Сервер протокола контекста модели (MCP), предоставляющий инструменты для планирования путешествий и информацию о погоде в Кокс-Базаре, Бангладеш. Создан с помощью FastMCP и управляется через uv.
Related MCP server: Travel Planner MCP Server
Ресурсы погоды: Прогнозы температуры и подробная информация о погоде
Инструменты маршрутов: Генерация маршрутов путешествий с помощью ИИ
Подсказки для путешествий: Предварительно настроенные подсказки для планирования поездок
Поддержка аутентификации: Опциональная аутентификация через Clerk (настраивается через переменные окружения)
Ограничение частоты запросов: Встроенное промежуточное ПО для ограничения частоты запросов
Готовность к Docker: Включен Dockerfile для промышленной эксплуатации
Линтинг и форматирование: Ruff + хуки pre-commit (см. _docs/lint-formatting.md)
Python 3.13+
uv (менеджер пакетов)
Node.js 20+ (только для 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-installВсе команды зарегистрированы в pyproject.toml и доступны через uv run:
Команда | Описание |
| Запуск MCP-сервера |
| Запуск MCP-сервера в режиме разработки (автоперезагрузка) |
| Запуск интерфейса MCP Inspector (требуется Node.js 20+) |
| Запуск хуков pre-commit (линтинг + форматирование) для всех файлов |
| Установка хуков pre-commit в git-репозиторий |
Запустите MCP-сервер с автоперезагрузкой через watchdog:
uv run mcp-server-dev
# or
./scripts/run-mcp-server.shЗапустите интерактивный интерфейс MCP Inspector для тестирования инструментов, ресурсов и подсказок:
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 .Полную информацию о конфигурации см. в _docs/lint-formatting.md.
./scripts/test.shСоглашения о тестировании и фикстуры см. в _docs/testing.md.
docker build -t mcp-server .
docker run mcp-serverСервер запускается через uv run mcp-server внутри контейнера. Транспорт и порт настраиваются через переменные окружения (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 LicenseДокумент | Описание |
Конфигурация Ruff и pre-commit | |
Настройка тестирования, фикстуры и соглашения | |
Использование асинхронного HTTP-клиента | |
Интеграция провайдера аутентификации | |
Руководство по удаленному подключению MCP | |
Записи о принятых архитектурных решениях (ADR) |
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0cox_ai_itineraryget_activity_suggestionsThe 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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