This MCP server provides AI-powered travel planning tools specifically for Cox's Bazar, Bangladesh, combining weather data with intelligent itinerary generation.
Capabilities:
Generate AI-powered travel itineraries - Create detailed multi-day trip plans by specifying trip duration and start date (supports flexible formats like "2025-01-15", "15 Jan 2025", or "today"). The system automatically fetches daily temperature forecasts and generates comprehensive itineraries.
Get weather-based activity suggestions - Receive personalized activity recommendations tailored to specific temperature conditions and time of day (morning, afternoon, or evening).
Access weather forecasts - Retrieve temperature and detailed weather information for informed travel planning.
Use pre-configured travel prompts - Leverage built-in prompts for consistent and streamlined itinerary generation workflows.
Supports authentication provider integration with Auth0, enabling enterprise-grade authentication and authorization for MCP server access.
Provides optional authentication via Clerk OAuth provider, allowing secure access to MCP server tools and resources with configurable authentication settings.
Includes production-ready Docker configuration for containerized deployment, enabling easy deployment and scaling of the MCP server in containerized environments.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Cox's Bazar AI Itinerary MCP Server generate a 3-day itinerary for Cox's Bazar with beach activities"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that provides travel planning tools and weather information for Cox's Bazar, Bangladesh. Built with FastMCP and managed by uv.
Related MCP server: Travel Planner MCP Server
Weather Resources: Temperature forecasts and detailed weather information
Itinerary Tools: AI-powered travel itinerary generation
Travel Prompts: Pre-configured prompts for travel planning
Auth Support: Optional authentication via Clerk (configurable via env)
Rate Limiting: Built-in rate limiting middleware
Docker Ready: Production Dockerfile included
Linting & Formatting: Ruff + pre-commit hooks (see _docs/lint-formatting.md)
Python 3.13+
uv (package manager)
Node.js 20+ (only for MCP Inspector)
# Install dependencies
uv sync
# Copy environment variables and configure
cp .env.example .env
# (Optional) Install pre-commit git hooks
uv run pre-commit-installAll commands are registered in pyproject.toml and available via uv run:
Command | Description |
| Start the MCP server |
| Start the MCP server in dev mode (auto-reload) |
| Launch the MCP Inspector UI (requires Node.js 20+) |
| Run pre-commit hooks (lint + format) on all files |
| Install pre-commit hooks into the git repo |
Start the MCP server with auto-reload via watchdog:
uv run mcp-server-dev
# or
./scripts/run-mcp-server.shLaunch the interactive MCP Inspector UI to test tools, resources, and prompts:
uv run mcp-inspector
# or
./scripts/run-inspector.sh# Run lint + format via pre-commit
uv run lint
# Or run individually
./scripts/lint.sh # ruff check . --fix
./scripts/format.sh # ruff format .See _docs/lint-formatting.md for full configuration details.
./scripts/test.shSee _docs/testing.md for test conventions and fixtures.
docker build -t mcp-server .
docker run mcp-serverThe server runs via uv run mcp-server inside the container. Transport and port are configurable through environment variables (TRANSPORT_NAME, SERVER_PORT, SERVER_HOST).
.
├── src/mcp_server/
│ ├── server.py # Main server entry point
│ ├── mcp_instance.py # FastMCP instance & auth config
│ ├── cli.py # CLI command definitions
│ ├── config/
│ │ ├── auth_provider.py # Auth provider factory
│ │ └── custom_routes.py # Custom HTTP routes
│ ├── handlers/ # MCP handler registrations (auto-discovered)
│ │ ├── tools/
│ │ │ ├── auth_additional.py
│ │ │ └── itinerary.py
│ │ ├── resources/
│ │ │ └── weather.py
│ │ └── prompts/
│ │ └── travel_prompts.py
│ ├── models/
│ │ └── itinerary_models.py # Pydantic models & schemas
│ ├── services/
│ │ └── itenerary_service.py # Business logic
│ ├── lib/
│ │ ├── clerk_auth_provider.py # Clerk OAuth provider
│ │ └── httpx_client.py # Async HTTP client wrapper
│ ├── prompt_templates/
│ │ └── travel.py # Prompt text builders
│ └── utils/
│ ├── elicitation.py
│ ├── get_weather_forecast.py
│ ├── helpers.py
│ └── http.py
├── tests/
│ ├── conftest.py
│ ├── fixtures/
│ │ ├── context.py
│ │ └── weather.py
│ ├── unit/
│ │ ├── test_auth_additional_tools.py
│ │ ├── test_auth_provider.py
│ │ ├── test_elicitation.py
│ │ ├── test_helpers.py
│ │ ├── test_itinerary_service_extra.py
│ │ ├── test_itinerary_tool_handler.py
│ │ ├── test_models.py
│ │ ├── test_server.py
│ │ ├── test_travel_prompts.py
│ │ ├── test_travel_prompts_handler.py
│ │ ├── test_weather_forecast.py
│ │ └── test_weather_resource.py
│ └── integration/
│ ├── test_itinerary_tool.py
│ └── test_weather_api.py
├── scripts/
│ ├── run-mcp-server.sh # Dev server with auto-reload
│ ├── run-inspector.sh # MCP Inspector launcher
│ ├── test.sh # Test runner
│ ├── lint.sh # Ruff lint --fix
│ ├── format.sh # Ruff format
│ └── generate-secrets.sh # Secret key generator
├── _docs/ # Documentation & ADRs
│ ├── adr/
│ │ ├── 001-choose-fastmcp.md
│ │ ├── 002-choose-httpx.md
│ │ └── ADR-template.md
│ ├── auth-provider-auth0.md
│ ├── httpx-client.md
│ ├── lint-formatting.md
│ ├── remote-mcp-connect.md
│ └── testing.md
├── .env.example # Environment variables template
├── .pre-commit-config.yaml # Pre-commit hook config
├── Dockerfile # Production Docker image
├── pyproject.toml # Project config & dependencies
├── ruff.toml # Ruff linter/formatter config
├── pytest.ini # Pytest configuration
├── glama.json # Glama registry config
└── LICENSE # MIT LicenseDocument | Description |
Ruff & pre-commit configuration | |
Test setup, fixtures, and conventions | |
Async HTTP client usage | |
Auth provider integration | |
Remote MCP connection guide | |
Architecture Decision Records |
MIT
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
| Name | Required | Description | Default |
|---|---|---|---|
| days | Yes | ||
| start_date | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the tool's workflow and mentions using a registered prompt for consistency, which adds useful context. However, it doesn't cover important behavioral aspects like error handling, rate limits, authentication needs, or what happens if temperature data is unavailable. The description doesn't contradict any annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections: workflow overview, args, and returns. Each sentence adds value, though the 'Full workflow' line could be more concise. The bullet-point format for args and returns is efficient. It's appropriately sized for a 2-parameter tool with a specific workflow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, workflow involving external data fetch and AI generation), no annotations, but with an output schema (implied by 'Returns' section), the description is reasonably complete. It explains the purpose, parameters, and output format. The main gap is lack of error handling or edge case guidance, but the output schema reduces the need to fully document return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides clear semantic meaning for both parameters: 'days' as 'Number of days for the trip' and 'start_date' with format examples. This adds significant value beyond the bare schema, though it doesn't explain constraints like date ranges or day limits. With 0% schema coverage and 2 parameters, this is strong but not perfect compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'fetch daily temperatures + generate AI itinerary' and mentions using a registered MCP prompt. It distinguishes from the sibling 'get_activity_suggestions' by focusing on full itinerary generation rather than just suggestions. However, it doesn't specify the exact resource being fetched (e.g., temperatures for what location?), making it slightly less specific than a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for trip planning with temperature data, but doesn't explicitly state when to use this tool versus alternatives like 'get_activity_suggestions'. It mentions the workflow but lacks clear guidance on prerequisites or exclusions (e.g., whether location data is needed elsewhere).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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
| Name | Required | Description | Default |
|---|---|---|---|
| temperature | Yes | ||
| time_of_day | No | afternoon |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a 'List of suggested activities,' which hints at read-only behavior, but it does not disclose any traits like whether it's safe, if there are rate limits, authentication needs, or how the suggestions are generated. The description is minimal and lacks critical behavioral context for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with a clear purpose statement followed by structured sections for 'Args' and 'Returns.' Every sentence earns its place by providing essential information without waste, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no annotations, no output schema), the description is somewhat complete but has gaps. It covers the purpose and parameters well, but lacks usage guidelines and behavioral transparency. Without an output schema, it minimally describes returns, but more context on behavior would improve completeness for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'temperature' is in Celsius and 'time_of_day' can be 'morning', 'afternoon', or 'evening', providing semantic context that the schema lacks. Since there are only 2 parameters and the description compensates well for the low schema coverage, this earns a high score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Suggest activities based on temperature and time of day.' It specifies the verb ('suggest') and resources ('activities'), and while it doesn't explicitly differentiate from the sibling tool 'cox_ai_itinerary', the purpose is specific enough to understand its function. It's not a tautology since it elaborates beyond just the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the sibling tool 'cox_ai_itinerary' in the context, but the description itself does not indicate any relationship, exclusions, or prerequisites. Usage is implied only by the parameters, with no explicit context or alternatives stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
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