Weekly Planning Assistant MCP
# Weekly Planning Assistant MCP
> An intelligent MCP server that turns your Google Calendar into actionable weekly insights through natural conversation.
[](https://modelcontextprotocol.io)
[](https://www.python.org/)
[](LICENSE)
## What It Does
Ask Claude natural questions about your schedule and get intelligent analysis:
- **"What does my week look like?"** ā Day-by-day breakdown with meeting hours, office days, and free time
- **"When should I book boxing classes?"** ā Ranked workout time suggestions based on your actual schedule
- **"Which days am I going to the office?"** ā Office day detection with commute warnings
- **"Do I have any scheduling problems?"** ā Identifies conflicts, missing lunch breaks, and marathon meeting blocks
## How It Works
```mermaid
graph LR
A[You ask Claude] --> B[Weekly Planner MCP]
B --> C[Google Calendar MCP]
C --> D[Your Google Calendar]
D --> C
C --> B
B --> A
```
This server demonstrates **MCP chaining** - it acts as both a server (to Claude) and a client (to Google Calendar MCP), providing domain-specific intelligence on top of raw calendar data.
## Features
### šļø **Week Analysis**
- Office vs. WFH day detection
- Total meeting hours per day
- Free time gaps (>1 hour)
- Back-to-back meeting blocks
- Busiest/lightest day identification
### ā ļø **Danger Zone Detection**
- Missing lunch breaks (meetings 12-2pm)
- Marathon meetings (3+ hours straight)
- Days with zero free time
- Calendar conflicts and overlaps
### š **Workout Planning**
- Quality-scored time slot suggestions
- Preference-based filtering (morning/lunch/evening)
- Commute-aware recommendations
- Duration-based slot matching
### š **Commute Planning**
- Office day detection via keywords
- Earliest arrival time calculation
- Early morning gym warnings
- Multi-calendar support
## Installation
### Prerequisites
- **Python 3.11+** - [Download](https://www.python.org/downloads/)
- **Node.js** - [Download](https://nodejs.org/) (for Google Calendar MCP)
- **Claude Desktop** - [Download](https://claude.ai/download)
- **Google Account** with Calendar access
### 1. Clone and Install
```bash
# Clone the repository
git clone https://github.com/yourusername/weekly-planner-mcp.git
cd weekly-planner-mcp
# Install uv (Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
# Install dependencies
uv sync
```
### 2. Configure Settings
```bash
# Copy example configuration
cp config.example.json config.json
# Edit with your preferences
nano config.json
```
Example `config.json`:
```json
{
"calendar_ids": ["primary"],
"office_keywords": ["office", "HQ"],
"work_hours": {
"start": "09:00",
"end": "18:00"
},
"preferred_workout_times": ["morning", "lunch"],
"min_workout_duration": 60
}
```
### 3. Set Up Google Calendar OAuth
#### A. Create Google Cloud Project
1. Go to [Google Cloud Console](https://console.cloud.google.com/)
2. Click **"New Project"**
3. Name it (e.g., "Weekly Planner")
4. Click **"Create"**
#### B. Enable Calendar API
1. Search for **"Google Calendar API"**
2. Click **"Enable"**
#### C. Configure OAuth Consent Screen
1. Go to **"APIs & Services"** ā **"OAuth consent screen"**
2. Select **"External"** user type
3. Fill in app name and your email
4. Click **"Save and Continue"** through all steps
5. Under **"Test users"**, click **"+ ADD USERS"**
6. Add your Gmail address
#### D. Create OAuth Credentials
1. Go to **"Credentials"** ā **"Create Credentials"** ā **"OAuth client ID"**
2. Application type: **"Desktop app"** ā ļø (NOT Web application!)
3. Name: "Weekly Planner Desktop"
4. Click **"Create"** and **"Download JSON"**
#### E. Save and Authenticate
```bash
# Save credentials
mkdir -p ~/.config/google-calendar-mcp
mv ~/Downloads/client_secret_*.json ~/.config/google-calendar-mcp/gcp-oauth.keys.json
# Authenticate
export GOOGLE_OAUTH_CREDENTIALS="$HOME/.config/google-calendar-mcp/gcp-oauth.keys.json"
npx -y @cocal/google-calendar-mcp auth
```
Your browser will open - sign in and grant calendar permissions.
### 4. Add to Claude Desktop
Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"weekly-planner": {
"command": "/Users/yourusername/.local/bin/uv",
"args": [
"--directory",
"/absolute/path/to/weekly-planner-mcp",
"run",
"python",
"-m",
"weekly_planner",
"--stdio"
],
"env": {
"GOOGLE_OAUTH_CREDENTIALS": "/Users/yourusername/.config/google-calendar-mcp/gcp-oauth.keys.json"
}
}
}
}
```
**Replace:**
- `/Users/yourusername/` with your actual home directory (run `echo $HOME`)
- `/absolute/path/to/weekly-planner-mcp` with full path to this project
### 5. Restart Claude Desktop
Quit Claude Desktop (ā+Q) and reopen. Test with:
```
"What does my week look like?"
```
## Configuration
### Basic Settings
Edit `config.json` to customize behavior:
```json
{
"calendar_ids": ["primary"], // Which calendars to analyze
"office_keywords": ["office", "HQ"], // Keywords for office detection
"work_hours": {
"start": "09:00",
"end": "18:00"
},
"preferred_workout_times": ["morning", "lunch", "evening"],
"min_workout_duration": 60 // Minutes
}
```
### Adding Multiple Calendars
To include work calendars or imported calendars, first list your available calendars, then add their IDs to `config.json`.
See the full command in the Development section below for listing calendars.
## Architecture
### Project Structure
```
weekly-planner-mcp/
āāā src/weekly_planner/
ā āāā server.py # FastMCP server with 4 tools
ā āāā gcal_client.py # Google Calendar MCP client
ā āāā analysis.py # Core scheduling analysis logic
ā āāā models.py # Pydantic models for structured output
ā āāā config.py # Configuration management
āāā config.json # Your configuration
āāā config.example.json # Example configuration
āāā pyproject.toml # Dependencies
```
### Tools Provided
1. **`analyze_week`** - Comprehensive weekly overview with day-by-day breakdown
2. **`find_danger_zones`** - Identifies scheduling problems and conflicts
3. **`suggest_workout_slots`** - Intelligent workout time suggestions with quality scoring
4. **`check_commute_requirements`** - Office day detection with commute planning
All tools return structured Pydantic models for type-safe, validated responses.
## Example Usage
**Week Overview:**
```
You: What does my week look like?
Claude: Here's your week breakdown:
Monday (4.5h meetings)
⢠Office day detected
⢠Free slots: 9:00-10:00, 14:00-16:00
⢠3-hour back-to-back block in morning
Tuesday (2h meetings)
⢠Lightest day this week
⢠Large gap: 11:00-17:00
```
**Workout Planning:**
```
You: When should I book a 60-minute workout this week?
Claude: Best times ranked by quality:
1. Tuesday 11:00-12:00 (Score: 8.5/10)
⢠Long gap available
⢠Lunch time slot
⢠Not near office days
2. Friday 18:30-19:30 (Score: 7.8/10)
⢠Evening slot
⢠After work hours
```
**Office Days:**
```
You: Which days am I going to the office?
Claude: You have 2 office days this week:
⢠Monday: Office all day (earliest: 9:30am)
ā ļø Early start - avoid morning gym
⢠Thursday: Afternoon in office (earliest: 2:00pm)
ā Morning free for workout
```
## Development
### List Available Calendars
```bash
cd /path/to/weekly-planner-mcp
export GOOGLE_OAUTH_CREDENTIALS="$HOME/.config/google-calendar-mcp/gcp-oauth.keys.json"
uv run python -c "
import asyncio, os
os.environ['GOOGLE_OAUTH_CREDENTIALS'] = os.path.expanduser('~/.config/google-calendar-mcp/gcp-oauth.keys.json')
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
async def list_cals():
params = StdioServerParameters('npx', ['-y', '@cocal/google-calendar-mcp', 'start'], env=os.environ.copy())
async with stdio_client(params) as (r, w):
async with ClientSession(r, w) as s:
await s.initialize()
result = await s.call_tool('list-calendars', {})
import json
for cal in json.loads(result.content[0].text)['calendars']:
print(f'{cal[\"summary\"]}: {cal[\"id\"]}')
asyncio.run(list_cals())
"
```
Add calendar IDs to `config.json` under `calendar_ids`.
### Check Logs
```bash
# Claude Desktop logs
tail -f ~/Library/Logs/Claude/mcp-server-weekly-planner.log
# Re-authenticate if needed
npx -y @cocal/google-calendar-mcp auth
```
## Troubleshooting
### "OAuth credentials not found"
- Verify: `ls -la ~/.config/google-calendar-mcp/gcp-oauth.keys.json`
- Ensure `GOOGLE_OAUTH_CREDENTIALS` is set in Claude Desktop config
### "No events returned"
- Re-authenticate: `npx -y @cocal/google-calendar-mcp auth`
- Verify calendar IDs in `config.json`
- Check logs: `tail -f ~/Library/Logs/Claude/mcp-server-weekly-planner.log`
### "Access denied" during OAuth
- Add yourself as a test user in Google Cloud Console
- Use the exact Gmail address you added
- Application type must be "Desktop app" not "Web application"
### "Token expired"
```bash
export GOOGLE_OAUTH_CREDENTIALS="$HOME/.config/google-calendar-mcp/gcp-oauth.keys.json"
npx -y @cocal/google-calendar-mcp auth
```
## What This Demonstrates
This project showcases key MCP concepts:
- **MCP Chaining** - One MCP server calling another MCP server
- **Structured Output** - Type-safe responses using Pydantic models
- **Domain Abstractions** - High-level analysis tools built on generic calendar API
- **Configuration Management** - File-based and environment variable configuration
- **FastMCP Usage** - Modern MCP server implementation with decorators
- **OAuth Integration** - Secure Google Calendar API authentication
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: weekly overview, problem detection, workout scheduling, and commute detection. Overlap is minimal (check_commute_requirements focuses on office days, while analyze_week includes office days as part of a broader analysis, but descriptions clearly differentiate).
All tool names follow a consistent verb_noun pattern: analyze_week, find_danger_zones, suggest_workout_slots, check_commute_requirements. No deviations in style or casing.
Four tools are well-scoped for a weekly planning assistant, covering analysis, conflict detection, scheduling suggestions, and commute needs. Each tool earns its place without redundancy.
The surface covers key planning tasks: analysis, conflict detection, workout suggestions, and commute checks. However, it lacks tools for modifying the calendar (e.g., creating events) or deeper integration like travel time estimation, which could be minor gaps for a planning assistant.