AIDLC Dashboard MCP Tools for Amazon Q integration
Project description
AIDLC MCP Tools
A Model Context Protocol (MCP) server implementation that provides AI agents with tools to interact with the AIDLC Dashboard service. This enables seamless integration between AI tools and project management workflows.
🚀 Features
- Project Management: Create and manage projects through MCP
- Artifact Upload: Upload AI-generated artifacts (epics, user stories, domain models, etc.)
- Progress Tracking: Update project status and track completion
- Health Monitoring: Check service availability and performance
- Batch Operations: Handle multiple artifacts efficiently
- Amazon Q Integration: Optimized for Amazon Q Developer workflows
📋 Requirements
- Python 3.11+
- requests library
- AIDLC Dashboard service running (for integration)
🛠️ Installation
Option 1: Using uv (Recommended)
# Install with uv
uv pip install -e .
# Or install from PyPI (when published)
uv pip install mcp-tools
Option 2: Using pip
# Install in development mode
pip install -e .
# Or install dependencies manually
pip install -r requirements.txt
Option 3: Quick Setup Script
./setup.sh
🌐 Usage
As MCP Server (Amazon Q Integration)
- Configure Amazon Q MCP settings:
{
"mcpServers": {
"aidlc-dashboard": {
"command": "uvx",
"args": ["--from", "aidlc-mcp-tools@latest", "aidlc-mcp-server"],
"env": {
"AIDLC_DASHBOARD_URL": "http://44.253.157.102:8000/api"
}
}
}
}
- Start the MCP server:
aidlc-mcp-server
🔧 Configuration
Environment Variables
| Variable | Description | Default |
|---|---|---|
AIDLC_DASHBOARD_URL |
Dashboard API base URL | http://localhost:8000/api |
AIDLC_TIMEOUT |
Request timeout (seconds) | 30 |
AIDLC_RETRY_ATTEMPTS |
Number of retry attempts | 3 |
AIDLC_LOG_LEVEL |
Logging level | INFO |
📖 Available MCP Tools
1. aidlc_create_project
Create a new project in the AIDLC Dashboard.
Parameters:
name(string): Project name
Example:
{
"name": "create_project",
"arguments": {
"name": "E-commerce Platform"
}
}
2. aidlc_upload_artifact
Upload an artifact to a project.
Parameters:
project_id(string): Project identifierartifact_type(string): Type of artifact (epics, user_stories, domain_model, model_code_plan, ui_code_plan)content(object): Artifact content
Example:
{
"name": "upload_artifact",
"arguments": {
"project_id": "project-123",
"artifact_type": "epics",
"content": {
"title": "User Management",
"description": "Complete user management system",
"priority": "high"
}
}
}
3. aidlc_update_status
Update the status of an artifact.
Parameters:
project_id(string): Project identifierartifact_type(string): Type of artifactstatus(string): New status (not-started, in-progress, completed)
4. aidlc_get_project
Get project details and current status.
Parameters:
project_id(string): Project identifier
5. aidlc_list_projects
List all projects in the dashboard.
Parameters: None
6. aidlc_health_check
Check if the dashboard service is healthy and accessible.
Parameters: None
🎯 Artifact Types and Schemas
Epics
{
"title": "Epic Title",
"description": "Epic description",
"user_stories": ["US-001", "US-002"],
"priority": "high|medium|low",
"acceptance_criteria": ["Criteria 1", "Criteria 2"]
}
User Stories
{
"stories": [
{
"id": "US-001",
"title": "Story Title",
"description": "As a user, I want...",
"acceptance_criteria": ["Criteria 1"],
"priority": "high",
"story_points": 5
}
],
"total_count": 1,
"epics": ["Epic-001"]
}
Domain Model
{
"entities": [
{
"name": "User",
"attributes": ["id", "username", "email"],
"description": "System user entity"
}
],
"relationships": [
{
"from": "User",
"to": "Order",
"type": "one-to-many"
}
]
}
Model Code Plan
{
"components": [
{
"name": "UserService",
"type": "service",
"dependencies": ["UserRepository"]
}
],
"implementation_steps": [
"Create entities",
"Implement repositories"
]
}
UI Code Plan
{
"pages": [
{
"name": "LoginPage",
"route": "/login",
"components": ["LoginForm", "Header"]
}
],
"navigation": {
"type": "SPA",
"router": "React Router"
}
}
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