Cursor Plan Executor
A Model Context Protocol server for Cursor that manages and executes task plans with SQLite3-based CRUD operations, status tracking, and workflow tools.
README
Cursor Plan Executor
A Model Context Protocol (MCP) server for Cursor that helps manage and execute task plans with SQLite3 database management tools.
Features
- Plan Management: Complete CRUD operations for task plan steps
- SQLite3 Database: Persistent storage for plan management
- Status Tracking: Track completion status of plan steps
- Execution Data: Store progress notes and results for each step
- Workflow Tools: Streamlined tools for plan execution workflow
Database Schema
The server includes a SQLite3 database with the following table:
CREATE TABLE plan (
step INTEGER PRIMARY KEY AUTOINCREMENT,
instructions VARCHAR(10000) NOT NULL,
status TINYINT DEFAULT 0 CHECK (status IN (0, 1)),
execution_data TEXT
);
Installation
Global Installation (Recommended for Cursor integration)
npm install -g .
Local Development
npm install
npm run build
Usage
As a Global Command
After installing globally, you can run:
cursor-plan-executor
With npx
npx cursor-plan-executor
Direct Execution
node dist/index.js
Cursor Integration
Configuration
Add the following to your Cursor settings:
{
"mcp": {
"servers": {
"cursor-plan-executor": {
"command": "cursor-plan-executor",
"args": [],
"env": {}
}
}
}
}
Or use the alternative format:
{
"mcpServers": {
"cursor-plan-executor": {
"command": "cursor-plan-executor",
"args": [],
"env": {}
}
}
}
Available Tools
initialize_plan
Initialize a new task with your plan. Call this tool whenever you start a new task and already have the plan for it. Define all separate steps in the plan. Do not initialize a plan twice, as once initialized it will delete all previous plan data. Create the plan for the whole task.
Parameters:
plan_steps(required): Array of plan steps (instructions) for the task
Example Usage:
{
"name": "initialize_plan",
"arguments": {
"plan_steps": [
"Create a new React component",
"Add state management",
"Implement the UI",
"Add tests",
"Deploy the component"
]
}
}
show_all_steps
Show all plan steps in a readable format with status indicators.
Parameters: None
Example Usage:
{
"name": "show_all_steps",
"arguments": {}
}
Response:
All Plan Steps:
1. ⏳ PENDING - Create a new React component
2. ✅ DONE - Add state management
Execution Data: Redux store configured successfully
3. ⏳ PENDING - Implement the UI
get_next_step
Get the next pending step that you should follow.
Parameters: None
Example Usage:
{
"name": "get_next_step",
"arguments": {}
}
Response:
Next step to follow: 1 - Create a new React component
get_current_validation_criteria
Get the validation criteria for the currently unfinished task. This will list the validation criteria for the next pending step.
Parameters: None
Example Usage:
{
"name": "get_current_validation_criteria",
"arguments": {}
}
Response:
Current unfinished task:
Step 1 - Create a new React component
Validation Criteria: Component should have proper TypeScript types and error handling
Response (when no validation criteria):
Current unfinished task:
Step 1 - Create a new React component
No validation criteria specified for this step.
mark_current_step_done
Mark the current (next pending) step as done.
Parameters: None
Example Usage:
{
"name": "mark_current_step_done",
"arguments": {}
}
Response:
Step 1 marked as done: Create a new React component
mark_step_as_not_done
Mark a specific step as not done (pending). Call this tool when you need to revert a completed step back to pending status.
Parameters:
step(required): Step number to mark as not done
Example Usage:
{
"name": "mark_step_as_not_done",
"arguments": {
"step": 2
}
}
update_step_data
Update the instructions for a specific step by step ID.
Parameters:
step(required): Step number to updateinstructions(required): New instructions for the step
Example Usage:
{
"name": "update_step_data",
"arguments": {
"step": 1,
"instructions": "Create a new React component with TypeScript"
}
}
update_step_execution_data
Update the execution data for a plan step (useful for tracking progress and notes).
Parameters:
step(required): Step number to updateexecution_data(required): Execution data to store (progress notes, results, etc.)
Example Usage:
{
"name": "update_step_execution_data",
"arguments": {
"step": 1,
"execution_data": "Component created successfully with proper TypeScript types"
}
}
get_plan_step
Get a specific plan step by step number with full details.
Parameters:
step(required): Step number to retrieve
Example Usage:
{
"name": "get_plan_step",
"arguments": {
"step": 1
}
}
delete_plan_step
Delete a plan step by step number.
Parameters:
step(required): Step number to delete
Example Usage:
{
"name": "delete_plan_step",
"arguments": {
"step": 1
}
}
clear_all_plan_steps
Clear all plan steps from the database (use with caution).
Parameters: None
Example Usage:
{
"name": "clear_all_plan_steps",
"arguments": {}
}
Typical Workflow
- Initialize Plan: Use
initialize_planto set up your complete task plan - Review Steps: Use
show_all_stepsto see the current status - Get Next Step: Use
get_next_stepto see what to do next - Complete Step: Use
mark_current_step_donewhen finished with a step - Add Notes: Use
update_step_execution_datato add progress notes (optional) - Repeat: Continue steps 3-5 until all steps are complete
Development
Prerequisites
- Node.js 18 or higher
- npm
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。