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Boost Your Agents with MCPs - Productivity Customized Tools
NORTH AMERICA
πŸ‡ΊπŸ‡Έ United Statesβ€’March 22, 2026

Boost Your Agents with MCPs - Productivity Customized Tools

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Originally published byDev.to

A. Features

a. Calendar Integration

Function Description Intelligence
create_event Schedule new meetings/appointments Conflict detection, optimal time suggestions
list_events View calendar for specific dates Smart filtering, availability analysis
check_availability Find free time slots Multi-day search, duration-aware
delete_event Remove scheduled events Cascade handling, notification support

b. Weather Intelligence

Function Description Intelligence
get_alerts Get weather alerts for US states Real-time severe weather notifications
get_forecast Multi-day weather predictions Official NWS forecasts with detailed periods
get_current_weather Current observation data Real-time conditions from weather stations

c. Analyzer

Function Description Intelligence
add Add two numbers together Total
subtract Subtract second number from first number Comparison
multiply Multiply two numbers together multiplication
divide Divide first number by second number Percentage
power power of a number Repitition
factorial factorial of positive number Is It A Prime Number?

B. Testing

a. Calendar

1. Natural Language Queries

Try these conversational requests:

Query Expected Behavior
"Schedule a team meeting tomorrow at 2pm" Creates event, checks conflicts
"Do I have anything on Friday afternoon?" Lists events for specified time
"Find me 30 minutes free time this week" Suggests available slots
"Cancel my 3pm meeting today" Finds and removes matching event

2. Complex Scenarios

The AI agent can handle complex requests as well:

I need to schedule a 90-minute project review with the team. I'm free Tuesday and Wednesday afternoons. What works best?
Agent Process:
1. Calls check_availability() for Tuesday/Wednesday afternoons
2. Finds optimal 90-minute slots
3. Suggests best options based on preferences
4. Creates event when user confirms

3. Error Handling

The calendar server handles various edge cases:

Error Type Handling User Experience
Invalid Date Format Parse error with format help "Please use format: YYYY-MM-DD HH:MM"
Past Date Scheduling Validation with warning "Cannot schedule events in the past"
File Corruption Backup and recovery Graceful fallback to empty calendar
Concurrent Access File locking Prevents data corruption

b. Weather

1. Natural Language Queries

Try these conversational weather requests:

Query Expected Behavior
"Get current weather for Seattle" Current conditions from nearest station
"Show me the forecast for San Francisco" 5-day detailed forecast periods
"Are there any weather alerts in California?" Active severe weather alerts for CA
"What's the weather like in New York?" Current observations with temperature

2. Error Handling

The server handles various failure modes gracefully:

  • Network timeouts: 30-second timeout prevents hanging
  • Invalid coordinates: Clear error messages for bad inputs
  • API unavailable: Graceful degradation with error messages
  • Missing data: Handles null/missing fields safely
  • State code validation: Validates US state codes for alerts

c. Analyzer

1. Natural Language Queries (Natural Language β†’ Tool Mapping)

Query Expected Behavior
"add 25 and 17" β†’ add(x=25, y=17)
"25 plus 17" β†’ add(x=25, y=17)
"what's 25 + 17" β†’ add(x=25, y=17)
"sum of 25 and 17" β†’ add(x=25, y=17)
"15% of 200" β†’ percentage(number=200, percent=15)
"what is 15 percent of 200" β†’ percentage(number=200, percent=15)
"square root of 64" β†’ square_root(x=64)
"sqrt(64)" β†’ square_root(x=64)

2. Complex Query Handling

For complex queries like

I have $1000, spend $234 on groceries and $156 on gas. How much is left?
Agent breaks down the problem
Calls subtract(1000, 234) β†’ 766
Calls subtract(766, 156) β†’ 610
Responds: "You have $610 left"

3. Error Handling

if y == 0:
    raise ValueError("Cannot divide by zero")
  • Provides clear error messages
  • Prevents invalid operations
  • Errors are automatically sent back to AI agent

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