Tripadvisor コンテンツ API 用のモデル コンテキスト プロトコル(MCP) サーバー。
これにより、標準化された MCP インターフェースを通じて Tripadvisor の位置データ、レビュー、写真にアクセスできるようになり、AI アシスタントが旅行先や体験を検索できるようになります。
[x] トリップアドバイザーで場所（ホテル、レストラン、観光スポット）を検索する
[x] 特定の場所の詳細情報を取得する
[x] 場所のレビューと写真を取得する
[x] 座標に基づいて近くの場所を検索する
[x] APIキー認証
[x] Dockerコンテナ化のサポート
[x] AIアシスタントのためのインタラクティブツールを提供する
ツールのリストは構成可能なので、MCP クライアントで使用できるようにするツールを選択できます。
Related MCP server: expedia-travel-recommendations-mcp
トリップアドバイザー開発者ポータルからトリップアドバイザー コンテンツ API キーを取得します。
.envファイルまたはシステム環境変数を使用して、Tripadvisor コンテンツ API の環境変数を設定します。
# Required: Tripadvisor Content API configuration
TRIPADVISOR_API_KEY=your_api_key_hereサーバー設定をクライアント設定ファイルに追加します。例えば、Claude Desktopの場合は以下のようになります。
{
"mcpServers": {
"tripadvisor": {
"command": "uv",
"args": [
"--directory",
"<full path to tripadvisor-mcp directory>",
"run",
"src/tripadvisor_mcp/main.py"
],
"env": {
"TRIPADVISOR_API_KEY": "your_api_key_here"
}
}
}
}注意: Claude Desktop で
Error: spawn uv ENOENT表示される場合は、uvへのフル パスを指定するか、構成で環境変数NO_UV=1を設定する必要があります。
このプロジェクトには、簡単な展開と分離を実現する Docker サポートが含まれています。
次を使用して Docker イメージをビルドします。
docker build -t tripadvisor-mcp-server .Docker を使用してサーバーを実行するにはいくつかの方法があります。
docker run -it --rm \
-e TRIPADVISOR_API_KEY=your_api_key_here \
tripadvisor-mcp-serverTripadvisor API キーを使用して.envファイルを作成し、次を実行します。
docker-compose upClaude Desktop でコンテナ化されたサーバーを使用するには、環境変数を使用して Docker を使用するように構成を更新します。
{
"mcpServers": {
"tripadvisor": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e", "TRIPADVISOR_API_KEY",
"tripadvisor-mcp-server"
],
"env": {
"TRIPADVISOR_API_KEY": "your_api_key_here"
}
}
}
}この構成では、変数名のみを指定した-eフラグを使用し、 envオブジェクトに実際の値を指定して、Claude Desktop から Docker コンテナーに環境変数を渡します。
貢献を歓迎します！ご提案や改善点がありましたら、問題を報告するか、プルリクエストを送信してください。
このプロジェクトは依存関係の管理にuvを使用しています。お使いのプラットフォームの手順に従ってuvをインストールしてください。
curl -LsSf https://astral.sh/uv/install.sh | sh次に、次のコマンドで仮想環境を作成し、依存関係をインストールします。
uv venv
source .venv/bin/activate # On Unix/macOS
.venv\Scripts\activate # On Windows
uv pip install -e .プロジェクトはsrcディレクトリ構造で構成されています:
tripadvisor-mcp/
├── src/
│ └── tripadvisor_mcp/
│ ├── __init__.py # Package initialization
│ ├── server.py # MCP server implementation
│ ├── main.py # Main application logic
├── Dockerfile # Docker configuration
├── docker-compose.yml # Docker Compose configuration
├── .dockerignore # Docker ignore file
├── pyproject.toml # Project configuration
└── README.md # This fileこのプロジェクトには、機能性を保証し、回帰を防ぐのに役立つテスト スイートが含まれています。
pytest でテストを実行します。
# Install development dependencies
uv pip install -e ".[dev]"
# Run the tests
pytest
# Run with coverage report
pytest --cov=src --cov-report=term-missing道具 | カテゴリ | 説明 |
| 検索 | クエリテキスト、カテゴリ、その他のフィルターで場所を検索します |
| 検索 | 特定の座標の近くの場所を見つける |
| 検索 | 場所の詳細情報を取得する |
| 検索 | 場所のレビューを取得する |
| 検索 | 場所の写真を取得する |
マサチューセッツ工科大学
Get detailed information about a specific location
| Name | Required | Description | Default |
|---|---|---|---|
| locationId | Yes | ||
| language | No | en |
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 only states what the tool does ('Get detailed information') without describing traits like whether it's read-only, requires authentication, has rate limits, or what the output format might be. This leaves critical behavioral aspects unspecified for a tool with no structured 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 a single, clear sentence with no wasted words. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration, making it efficient for quick understanding.
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 complexity (2 parameters, no output schema, no annotations), the description is incomplete. It lacks details on parameter usage, behavioral traits, output expectations, and differentiation from siblings. For a tool with no structured support, this minimal description does not provide enough context for an agent to use it effectively.
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 schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'a specific location' but does not explain the parameters (locationId and language) or their semantics, such as what locationId represents or how language affects the response. This fails to add meaningful context beyond the bare schema.
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 states the tool's purpose ('Get detailed information about a specific location'), which is clear but vague. It specifies the verb ('Get') and resource ('location'), but does not distinguish what 'detailed information' entails or how it differs from sibling tools like get_location_photos or get_location_reviews. This leaves the scope ambiguous compared to alternatives.
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 siblings. It does not mention alternatives, prerequisites, or exclusions, such as whether it should be used for basic info versus photos or reviews. Without such context, an agent might struggle to select the correct tool among the provided options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get photos for a specific location
| Name | Required | Description | Default |
|---|---|---|---|
| locationId | Yes | ||
| language | No | en |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only states the basic action without mentioning permissions, rate limits, pagination, response format, or whether it's read-only or has side effects. This leaves critical operational details unspecified for a tool with parameters.
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 a single, clear sentence with no wasted words. It's front-loaded and efficiently conveys the core purpose without unnecessary elaboration, making it easy to parse 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 complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover parameter meanings, behavioral aspects like safety or output, or usage context. For a tool that fetches data, more details on what to expect and how to use it are needed for effective agent operation.
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 by explaining parameters. It doesn't mention 'locationId' or 'language' at all, leaving both parameters undocumented. The description adds no meaning beyond the schema, failing to clarify what 'locationId' refers to or how 'language' affects results.
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 'Get photos for a specific location' clearly states the action (get) and resource (photos for a location), but it's vague about scope (e.g., number of photos, types, or source). It doesn't differentiate from siblings like 'get_location_details' or 'get_location_reviews', which also fetch location-related data but for different resources.
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?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't specify if this is for visual content versus textual details (vs. get_location_details) or reviews (vs. get_location_reviews), or when to prefer search tools like search_locations. The description lacks any context for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get reviews for a specific location
| Name | Required | Description | Default |
|---|---|---|---|
| locationId | Yes | ||
| language | No | en |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the basic action without detailing traits like whether it's read-only, requires authentication, has rate limits, returns paginated results, or includes error handling. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 extremely concise with a single sentence that directly states the tool's purpose. It is front-loaded and wastes no words, making it easy to parse quickly. This efficiency is appropriate for a simple tool, though it sacrifices detail for brevity.
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 complexity (2 parameters, no output schema, no annotations), the description is incomplete. It does not cover parameter meanings, behavioral traits, or output expectations (e.g., review format, error cases). Without annotations or an output schema, the description fails to provide enough context for reliable 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 schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'a specific location' but does not explain the 'locationId' parameter (e.g., format, source) or the optional 'language' parameter (e.g., supported codes, default behavior). This lack of semantic context leaves parameters ambiguous beyond the schema.
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 'Get reviews for a specific location' clearly states the action (get) and resource (reviews for a location), which is adequate. However, it lacks specificity about what 'reviews' entail (e.g., ratings, comments, dates) and does not differentiate from sibling tools like 'get_location_details' or 'get_location_photos', leaving ambiguity about the scope of information returned.
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 does not mention prerequisites (e.g., needing a location ID), exclusions, or comparisons to siblings such as 'search_locations' for broader queries. This absence of context makes it harder for an agent to select the correct tool in practice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for locations (hotels, restaurants, attractions) on Tripadvisor
| Name | Required | Description | Default |
|---|---|---|---|
| searchQuery | Yes | ||
| language | No | en | |
| category | No | ||
| phone | No | ||
| address | No | ||
| latLong | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('search') but doesn't describe what the search returns (e.g., list of results, pagination), performance characteristics, rate limits, authentication needs, or error conditions. This leaves significant gaps for an agent to understand the tool's behavior.
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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function.
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 complexity (6 parameters, no schema descriptions, no output schema, no annotations), the description is inadequate. It doesn't address parameter usage, return values, error handling, or behavioral traits, leaving the agent with insufficient information to use the tool effectively beyond its basic purpose.
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 input schema has 6 parameters with 0% description coverage, and the tool description provides no information about any parameters. It doesn't explain what 'searchQuery' should contain, how 'category' is used, the format of 'latLong', or the purpose of optional fields like 'phone' and 'address'. This fails to compensate for the schema's lack of documentation.
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 verb ('search for') and resource ('locations'), and specifies the domain ('on Tripadvisor') with examples of location types ('hotels, restaurants, attractions'). It doesn't explicitly differentiate from sibling tools like 'search_nearby_locations', but the purpose is unambiguous.
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?
No guidance is provided on when to use this tool versus alternatives like 'search_nearby_locations' or 'get_location_details'. The description implies a general search function but lacks context about prerequisites, limitations, or optimal use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for locations near a specific latitude/longitude
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes | ||
| language | No | en | |
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the search action but doesn't describe what 'near' means (distance/radius), result format, pagination, rate limits, authentication needs, or error conditions. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
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 a single, efficient sentence with zero wasted words. It's appropriately sized for a search tool and front-loads the core functionality without unnecessary elaboration.
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 4 parameters with 0% schema coverage, no annotations, no output schema, and multiple sibling tools, the description is incomplete. It covers the basic purpose but lacks parameter details, behavioral context, usage differentiation, and return value information that would help an agent use this tool effectively.
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 for undocumented parameters. The description mentions latitude/longitude (covering 2 of 4 parameters) but doesn't explain the 'language' or 'category' parameters, their formats, or valid values. It adds some meaning for required parameters but leaves half the parameters unexplained.
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 as 'Search for locations near a specific latitude/longitude', which includes a specific verb ('Search') and resource ('locations') with geographic context. However, it doesn't distinguish this tool from its sibling 'search_locations', which might have different functionality.
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 like 'search_locations' or when not to use it. There's no mention of prerequisites, limitations, or comparison with sibling tools, leaving the agent without contextual usage information.
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.
get_location_detailsget_location_photosget_location_reviewssearch_locationssearch_nearby_locationsScored across 5 tools
Each tool has a clearly distinct purpose: get_location_details retrieves general information, get_location_photos handles photos, get_location_reviews focuses on reviews, search_locations performs general searches, and search_nearby_locations finds locations based on proximity. There is no overlap or ambiguity between these functions.
All tool names follow a consistent verb_noun pattern with 'get_' or 'search_' prefixes and descriptive suffixes like 'details', 'photos', 'reviews', 'locations', and 'nearby_locations'. The naming is uniform and predictable throughout the set.
With 5 tools, this server is well-scoped for its Tripadvisor domain, covering key operations like retrieving details, photos, reviews, and performing searches. Each tool serves a unique and necessary function without being excessive or insufficient.
The tool set provides strong coverage for core Tripadvisor functionalities, including search and retrieval of location details, photos, and reviews. A minor gap exists in lacking CRUD operations (e.g., creating or updating reviews), but this is reasonable for a read-focused API, and agents can work effectively with the provided tools.
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