browser-optimizer-mcp 0.1.1

Browser Optimizer MCP — reduces LLM token usage by 80-98% for browser automation

Browser Optimizer MCP

⚡ Quick Install

pip install browser-optimizer-mcp
browser-optimizer install

The install command automatically:

• ✅ Checks your Python version (≥ 3.11 required)
• ✅ Installs Playwright browser binaries (chromium)
• ✅ Detects Claude Desktop and auto-writes its MCP config
• ✅ Detects Antigravity IDE and auto-writes its MCP config
• ✅ Prints manual setup instructions for Cursor
• ✅ Verifies the installation end-to-end

Once installed, start the server with:

browser-optimizer start

Tip: Run browser-optimizer doctor at any time to diagnose your setup.

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An optimization middleware layer built on top of FastMCP and Playwright. It sits between AI agents (LLMs) and browser automation frameworks to drastically reduce token usage, execution latency, and API inference costs while maintaining high accuracy for browser workflows.

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🔄 How It Works & Process Flow

1. Step-by-Step Execution Pipeline

When an AI agent requests a page analysis, the Optimizer executes the following pipeline:

1. Request Intake: The AI agent calls extract_context with a target URL.
2. Browser Navigation: The browser manager opens or reuses a Playwright page and navigates to the URL.
3. HTML & Accessibility Tree Capture: The extractor captures the raw HTML markup and generating a semantic ARIA snapshot of the page body.
4. xxhash Fingerprinting: The semantic cache hashes the raw HTML to uniquely identify the page state.
   • Cache Hit: If the hash matches a stored signature, the server returns the cached context in less than 1ms, skipping DOM parsing and cleaning completely.
   • Cache Miss: If it's a new page or the content changed, the extraction process continues.
5. Context Compression: The compressor strips out styling, scripts, SVGs, and header/footer boilerplate. It extracts only interactable elements (buttons, inputs, dropdowns, links).
6. Task Classification: The rule-based classifier analyzes the interactive elements to score and categorize the page type (e.g. login, product search, checkout).
7. Delta Diff Calculation: The difference engine compares the fresh UI elements with the last observed state of this URL, outputting only added or removed controls.
8. Metrics Logging: The metrics module records raw bytes, compressed bytes, and savings ratios.
9. Payload Delivery: A compact JSON package containing the optimized UI elements, ARIA snapshot, and page metadata is returned to the agent.

2. Process Flow Diagram

sequenceDiagram
    autonumber
    actor Agent as AI Agent (LLM)
    participant Server as MCP Server (main.py)
    participant Cache as Semantic Cache (cache.py)
    participant Browser as Browser Manager (manager.py)
    participant Ext as Page Extractor (extractor.py)
    participant Comp as Context Compressor (compressor.py)
    participant Diff as Difference Engine (diff.py)

    Agent->>Server: extract_context(url)
    Server->>Browser: navigate(url)
    Browser-->>Server: page loaded
    Server->>Cache: lookup(url, current_html)
    alt Cache Hit (HTML Unchanged)
        Cache-->>Server: Return cached context
    else Cache Miss (HTML Changed / New URL)
        Server->>Ext: extract(page)
        Ext-->>Server: raw html, title, url, ARIA tree
        Server->>Comp: compress(extracted_data)
        Comp-->>Server: cleaned UI elements list, text summary
        Server->>Cache: store(url, html, compressed_context)
    end
    Server->>Diff: compute_diff(url, current_ui)
    Diff-->>Server: added & removed UI elements
    Server-->>Agent: Return optimized JSON context

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Use Cases

Here are 10 key use cases where the Browser Optimizer MCP can be utilized:

1. AI Agent Cost Reduction: Reduces token usage on dense web pages (like e-commerce portals or social media feeds) by up to 98%, dramatically lowering LLM API costs.
2. Structured Web Scraping: Empowers LLM-based scrapers to locate and extract content from specific elements without parsing scripts, styles, or bloated HTML trees.
3. Automated UI/E2E Testing: Helps developers run fast assertion checks on UI changes by using delta diffing to detect structural regressions.
4. Form Auto-Filling: Provides clean, interactive element trees, allowing agents to fill forms, log in, or interact with custom widgets without parsing complex nested divs.
5. Real-time Page Monitoring: Monitors active pages for updates using delta diffing, alerting agents only when new interactive components are added or removed.
6. Web Search & RAG Pipelines: Acts as an efficient web crawler that strips boilerplates (headers, footers, etc.) and provides clean context for Retrieval-Augmented Generation.
7. Accessibility Compliance Audits: Exposes semantic ARIA accessibility trees, letting automated agents inspect pages for accessibility compliance.
8. E-Commerce Monitoring: Navigates product listings, classifies page types, and extracts price/stock updates with minimal payload sizes.
9. Dynamic Single Page Application (SPA) Automation: Handles modern JavaScript-heavy frameworks by running Playwright locally, caching states, and delivering optimized components.
10. Multi-Agent Browser Sharing: Serves as a standardized MCP bridge for multi-agent workflows, letting separate agents share, observe, and control the same browser session.

and many more...

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🛠️ MCP Tools Reference

The server registers and exposes the following tools:

Tool	Parameters	Return Type	Description
extract_context	url (string)	CompressedContext	Navigates to a URL, performs cleanup and compression, runs page classification, and returns optimized UI and ARIA trees.
page_diff	url (string)	PageDiff	Returns deltas (added/removed elements) compared to the last observed state of this URL.
execute_action	action (string), selector (optional), value (optional)	ActionResult	Executes standard interactions (click, type, select, scroll, wait, navigate) on the active page.
summarize_page	url (string)	Dict	Produces an instant text summary listing interactive element counts and text content snippets.
classify_page	url (string)	ClassificationResult	Evaluates UI elements to identify the page category (e.g. login, search, survey).
wait_until_ready	url (string), timeout (optional)	ActionResult	Navigates to a page and waits for browser stabilization.
cache_lookup	url (string)	Dict	Directly queries the semantic cache for stored context.
get_metrics	None	Dict	Retrieves telemetry (bytes saved, cache hit rate, total actions).

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📂 About the Code & Module Architecture

The codebase is structured modularly under the app/ directory:

• app/browser/manager.py: Controls the lifecycle of the async Playwright browser. Reuses page contexts to avoid startup overhead and manages navigation timeouts.
• app/extractor/extractor.py: Siphons raw HTML and utilizes Playwright's latest .aria_snapshot() API to capture accessibility trees.
• app/compressor/compressor.py: Houses DOM filters. Strips unneeded tags (scripts, styles, headers, footers, SVGs) and outputs a list of structured interactive UI controls.
• app/classifier/classifier.py: Employs a heuristics-based scoring algorithm to classify pages into LOGIN, SEARCH, SURVEY, CHECKOUT, PRODUCT, or DASHBOARD states.
• app/diff/diff.py: Compares consecutive observations on the same URL and generates a delta report (added and removed elements) using composite element fingerprints.
• app/cache/cache.py: Manages an in-memory cachetools.TTLCache indexed by URL and validated via 64-bit xxhash signatures of page HTML.
• app/executor/executor.py: Executes browser interactions (click, type, select, scroll, wait, navigate) deterministically.
• app/schemas/schemas.py: Declares Pydantic data models enforcing contract compliance across modules and tools.
• app/metrics/metrics.py: Logs context size reductions, cache hits/misses, and calculates cumulative byte savings.

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⚙️ Setup & Installation

1. Prerequisites

• Python 3.11 or newer.
• Playwright dependencies installed on your system.

2. Installation Steps

# Clone the repository
git clone https://github.com/yourusername/browser-optimizer-mcp.git
cd browser-optimizer-mcp

# Create and activate virtual environment
python -m venv venv
venv\Scripts\activate     # On Windows
source venv/bin/activate  # On macOS/Linux

# Install dependencies
pip install -r requirements.txt

# Install Playwright browser binaries
playwright install chromium

3. Configuration

Create a .env file in the project root:

LOG_LEVEL=INFO
HEADLESS=True
CACHE_ENABLED=True
CACHE_TTL=300
CACHE_MAX_SIZE=100
BROWSER_TIMEOUT=30000

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🖥️ Client Integration

1. Claude Desktop Setup

Add the configuration to your claude_desktop_config.json file (located at %APPDATA%\Claude\claude_desktop_config.json on Windows or ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

For Windows (uses the cmd wrapper to handle folder spaces):

{
  "mcpServers": {
    "browser-optimizer": {
      "command": "cmd",
      "args": [
        "/c",
        "C:\\path\\to\\browser-optimizer-mcp\\venv\\Scripts\\python.exe",
        "-m",
        "app.server.main"
      ],
      "env": {
        "PYTHONPATH": "C:\\path\\to\\browser-optimizer-mcp"
      }
    }
  }
}

For macOS / Linux:

{
  "mcpServers": {
    "browser-optimizer": {
      "command": "/path/to/browser-optimizer-mcp/venv/bin/python",
      "args": ["-m", "app.server.main"],
      "env": {
        "PYTHONPATH": "/path/to/browser-optimizer-mcp"
      }
    }
  }
}

2. Antigravity IDE Setup

Add this to your mcp_config.json file (located at %USERPROFILE%\.gemini\config\mcp_config.json on Windows or ~/.gemini/config/mcp_config.json on macOS/Linux):

{
  "mcpServers": {
    "browser-optimizer": {
      "command": "C:\\path\\to\\browser-optimizer-mcp\\venv\\Scripts\\python.exe",
      "args": ["-m", "app.server.main"],
      "env": {
        "PYTHONPATH": "C:\\path\\to\\browser-optimizer-mcp"
      }
    }
  }
}

3. Cursor Setup

1. Go to Settings -> Features -> MCP.
2. Click + Add New MCP Server.
3. Configure the parameters:
   • Name: browser-optimizer
   • Type: command
   • Command: C:\path\to\browser-optimizer-mcp\venv\Scripts\python.exe -m app.server.main
4. Set the environment variable PYTHONPATH = C:\path\to\browser-optimizer-mcp.
5. Click Save.

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📊 Benchmark & Tool Comparison

Directly comparing the Browser Optimizer MCP against traditional browser automation agents highlights the efficiency gains:

1. Performance Comparison

Metric / Feature	Standard Browser Tools (Direct DOM/Screenshots)	Browser Optimizer MCP
Average Token Count (Google)	~50,000+ tokens	~120 tokens (97.7% reduction)
Average Token Count (HN)	~9,000+ tokens	~1,500 tokens (87.8% reduction)
Observation Payload Type	Raw DOM or Base64 screenshots	Clean JSON UI controls + ARIA snapshot
Incremental Observations	Resends entire DOM or new screenshot	Returns only element deltas (added/removed)
Re-observation Latency	Full DOM download and parse (~1.5s)	In-memory cache lookup (~0.12ms)
Page Classification	Requires LLM API call & reasoning tokens	Instant, local rule-based heuristics (0 tokens)
Action Execution	LLM must reason step-by-step	Deterministic rule-based execution
Inference Cost	High ($0.15 - $1.00+ per step)	Extremely Low (80-95% cost reduction)

2. Running the Benchmark Suite

To run the benchmark suite against live public pages and verify these savings on your local machine:

$env:PYTHONPATH="."
venv/Scripts/python scripts/benchmark.py

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🧪 Testing & Deployment

Run Unit Tests

pytest tests/ -v

Docker Deployment

docker compose -f docker/docker-compose.yml up --build

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📄 License

Distributed under the MIT License. See LICENSE for more details.

Copyright (c) 2026 Manthan.