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D3 MCP Server
A TypeScript Express-based Model Context Protocol (MCP) server for D3.js information. This server provides D3 visualization documentation, chart recommendations, and code generation through the MCP protocol.
Features
- 📊 D3 Resources: Access documentation and examples from D3 sources
- 🔍 Chart Recommendation: Get recommendations for the right chart type based on your data and goals
- 💻 Code Generation: Generate D3 code examples for various chart types
- 🔌 MCP Integration: Connect AI tools directly to D3 knowledge
Prerequisites
- Node.js 16.x or higher
- npm or yarn
Installation
- Clone the repository
git clone https://github.com/iamfiscus/mcp-d3-server.git
cd mcp-d3-server
- Install dependencies
npm install
- Build the project
npm run build
Usage
- Start the server
npm start
-
The server will be running at http://localhost:3000 (or the PORT specified in your .env file)
-
Connect to the MCP endpoint at http://localhost:3000/mcp-sse using an MCP client
Development
For development with hot reloading:
npm run dev
Available MCP Resources and Tools
Resources
- d3-docs://{topic}: Access D3 documentation on specific topics
- d3-charts://{chartType}: Get information about specific D3 chart types
Tools
-
generate-d3-chart: Generate D3 code for different chart types
- Parameters: chartType, dataFormat, features (optional)
-
recommend-chart: Get recommendations for D3 chart types based on your data
- Parameters: dataDescription, purpose
Examples
MCP Client Connection Example
import { McpClient } from "@modelcontextprotocol/sdk/client/mcp.js";
import { SseClientTransport } from "@modelcontextprotocol/sdk/client/sse.js";
// Create an MCP client
const client = new McpClient();
// Connect to the D3 MCP server
const transport = new SseClientTransport("http://localhost:3000/sse");
await client.connect(transport);
// Get chart recommendations
const result = await client.invokeTool("recommend-chart", {
dataDescription: "Monthly sales data for 5 product categories over 2 years",
purpose: "Show trends and compare performance across categories"
});
console.log(result.content[0].text);
License
MIT
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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Espejo dehttps: //github.com/agentience/practices_mcp_server
🧑🚀 全世界最好的 llM 资料总结(数据处理、模型训练、模型部署、 O1 模型、 MCP 、小语言模型、视觉语言模型) | Resumen de los mejores recursos del mundo.
Reviews

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