Amazing Marvin Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Amazing Marvin Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Connect your Amazing Marvin productivity system with AI assistants for smarter task management
<a href="https://youtu.be/xdB8DevqTik"> <img src="https://github.com/user-attachments/assets/d073cba7-3bdd-42de-ad11-37de7a2e752a" width="600"/> </a>
This connects your Amazing Marvin productivity system with AI assistants like Claude, Cursor, and others. Instead of manually copying your tasks and projects into chat, your AI assistant can see and help with your actual Amazing Marvin data.
Easy way (Smithery):
npx -y @smithery/cli mcp add @bgheneti/amazing-marvin-mcp --client claudePaste the API key when prompted
Alternative (pip):
pip install amazing-marvin-mcpThen add to your AI client config (see installation guide)
Ask your AI: "What tasks do I have today?"
🎉 That's it! Your AI can now see your Amazing Marvin data.
Once connected, your AI assistant becomes your personal productivity coach with access to your real Amazing Marvin data:
"What should I focus on today?" - Get personalized recommendations based on your actual deadlines and priorities
"I'm feeling overwhelmed - what's most important?" - AI helps you cut through the noise and identify what really matters
"How is my website redesign project going?" - See progress, completed tasks, and what's left to do
"Show me everything related to client work this week" - Get organized views of your tasks by project or category
"What did I accomplish this week?" - Review your productivity patterns and celebrate wins
"Which days am I most productive?" - Understand your patterns to plan better
"What's overdue and needs attention?" - Never lose track of important deadlines
"Help me plan tomorrow based on what I have scheduled" - Get realistic daily plans that work
"Start tracking time on this task" - Seamlessly manage time tracking from your AI chat
"What have I been working on today?" - Review your time allocation and focus
Why this is better than generic productivity advice: Your AI sees your actual tasks, deadlines, and progress - so the help you get is personalized to your real situation, not generic tips.
Note: This covers most Amazing Marvin features, though some advanced customizations and strategies have limited API access.
npx -y @smithery/cli install @bgheneti/amazing-marvin-mcp --client claudeVisit Smithery Registry for other clients.
Why choose this option:
pip install --upgrade amazing-marvin-mcp#### Prerequisites
#### Installation
# Install from PyPI (recommended)
pip install amazing-marvin-mcp#### 📱 Client Configuration
<details> <summary>🖥️ Claude Desktop</summary>
Add to your claude_desktop_config.json:
📍 Config file locations:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"amazing-marvin": {
"command": "python",
"args": ["-m", "amazing_marvin_mcp"],
"env": {
"AMAZING_MARVIN_API_KEY": "your-api-key-here"
}
}
}
}</details>
<details> <summary>🎯 Cursor</summary>
Add to your MCP settings:
{
"mcpServers": {
"amazing-marvin": {
"command": "python",
"args": ["-m", "amazing_marvin_mcp"],
"env": {
"AMAZING_MARVIN_API_KEY": "your-api-key-here"
}
}
}
}</details>
<details> <summary>💨 Windsurf</summary>
Add to your Windsurf MCP configuration:
{
"mcpServers": {
"amazing-marvin": {
"command": "python",
"args": ["-m", "amazing_marvin_mcp"],
"env": {
"AMAZING_MARVIN_API_KEY": "your-api-key-here"
}
}
}
}</details>
<details> <summary>🆚 VS Code</summary>
Add to your VS Code MCP configuration:
{
"mcpServers": {
"amazing-marvin": {
"command": "python",
"args": ["-m", "amazing_marvin_mcp"],
"env": {
"AMAZING_MARVIN_API_KEY": "your-api-key-here"
}
}
}
}</details>
The MCP provides specific tools that your AI can use. Simply ask your AI to help with productivity tasks and it will use the appropriate tools:
| What you might ask | Tools the AI will use |
|---|---|
| "What should I focus on today?" | get_daily_productivity_overview() |
| "What tasks do I have today?" | get_daily_productivity_overview() or get_tasks() |
| "Show me my projects" | get_projects() |
| "What's overdue?" | get_due_items() or get_daily_productivity_overview() |
| "Create a new task for X" | create_task() |
| "Mark task Y as done" | mark_task_done() |
| "Start tracking time on this" | start_time_tracking() |
Your AI assistant automatically understands your Amazing Marvin structure:
No need to explain your system - your AI just gets it!
<details> <summary><strong>"API key not found" error</strong></summary>
Problem: The MCP can't find your API key.
Solutions:
echo $AMAZING_MARVIN_API_KEY</details>
<details> <summary><strong>"Connection refused" or timeout errors</strong></summary>
Problem: Can't connect to Amazing Marvin API.
Solutions:
python -c "import requests; print(requests.get('https://serv.amazingmarvin.com/api').status_code)"</details>
<details> <summary><strong>AI says "I don't see any Amazing Marvin data"</strong></summary>
Problem: MCP is running but not returning data.
Solutions:
</details>
<details> <summary><strong>Python module not found</strong></summary>
Problem: ModuleNotFoundError: No module named 'amazing_marvin_mcp'
Solutions:
pip install --force-reinstall amazing-marvin-mcppython -c "import sys; print(sys.path)"which python and use that in your config</details>
<details> <summary><strong>Is my data safe?</strong></summary>
Absolutely! Your Amazing Marvin data stays between you, Amazing Marvin, and your AI assistant. The connection runs on your computer - nothing is stored on external servers or shared with anyone else. </details>
<details> <summary><strong>Which AI assistants work with this?</strong></summary>
Any AI assistant that supports the Model Context Protocol, including Claude Desktop, Cursor, VS Code, and Windsurf. More are being added regularly. </details>
<details> <summary><strong>What can my AI assistant see?</strong></summary>
Your AI can see:
Basically everything you see in Amazing Marvin, your AI can see too. </details>
<details> <summary><strong>Will this make my AI assistant slower?</strong></summary>
Not noticeably. The system fetches your data from Amazing Marvin when you ask productivity questions. Response time depends on your internet connection, but it's usually very quick. </details>
<details> <summary><strong>Can my AI assistant change my tasks?</strong></summary>
Yes, if you ask it to! Your AI can:
Don't worry - it only makes changes when you specifically ask it to. </details>
<details> <summary><strong>Can I see completed tasks?</strong></summary>
Yes! The MCP can find and display completed tasks in several ways:
📊 In Daily Focus View:
📁 In Project Overviews:
🔍 Efficient Historical Access:
</details>
<details> <summary><strong>What can't the MCP do?</strong></summary>
🚫 Cannot Delete or Remove:
📝 Cannot Edit:
📚 Limited Access:
For these operations, use the Amazing Marvin app directly. </details>
<details> <summary><strong>How often is the data updated?</strong></summary>
Data is fetched in real-time with each request to Amazing Marvin's API. There's no background syncing or caching - you always get the most current data from your Amazing Marvin account. </details>
git clone https://github.com/bgheneti/Amazing-Marvin-MCP.git
cd Amazing-Marvin-MCP
pip install -e ".[dev]"
pre-commit installOption A: Environment variable
export AMAZING_MARVIN_API_KEY="your-api-key-here"Option B: Create a `.env` file
AMAZING_MARVIN_API_KEY=your-api-key-herepytest tests/ -v⚠️ Note: Tests create temporary items in your Amazing Marvin account with [TEST] prefixes. These may need manual cleanup due to API limitations.
# Run all checks
pre-commit run --all-files
# Individual tools
ruff check . # Linting
ruff format . # Formatting
mypy . # Type checking
pytest tests/ # TestsThe MCP provides 28 comprehensive tools to AI assistants:
📖 Read Operations:
get_daily_productivity_overview() - PRIMARY comprehensive daily view (today's tasks, overdue, completed, planning insights)get_tasks() - Today's scheduled items onlyget_projects() - All projectsget_categories() - All categoriesget_due_items() - Overdue/due items onlyparent_id: str, recursive: bool = False )` - Subtasks of a parent task/project
label: str = None )` - Find all tasks with optional label filter (comprehensive search)
get_labels() - Task labelsget_goals() - Goals and objectivesget_account_info() - Account detailsget_completed_tasks() - Completed items with date categorization (defaults to past 7 days)date: str )` - Completed items for specific date (YYYY-MM-DD format)
days: int = 7, start_date: str = None, end_date: str = None )` - Flexible productivity analytics
get_currently_tracked_item() - Active time tracking✏️ Write Operations:
title: str, project_id: str = None, category_id: str = None, due_date: str = None, note: str = None )` - Create new tasks
item_id: str, timezone_offset: int = 0 )` - Complete tasks
title: str, project_type: str = "project" )` - Create new projects
task_id: str )` - Begin time tracking
task_id: str )` - End time tracking
task_ids: list[str] )` - Complete multiple tasks
task_list: list[str], project_id: str = None, category_id: str = None )` - Create multiple tasks
points: int, item_id: str, date: str )` - Claim kudos points
get_kudos_info() - Get reward system and kudos information🔧 Utility Operations:
test_api_connection() - Verify API connectivityproject_id: str )` - Project analytics
get_daily_focus() - Daily prioritiesget_productivity_summary() - Performance metricstime_tracking_summary() - Time analyticsquick_daily_planning() - Planning assistanceproject_title: str, task_titles: list[str], project_type: str = "project" )` - Project setup
task_ids: list[str] )` - Time tracking history
This project uses automated publishing to PyPI via GitHub Actions.
For maintainers:
# Make your changes and test them
pytest tests/ -v
ruff check src/
mypy src/amazing_marvin_mcp/
# Use the release script to bump version and create tag
python scripts/release.py patch # for bug fixes
python scripts/release.py minor # for new features
python scripts/release.py major # for breaking changes
# Push to trigger CI and PyPI publish
git push origin main
git push origin v1.x.xThe workflow:
git clone https://github.com/bgheneti/Amazing-Marvin-MCP.git
cd Amazing-Marvin-MCP
pip install -e ".[dev]"
pre-commit installYou can also manually publish to Test PyPI by running the workflow manually on GitHub.
What to expect:
Technical details:
MIT License - see LICENSE for details.
<div align="center">
Made with ❤️ for Amazing Marvin users
Report Issues • Suggest Improvements • Star on GitHub
</div>
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.