mcprune

MCP middleware that prunes Playwright accessibility snapshots for LLM agents. Zero ML, 75-95% token reduction, all refs preserved.

The problem

Playwright MCP gives LLM agents browser control via accessibility snapshots — YAML trees of every element on the page. But real pages produce 100K-400K+ tokens per snapshot. That's too large for any LLM context window to handle effectively.

mcprune sits between the agent and Playwright MCP, intercepting every response and pruning snapshots down to only what the agent needs: interactive elements, prices, headings, and refs to click.

Agent  ←→  mcprune (proxy)  ←→  Playwright MCP  ←→  Browser
              ↓
         prune() + summarize()
         75-95% token reduction

Before / After

Amazon search page — raw Playwright snapshot: ~100,000 tokens. Includes every pixel-level wrapper, tracking URL, sidebar filter, energy label, legal footer, and duplicated link.

After mcprune: ~14,000 tokens. Product titles, prices, ratings, color options, "Add to basket" buttons, and clickable refs. Everything an agent needs to shop.

Amazon product page: ~28,000 tokens → ~3,300 tokens (88% reduction). Full buy flow preserved.

Quick start

As an MCP server (recommended)

Add to your Claude Code, Cursor, or any MCP client config:

{
  "mcpServers": {
    "browser": {
      "command": "node",
      "args": ["/path/to/mcprune/mcp-server.js"]
    }
  }
}

That's it. The agent gets all Playwright browser tools (browser_navigate, browser_click, browser_type, browser_snapshot, etc.) with automatic pruning on every response.

Options:

• --headless — run browser without visible window
• --mode act|browse|navigate|full — pruning mode (default: act)

As a library

import { prune, summarize } from 'mcprune';

const snapshot = await page.locator('body').ariaSnapshot();

const pruned = prune(snapshot, {
  mode: 'act',
  context: 'iPhone 15 price'  // optional: keywords for relevance filtering
});

const summary = summarize(snapshot);
// → "Apple iPhone 15 (128GB) - Black | pick color(5), set quantity, add to basket, buy now, 91 links"

How it works

A 9-step rule-based pipeline. No ML, no embeddings, no API calls.

Step	What	Why
1. Extract regions	Keep landmarks matching the mode (act → main only)	Drop banner, footer, sidebar in action mode
2. Prune nodes	Drop paragraphs, images, descriptions. Keep interactive elements, prices, short labels	Core reduction — 50-60% happens here
3. Collapse wrappers	generic > generic > button "Buy" → button "Buy"	Playwright trees are deeply nested
4. Clean up	Trim combobox options, drop orphaned headings	A 50-option dropdown → just the combobox name
5. Dedup links	One link per unique text per product card	Amazon cards have 3+ links to the same product
6. Drop noise	Energy labels, product sheets, ad feedback, "view options"	These repeat 10-30x per search page
7. Truncate footer	Everything after "back to top" is noise	Corporate links, legal text, subsidiaries
8. Drop filters	Sidebar refinement panels	20+ collapsible filter groups on Amazon
9. Serialize	Back to YAML, strip URLs, clean tracking params	URLs were 62% of output — agents click by ref

Context-aware pruning

When the agent types a search query, mcprune captures it as context. Product cards that don't match any keywords are collapsed to just their title, while matching products keep full details.

Agent types "iPhone 15" in search box
  → mcprune captures context: ["iphone", "15"]
  → Matching cards: full price, rating, colors, buttons
  → Non-matching cards: title only

Pruning modes

The mode controls only how mcprune prunes the snapshot. Playwright MCP executes all browser actions identically regardless of mode.

Mode	Regions kept	Pipeline	Use case
act	main only	All 9 steps	Shopping, forms, taking actions
browse	main only	Steps 1-4 + 9 (skip e-commerce noise removal)	Docs, articles, reading content
navigate	main + banner + nav + search	All 9 steps	Site exploration
full	All landmarks	All 9 steps	Debugging, full page view

Browse mode preserves paragraphs, code blocks, term/definition pairs, inline links, all headings, and figure captions — content that act mode drops because agents taking actions don't need article text.

Performance

Tested live via MCP proxy:

Page	Raw	Pruned	Reduction
Amazon NL search (30 products)	~100K tokens	~14K tokens	85.8%
Amazon NL product page	~28K tokens	~3.3K tokens	88.0%
Wikipedia article (browse)	~54K tokens	~8.6K tokens	84.0%
MDN docs (browse)	~10K tokens	~5.5K tokens	~43%
Python docs (browse)	~22K tokens	~17K tokens	~23%
Amazon product (fixture)	~1.2K tokens	~289 tokens	76.5%

All refs ([ref=eN]) are preserved. The agent can click, type, and interact with every element in the pruned output.

Install

git clone https://github.com/hamr0/mcprune.git
cd mcprune
npm install
npx playwright install chromium

Test

npm test  # 121 tests

Project structure

mcprune/
  mcp-server.js       MCP proxy — entry point, spawns Playwright MCP
  src/
    prune.js           9-step pruning pipeline + summarize(), mode-aware filtering
    parse.js           Playwright ariaSnapshot YAML → tree
    serialize.js       Tree → YAML, URL cleaning
    roles.js           ARIA role taxonomy (LANDMARKS, INTERACTIVE, STRUCTURAL, ...)
    proxy-utils.js     Extracted proxy logic (snapshot detection, context, stats)
  test/
    parse.test.js      8 parser tests
    prune.test.js      12 prune + summarize tests
    proxy.test.js      24 proxy utility tests
    edge-cases.test.js 77 edge case + browse mode + regression tests
    fixtures/          9 real-world page snapshots (e-commerce, docs, forums, gov)
  scripts/             Dev tools for capturing live snapshots
  blueprint.md         Detailed technical documentation
  docs/                Structured project documentation

How the MCP proxy works

1. Spawns @playwright/mcp as a child process over stdio
2. Forwards all JSON-RPC messages bidirectionally
3. Tracks context from browser_type text and browser_navigate URL params
4. Intercepts all tool responses (not just browser_snapshot — Playwright embeds snapshots in browser_click, browser_type, etc.)
5. Detects snapshots via regex, runs prune() + summarize()
6. Prepends a stats header: [mcprune: 85.8% reduction, ~100K → ~14K tokens | page summary]

License

MIT