tradingview-chart-mcp vs memory

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

	tradingview-chart-mcp by ertugrul59	memory by modelcontextprotocol
Stars	★ 93	★ 85,748
30d uses	—	—
Score	47	77
Official	—	✓
Categories	Web ScrapingFinanceAI / LLM Tools	Knowledge GraphAI / LLM ToolsProductivity
Language	Python	TypeScript
Last commit	2 mo ago	this month

tradingview-chart-mcp · Summary

MCP server that captures TradingView chart images via Selenium with browser pooling for concurrent performance.

memory · Summary

An MCP server implementing persistent memory using a local knowledge graph for AI models to remember user information across chats.

tradingview-chart-mcp · Use cases

Financial analysis platforms that need to display real-time TradingView charts
AI assistants providing market insights with visual chart data
Automated trading systems requiring chart image capture

memory · Use cases

Personalizing AI assistant interactions by remembering user preferences, history, and relationships
Building context-aware chat applications that maintain conversation history
Creating knowledge bases that persist across AI model sessions

tradingview-chart-mcp · Install

Installation

Clone the repository:

git clone https://github.com/ertugrul59/tradingview-chart-mcp
cd tradingview-chart-mcp

Create and activate virtual environment:

python3 -m venv .venv
source .venv/bin/activate  # macOS/Linux
.venv\Scripts\activate  # Windows

Install dependencies:

pip install -r requirements.txt

Configure environment variables:

Copy .env.example to .env
Add your TradingView credentials (TRADINGVIEW_SESSION_ID and TRADINGVIEW_SESSION_ID_SIGN)

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "tradingview-chart-mcp": {
      "command": "/path/to/tradingview-chart-mcp/.venv/bin/python3",
      "args": ["/path/to/tradingview-chart-mcp/main.py"],
      "env": {
        "TRADINGVIEW_SESSION_ID": "YOUR_SESSION_ID_HERE",
        "TRADINGVIEW_SESSION_ID_SIGN": "YOUR_SESSION_ID_SIGN_HERE"
      }
    }
  }
}

Run the server:

python main_optimized.py  # Production with browser pooling
# or
python main.py  # Legacy version

memory · Install

Installation

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ]
    }
  }
}

VS Code

Use one-click installation buttons or manually configure in .vscode/mcp.json:

{
  "servers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ]
    }
  }
}

Docker

{
  "mcpServers": {
    "memory": {
      "command": "docker",
      "args": ["run", "-i", "-v", "claude-memory:/app/dist", "--rm", "mcp/memory"]
    }
  }
}

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