mcp-server-chart vs mcp-agent-langchainjs
Side-by-side comparison to help you pick between these two MCP servers.
mcp-server-chart by antvis | mcp-agent-langchainjs by Azure-Samples | |
|---|---|---|
| Stars | ★ 4,068 | ★ 183 |
| 30d uses | 10,239 | — |
| Score | 84 | 48 |
| Official | — | — |
| Categories | AI / LLM ToolsDeveloper ToolsProductivity | AI / LLM ToolsE-commerceDeveloper Tools |
| Language | TypeScript | TypeScript |
| Last commit | this month | 1 mo ago |
mcp-server-chart · Summary
A TypeScript MCP server for generating 26+ visualization charts using AntV, supporting multiple chart types and deployment options.
mcp-agent-langchainjs · Summary
A burger ordering AI agent system using LangChain.js and MCP servers to interact with a restaurant API.
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
mcp-agent-langchainjs · Use cases
- AI-powered food ordering system through natural language conversation
- Demonstration of MCP tool calling for real-world applications
- Serverless architecture for scalable AI agents
mcp-server-chart · Install
Installation
Install globally:
npm install -g @antv/mcp-server-chartFor 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"]
}
}
}mcp-agent-langchainjs · Install
Installation Steps
- **GitHub Codespaces (Recommended)**:
- Open the project in [GitHub Codespaces](https://codespaces.new/Azure-Samples/mcp-agent-langchainjs?hide_repo_select=true&ref=main&quickstart=true) for a preconfigured environment
- **Local Development**:
- Clone the repository: git clone <your-repo-url> - Install Node.js LTS - Install Azure Developer CLI 1.19+ - For local testing with Ollama: ``bash ollama pull qwen3:8b ` Create a .env file with: `env AZURE_OPENAI_API_ENDPOINT="http://localhost:11434/v1" AZURE_OPENAI_MODEL="qwen3:8b" AZURE_OPENAI_API_KEY="__not_used__" `` - Start the application following the README instructions
- **Deployment to Azure**:
- Run azd auth login - Run azd up to deploy all services
**Claude Desktop Configuration** (add to claude_desktop_config.json):
{
"mcpServers": {
"burger-mcp": {
"command": "node",
"args": ["packages/burger-mcp/dist/server.js"]
}
}
}