieval hash. Source code is skipped; small outputs pass through untouched.

Tools

Tool	Purpose
`sarup_compress(content, target_ratio?, lossless?, query?, mode?)`	Compress; returns compressed text, hash, token metrics,`verified`, `token_method`.
`sarup_retrieve(hash)`	Recover the original content byte-for-byte.
`sarup_stats()`	Cumulative session savings.

`sarup_compress` arguments

Arg	Type	Default	Meaning
`content`	string	—	Text to compress (required).
`target_ratio`	number	`0.5`	Fraction of prose to keep (0.1–0.9).
`lossless`	boolean	`false`	Only apply lossless transforms (whitespace / JSON compact).
`query`	string	`""`	What you need from the text; sentences matching it are kept. Pass it whenever you know: it took the QA eval from 69% to 100% at the same size.
`mode`	string	`extractive`	See modes below.

Compression modes

Mode	How	Needs Ollama	Savings¹	Speed¹	Output
`extractive` (default)	TF-IDF scoring + fact boost + n-gram dedup	no	52.7%	~1 ms	verbatim subset
`semantic`	Embedding centrality + cosine dedup	yes	63.8%	~2 s	verbatim subset
`abstractive`	Local-LLM rewrite	yes	44.4%	~8 s	paraphrased
`pipeline`	Cascade: semantic → abstractive	yes	78.2%	~6 s	paraphrased
`auto`	semantic if Ollama is up, else extractive	optional	63.8%	~2 s	subset

¹ Measured on a 10-sentence Thai paragraph (522 tokens), rewrite model gemma4:12b. Every mode stays 100% recoverable via the store; Ollama modes degrade gracefully to extractive when the backend is down. (gemma4:e4b: pipeline 82.2%; June 2026, gemma3:12b: 88.1%.)

Extractive and semantic output is a verbatim subset of the input sentences, with […] on its own line wherever sentences were dropped. Sentences carrying specifics (numbers, Thai digits, identifiers like X-Api-Key) score higher than general statements, since those are what questions ask about.

Measured results

$ .\.venv\Scripts\python.exe bench\benchmark.py

sample before after savings verify Thai prose 522 247 52.7% OK Thai prose (aggressive) 522 189 63.8% OK English prose 105 60 42.9% OK JSON (lossless) 67 44 34.3% OK Logs 563 300 46.7% OK TOTAL 1779 840 52.8% ALL OK → 100% recoverable

Mode comparison (Thai prose, 522 tok): extractive 52.7% (1ms) · semantic 63.8% (2.1s) · auto 63.8% (2.1s) abstractive 44.4% (8.3s) · pipeline 78.2% (5.8s) ← gemma4:12b

Token counts via tiktoken cl100k_base. That is OpenAI's tokenizer and undercounts Claude, most of all on Thai; bench\benchmark.py --claude adds a column from Claude's own tokenizer (Anthropic count_tokens, needs an API key).

Does Claude still get it right?

Fewer tokens only help if the facts survive. bench\eval_qa.py compresses 8 Thai documents (incident report, meeting notes, API docs, HR policy, product spec, support chat, README, sales report; half line-per-sentence, half dense paragraphs) and asks Claude Opus 5.5 32 questions from the compressed view alone, graded deterministically:

$ .\.venv\Scripts\python.exe bench\eval_qa.py        # uses your local `claude` login

mode claude tok saved correct abstain wrong ────────────────────────────────────────────────────────────── original 4601 0.0% 100% 0% 0 extractive:0.5 3204 30.4% 69% 31% 0 extractive:0.3 1969 57.2% 44% 56% 0 semantic:0.5 3214 30.1% 62% 38% 0 extractive:0.5+q 3207 30.3% 100% 0% 0 abstractive:0.5 2697 41.6% 91% 9% 0 ← gemma4:12b pipeline:0.5 1854 59.8% 59% 41% 0 ← gemma4:12b

(gemma4:e4b scored the same: abstractive 91%, pipeline 59%, wrong 0.)

wrong = 0 is the safety property: when a fact was dropped, Claude said so (*abstain*) and in real use would call sarup_retrieve. It did not guess from what was left.
abstain is the real cost of blind compression: about a third of specific questions need a retrieval round-trip at 0.5. Know what you're looking for? Pass it as query (+q row).
abstractive keeps the most facts per token (91% at 42% saved) because a rewrite can keep every number while dropping words; extraction can only drop whole sentences. It costs seconds per document, so use it through sarup_compress, not the hook.
In both evals no paraphrase changed a fact into a wrong one, but 8 documents can't prove a rewrite never will, which is why the original stays one sarup_retrieve away.
Token counts here are Claude's own (from the answer calls' usage) and include the ~40-token [sarup: …] footer, which weighs heavily on these short (~570-token) documents and fades on the large outputs the hook actually compresses.
8 documents is a small sample; differences of a couple of questions between modes are noise.

Before the fixes this eval drove (Oct 2026): dense Thai paragraphs were not compressed at all (the sentence splitter's model wasn't installed), extractive:0.5 answered 59% with one confident wrong answer, and semantic:0.5 47%.

Example

A real sarup_compress call on a Thai paragraph (mode="auto", Ollama up → semantic):

// → sarup_compress(content="…518-token Thai paragraph…", mode="auto")

{ "compressed": "จุดเด่นที่สำคัญที่สุดคือมันไม่มีทางทำให้ Claude พัง…", "hash": "caa568140bec0ff734937cf5", "original_tokens": 518, "compressed_tokens": 154, "tokens_saved": 364, "savings_percent": 70.3, "transforms": ["semantic

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

Sarup

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

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 Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Detailing Manager

README from the maker

สรุป · Sarup

Contents

Highlights

Why it's safe — the two-tier guarantee

How it works

Tools

Compression modes

Measured results

Does Claude still get it right?

Example