mcp-server-chart vs open-computer-use

Side-by-side comparison to help you pick between these two MCP servers.

	mcp-server-chart by antvis	open-computer-use by Wide-Moat
Stars	★ 4,068	★ 74
30d uses	10,239	—
Score	84	49
Official	—	—
Categories	AI / LLM ToolsDeveloper ToolsProductivity	Developer ToolsProductivityAI / LLM Tools
Language	TypeScript	Python
Last commit	this month	this month

mcp-server-chart · Summary

A TypeScript MCP server for generating 26+ visualization charts using AntV, supporting multiple chart types and deployment options.

open-computer-use · Summary

MCP server providing Docker workspaces with browser, terminal, code execution and document skills for any LLM.

mcp-server-chart · Use cases

Data analysts creating visual reports from datasets
AI assistants generating custom charts based on user requests
Web applications embedding visualization capabilities via HTTP API

open-computer-use · Use cases

AI agent web scraping and data analysis with real-time browser interaction
Automated document generation (Word, Excel, PDF) with professional formatting
Code development and testing in isolated environments with full terminal access

mcp-server-chart · Install

Installation

Install globally:

npm install -g @antv/mcp-server-chart

For Desktop Apps (e.g., Claude Desktop, VSCode):

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "npx",
      "args": ["-y", "@antv/mcp-server-chart"]
    }
  }
}

For Windows:

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "@antv/mcp-server-chart"]
    }
  }
}

open-computer-use · Install

Installation

git clone https://github.com/Wide-Moat/open-computer-use.git
cd open-computer-use
cp .env.example .env
# Edit .env — set OPENAI_API_KEY (or any OpenAI-compatible provider)

# 1. Start Computer Use Server (builds workspace image on first run, ~15 min)
docker compose up --build

# 2. Start Open WebUI (in another terminal)
docker compose -f docker-compose.webui.yml up --build

For MCP client integration, connect to http://localhost:8081/mcp for self-hosted or https://api.yambr.com/mcp/computer_use with API key for hosted version.

Comparison generated from public README + GitHub signals. Last updated automatically.