mcp-server-chart vs gptr-mcp

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

	mcp-server-chart by antvis	gptr-mcp by assafelovic
Stars	★ 4,068	★ 346
30d uses	10,239	—
Score	84	47
Official	—	—
Categories	AI / LLM ToolsDeveloper ToolsProductivity	SearchAI / LLM ToolsProductivity
Language	TypeScript	Python
Last commit	this month	6 mo ago

mcp-server-chart · Summary

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

gptr-mcp · Summary

MCP server for deep research, providing comprehensive web search and report generation capabilities.

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

gptr-mcp · Use cases

Investment research and analysis
Competitive intelligence gathering
Academic research support
Market trend analysis
Content creation with verified sources

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"]
    }
  }
}

gptr-mcp · Install

Installation

Clone the repository:

git clone https://github.com/assafelovic/gpt-researcher.git
cd gpt-researcher/cd gptr-mcp

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

cp .env.example .env
# Edit .env with your API keys

Claude Desktop Configuration

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "gptr-mcp": {
      "command": "python",
      "args": ["/absolute/path/to/gptr-mcp/server.py"],
      "env": {
        "OPENAI_API_KEY": "your-openai-key-here",
        "TAVILY_API_KEY": "your-tavily-key-here"
      }
    }
  }
}

Running the Server

Directly: python server.py
With Docker: docker-compose up -d

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