Token Enhancer

A local proxy that strips web pages down to clean text before they enter your AI agent's context window.

One fetch of Yahoo Finance: 704,760 tokens → 2,625 tokens. 99.6% reduction.

No API key. No LLM. No GPU. Just Python.

The Problem

AI agents waste most of their token budget loading raw HTML pages into context. A single Yahoo Finance page is 704K tokens of navigation bars, ads, scripts, and junk. Your agent pays for all of it before any reasoning happens.

The Solution

Token Enhancer sits between your agent and the web. It fetches the page, strips the noise, caches the result, and returns only clean data.

Source	Raw Tokens	After Proxy	Reduction
Yahoo Finance (AAPL)	704,760	2,625	99.6%
Wikipedia article	154,440	19,479	87.4%
Hacker News	8,662	859	90.1%
GitHub repo page	171,234	6,976	95.9%

Install

pip install xelektron-token-enhancer

Quick Start (from source)

git clone https://github.com/xelektron/token-enhancer.git
cd token-enhancer
chmod +x install.sh
./install.sh
source .venv/bin/activate
python3 test_all.py --live

Usage

As a standalone proxy

source .venv/bin/activate
python3 proxy.py

Then in another terminal:

curl -s http://localhost:8080/fetch \
  -H "content-type: application/json" \
  -d '{"url": "https://finance.yahoo.com/quote/AAPL/"}' \
  | python3 -m json.tool

As an MCP Server (Claude Desktop, Cursor, OpenClaw)

This is the plug and play option. Your AI agent discovers the tools automatically and uses them on its own.

pip install xelektron-token-enhancer

Claude Desktop: Add to your config file

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "token-enhancer": {
      "command": "python3",
      "args": ["-m", "mcp_server"]
    }
  }
}

Cursor: Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "token-enhancer": {
      "command": "python3",
      "args": ["-m", "mcp_server"]
    }
  }
}

Once connected, your agent gets three tools:

fetch_clean fetches any URL and returns clean text (86 to 99% smaller)

fetch_clean_batch fetches multiple URLs at once

refine_prompt optional prompt cleanup, shows both versions so you decide

As a LangChain Tool

from langchain.tools import tool
import requests

@tool
def fetch_clean(url: str) -> str:
    """Fetch a URL and return clean text with HTML noise removed."""
    r = requests.post("http://localhost:8080/fetch", json={"url": url})
    return r.json()["content"]

Add fetch_clean to your agent's tool list. Start python3 proxy.py first.

Features

Data Proxy (Layer 2)
Fetches any URL, strips HTML/JSON noise, returns clean text. Caches results so repeat fetches are instant. Handles HTML, JSON, and plain text.

Prompt Refiner (Layer 1, opt in)
Strips filler words and hedging while protecting tickers, dates, money values, negations, and conversation references. You see both versions and choose.

MCP Server
Plug into Claude Desktop, Cursor, OpenClaw, or any MCP client. Agent discovers the tools and uses them automatically.

API Endpoints (proxy mode)

Endpoint	Method	Description
`/fetch`	POST	Fetch URL, strip noise, return clean data
`/fetch/batch`	POST	Fetch multiple URLs at once
`/refine`	POST	Opt in prompt refinement
`/stats`	GET	Session statistics

Run Tests

python3 test_all.py           # Layer 1 only (offline)
python3 test_all.py --live    # Layer 1 + Layer 2 (needs internet)

Roadmap

Layer 1: Prompt refiner
Layer 2: Data proxy with caching
MCP server integration
LangChain tool example
Browser fallback (Playwright) for bot blocked sites
Authenticated session management
Layer 3: Output/history compression
CLI tool
Dashboard UI

Requirements

Python 3.10+. No API keys. No GPU.

License

MIT