Assistant MCP Server

Name: Assistant MCP Server
Author: nodlab

@nodlab

代码仓库

关于 Assistant MCP Server

MCP server for productive development

基本信息

分类

AI 与智能体

许可证

MIT

运行时

node

传输方式

stdio

发布者

nodlab

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-assistant-local": {
      "command": "npx",
      "args": [
        "tsx",
        "/path/to/folder/src/index.ts"
      ],
      "env": {
        "TOOLS_PATH": "/path/to/folder/tools.json"
      }
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Assistant MCP Server?

An MCP server that provides tools for retrieving project architecture information, searching tasks, and generating optimized prompts for AI models. It is designed for developers working on frontend applications who need to integrate structured project data into AI workflows.

How to use Assistant MCP Server?

Install dependencies with yarn install, then build with yarn build. Add a tools.json file defining the available tools. To run locally, configure an MCP client with the command npx tsx /path/to/src/index.ts and set the TOOLS_PATH environment variable to the path of tools.json.

Key features of Assistant MCP Server

Provides three pre‑defined tools: architecture_info, search_tasks, and optimize_prompt.
Uses a tools.json configuration file for tool definitions.
Supports file‑based plugins (e.g., reading architecture info and tasks from local files).
Includes a promptOptimizer plugin for generating final structured AI prompts.
Runs locally via npx tsx for easy development and testing.

Use cases of Assistant MCP Server

Automatically retrieve frontend project architecture before implementing new tasks.
Collect project tasks from a tasks.txt file and guide AI to execute them according to architecture.
Generate a final, structured prompt by combining multiple context sections and instructions for an AI model.

FAQ from Assistant MCP Server

What dependencies are required?

The server requires Node.js and TypeScript (via tsx). Dependencies are installed with yarn install.

How do I configure the tools?

Create a tools.json file at the project root with an array of tool objects. Each tool specifies its name, description, input schema, and plugin configuration (e.g., file or promptOptimizer).

Where is the data stored?

Tool data resides in local files as specified in the plugin arguments (e.g., architecture.md and tasks.txt). The server reads these files at runtime.