Skip to main content

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)

  1. 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"
      }
    }
  }
}
  1. 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 identifier
  • artifact_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 identifier
  • artifact_type (string): Type of artifact
  • status (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"
  }
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aidlc_mcp_tools-1.0.5.tar.gz (20.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

aidlc_mcp_tools-1.0.5-py3-none-any.whl (12.5 kB view details)

Uploaded Python 3

File details

Details for the file aidlc_mcp_tools-1.0.5.tar.gz.

File metadata

  • Download URL: aidlc_mcp_tools-1.0.5.tar.gz
  • Upload date:
  • Size: 20.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for aidlc_mcp_tools-1.0.5.tar.gz
Algorithm Hash digest
SHA256 7faedc872bc669b539506cf8a43e466b530777b09000d6a021e6bf8538b992ac
MD5 d5ca6c0f9bc929316056506e07505705
BLAKE2b-256 3b2e5626a17e46fd7cdfc67677b8faa5b24929a91db98fd54d3a33fe8f2e0342

See more details on using hashes here.

File details

Details for the file aidlc_mcp_tools-1.0.5-py3-none-any.whl.

File metadata

File hashes

Hashes for aidlc_mcp_tools-1.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 e2dad53e7246554b5b242851e9f2afb4133e7793f2d805d31e4ed77849b8c9a2
MD5 b2f5f437ca40b4ad785f3adacafcbd57
BLAKE2b-256 f4b63cabab6e5020df4b59d97f05d8f320304c624497d3be878a654ccf911e24

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page