Cox's Bazar AI Itinerary MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| cox_ai_itineraryA | Full workflow: fetch daily temperatures + generate AI itinerary. Uses the registered MCP prompt 'generate_itinerary' for consistency. Args: days: Number of days for the trip start_date: Start date (e.g., "2025-01-15", "15 Jan 2025", "today") Returns: Formatted prompt for AI to generate detailed itinerary |
| get_activity_suggestionsB | Suggest activities based on temperature and time of day. Args: temperature: Temperature in Celsius time_of_day: "morning", "afternoon", or "evening" Returns: List of suggested activities |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| generate_itinerary | Generate day-by-day itinerary based on number of days, temperature forecast, and start date |
| generate_detailed_itinerary | Generate detailed itinerary with budget and interests |
| suggest_activities | Suggest activities based on weather conditions |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| resource_current_weather | Current weather conditions for Cox's Bazar with today's forecast. |
| resource_weather_forecast | Detailed 7-day weather forecast for Cox's Bazar. |
| resource_temperature_summary | Quick temperature summary for the next 3 days in Cox's Bazar. |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: 'cox_ai_itinerary' generates a full multi-day itinerary based on dates and duration, while 'get_activity_suggestions' provides activity recommendations based on specific weather and time conditions. There is no overlap in functionality or ambiguity between them.
The naming is mixed: 'cox_ai_itinerary' uses a descriptive noun phrase with underscores, while 'get_activity_suggestions' follows a verb_noun pattern. Although both are readable, they lack a consistent convention, which could confuse agents expecting a uniform style.
With only two tools, the server feels thin for an itinerary planning domain. It lacks essential operations like updating itineraries, fetching historical data, or managing user preferences, which are typical for such a purpose. This minimal set limits functionality and may require agents to work around gaps.
The tool surface is significantly incomplete for itinerary planning. While it covers itinerary generation and activity suggestions, it misses core CRUD operations (e.g., no way to retrieve, modify, or delete itineraries) and lacks integration with user inputs or preferences. This will likely cause agent failures in real-world scenarios.