Mmcp.market

Charlotte MCP server

by TickTockBent·io.github.TickTockBent/charlotte·v0.1.3·179 stars

Renders web pages into structured, agent-readable representations using headless Chromium.

A85/100grade A
What users say
No reviews yet
Be the first
Safety scan
A85/100

full report

Adoption
Established

179 stars581 downloads/wk

Reviews

Write one

Nobody has reviewed Charlotte yet.

If you have run it, two minutes of your experience saves the next person an afternoon.

Charlotte tools

No tool declarations could be read from the package source. They show once the server is installed.

Public scan report

scanner v0.1.10 · 2026-09-28 · same rubric, same numbers if you re-run it

1 medium
  • Code scan67 source files scanned20/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 17 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Findings (1)

  • mediumeval / new Function usedexec.eval
    dist/tools/evaluate.js: … { let result = eval(expr); if (await…
Overall 85/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

Install Charlotte in Claude Code, Cursor or VS Code

Runs npx -y @ticktockbent/charlotte on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add charlotte -- npx -y @ticktockbent/charlotte
Add to Cursor

What the publisher says

From the Charlotte repository's README, as published. We do not edit it. Read it on GitHub

Charlotte

The Web, Readable.

Your AI agent burns ~50,000 characters of accessibility tree just to look at the Hacker News front page. Charlotte does it in 364.

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. On content-heavy pages that orientation is up to ~140x smaller than a full accessibility-tree snapshot from Playwright MCP; on trivially small pages the two are roughly the same size.

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 (364 characters for Hacker News) instead of the full element dump (~50,000 characters). When it needs specifics, it asks for them.

Benchmarks

Measured on Charlotte v0.8.0 against Playwright MCP v0.0.79, by characters returned per tool call on real websites (npx tsx benchmarks/run-benchmarks.ts --suite comparison), 2026-08-08. This section is a summary — the canonical benchmarks page (including per-task cost and release drift) is charlotte.mintlify.site/benchmarks; methodology, instruments, and raw results: benchmarks/.

Orientation cost (what an agent pays to "see" a page on arrival):

A Charlotte navigate returns a usable orientation by default — landmarks, headings, and interactive element counts grouped by page region. To get the equivalent with Playwright MCP, an agent calls browsersnapshot, which returns the full accessibility tree. (Playwright's browsernavigate alone returns only a short confirmation, not the page content, so it isn't a like-for-like comparison.)

The advantage scales with page complexity: on content-heavy pages the structured orientation is ~10–140x smaller than the full snapshot, while on a trivially small page like example.com the two are within ~20% of each other (and on a page that small, the structured representation can be the larger of the two — there is simply nothing to summarize away). Charlotte's value shows up precisely where Playwright's flat dump hurts most. When an agent needs more than the orientation, it calls observe or find for exactly the part it wants instead of paying for the whole tree up front.

Tool definition overhead (invisible cost per API call):

Tool definitions are sent on every API round-trip. With the default browse profile, Charlotte carries ~49% less definition overhead than loading all 43 tools; the minimal core profile cuts it by ~75%. See the profile benchmark report for full results.

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Charlotte: common questions

Is Charlotte MCP server safe?
Yes, by our scan: it is graded A (85/100). Read the Charlotte safety report
How do I install Charlotte?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Charlotte need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is Charlotte maintained?
The last commit was 32 days ago (2026-09-11). The latest release is v0.1.3.
What can I use instead of Charlotte?
Servers from other publishers that do the same job: Robin: AI-Powered Dark Web OSINT MCP server, Agent-Native Plan MCP server and VisualTorch MCP server. Compare all Charlotte alternatives.

Alternatives to Charlotte

Same job from other publishers: the closest match first, then the best rated.

All Charlotte alternatives →
  • Robin: AI-Powered Dark Web OSINT
    Dark web OSINT over Tor: search onion engines, scrape pages, report with your own model.
    A
  • Agent-Native Plan
    Structured visual plans and PR recaps with diagrams, prototypes, annotations, and sharing
    A
  • VisualTorch
    Render PyTorch architecture diagrams and animated GIF reveals from trusted model source.
    A
  • PDF
    Agentic RAG over one PDF or a whole folder: hybrid search, selective page reads, tables, OCR.
    A
  • Google Surf
    Web, academic and code search with graph RAG, data lineage, ontology and cross-project schema links.
    B

More from TickTockBent →