sfrangulov

Charlotte — AI skill for Claude Code

AI community

Token-efficient browser MCP server — structured web pages for AI agents, not raw accessibility dumps.

How to install Charlotte

This entry records only its repository, not the path inside it, so there is no exact command to give. Open sfrangulov/charlotte and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Charlotte does

Token-efficient browser MCP server — structured web pages for AI agents, not raw accessibility dumps.

Alternatives in AI

AIHawk — Open-source AI browser agent: describe any task in plain language and it autonomously browses, clicks, types a 30.3k ★
Deepseek Pp — DeepSeek Web browser extension: AI agent workspace with MCP tools, memory, Skills, automation, web search, and 1.7k ★
Nocturne Memory — A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents 1.3k ★

README

Charlotte

**The Web, Readable.**

Your AI agent spends 60,000 tokens just to look at a web page. Charlotte does it in 336.

Charlotte is an MCP server that gives AI agents structured, token-efficient access to the web. Instead of dumping the full accessibility tree on every call, Charlotte returns only what the agent needs: a compact page summary on arrival, targeted queries for specific elements, and full detail only when explicitly requested. The result is 25-182x less data per page compared to [Playwright MCP](https://github.com/anthropics/playwright-mcp), saving thousands of dollars across production workloads.

Why Charlotte?

Most browser MCP servers dump the entire accessibility tree on every call — a flat text blob that can exceed a million characters on content-heavy pages. Agents pay for all of it whether they need it or not.

Charlotte decomposes each page into a typed, structured representation — landmarks, headings, interactive elements, forms, content summaries — and lets agents control how much they receive with three detail levels. When an agent navigates to a new page, it gets a compact orientation (336 characters for Hacker News) instead of the full element dump (61,000+ characters). When it needs specifics, it asks for them.

Benchmarks

Charlotte v0.6.2 vs Playwright MCP, measured by characters returned per tool call on real websites:

**Navigation** (first contact with a page):

Site	Charlotte `navigate`	Playwright `browser_navigate`
example.com	612	817
Wikipedia (AI article)	7,667	1,040,636
Hacker News	336	61,230
GitHub repo	3,185	80,297

Charlotte's `navigate` returns minimal detail by default — landmarks, headings, and interactive element counts grouped by page region. Enough to orient, not enough to overwhelm. On Wikipedia, that's **135x smaller** than Playwright's response.

**Tool definition overhead** (invisible cost per API call):

| Profile | Tools | Def. tokens/call | S