このサーバーは、ClaudeやVS Codeなどの大規模言語モデルがPlayFabサービスと直接やり取りできるようにするミドルウェアです。安全で効率的な翻訳者として機能し、AIアシスタントをアイテム検索、セグメント照会、プレイヤープロフィール検索、インベントリ管理、PlayFab ID変換といったPlayFabの様々な機能に接続します。
You: "Show me the latest 10 items."
Claude: *calls the PlayFab search_items API and returns the results in plain text*Related MCP server: Azure Cosmos DB MCP Server
このサーバーは、モデルコンテキストプロトコル（MCP）を活用して、AIモデルとPlayFabサービス間のユニバーサルインターフェースを確立します。MCPはあらゆるAIモデルをサポートするように設計されていますが、現在は開発者向けプレビューとして提供されています。
開始するには、次の手順に従ってください。
プロジェクトを設定します。
プロジェクトの詳細を LLM クライアントの構成に追加します。
PlayFab データを自然に操作してみましょう。
PlayFab の search_items API を使用してアイテムを検索します。
包括的なセグメント情報を取得します。
指定されたセグメント内のプレーヤー プロファイルを照会します。
get_inventory_items API を使用して現在の在庫アイテムを取得します。
get_inventory_collection_ids API を使用してインベントリ コレクション ID を取得します。
get_title_player_account_id_from_playfab_id API を使用して、PlayFab ID をタイトル プレーヤー アカウント ID に変換します。
Smithery経由で Claude Desktop 用の PlayFab MCP Server を自動的にインストールするには:
npx -y @smithery/cli install @akiojin/playfab-mcp-server --client claudeNode.js 18 以上。
有効な PlayFab アカウント (PlayFab ゲーム マネージャー経由でタイトル ID と開発者秘密キーを取得します)。
Claude Desktop などのサポートされている LLM クライアント。
PlayFab ゲーム マネージャーから PlayFab タイトル ID と開発者秘密キーを取得し、次の内容を含む.envファイルをプロジェクト ルートに作成します (プレースホルダーを実際の資格情報に置き換えます)。
PLAYFAB_TITLE_ID=
PLAYFAB_DEV_SECRET_KEY=依存関係のインストールプロジェクト ルートで次のコマンドを実行して、必要な依存関係をすべてインストールします。
npm installプロジェクトをビルドする 次のコマンドを実行してプロジェクトをコンパイルします。
npm run buildサーバーの起動 次のコマンドを実行してサーバーを起動します。
npm start確認メッセージ起動時に次のメッセージが表示されます。
PlayFab Server running on stdioカーソルで PlayFab MCP サーバーを使用するには、次の手順に従います。
まだインストールしていない場合は、 Cursor Desktopをインストールしてください。
空のフォルダーでカーソルの新しいインスタンスを開きます。
このリポジトリからmcp.jsonファイルをフォルダーにコピーし、環境に応じて値を更新します。
カーソルを起動します。PlayFab MCP サーバーがツール リストに表示されます。
たとえば、「最新の 10 件のアイテムを表示」のようなプロンプトを試して、サーバーがクエリを正しく処理していることを確認します。
Claude Desktopを開き、「ファイル」→「設定」→「開発者」→「設定の編集」に移動します。次に、 claude_desktop_configファイルの内容を以下のコードに置き換えます。
{
"mcpServers": {
"playfab": {
"command": "npx",
"args": [
"-y",
"@akiojin/playfab-mcp-server"
],
"env": {
"PLAYFAB_TITLE_ID": "Your PlayFab Title ID",
"PLAYFAB_DEV_SECRET_KEY": "Your PlayFab Developer Secret Key"
}
}
}
}これらの手順により、PlayFab MCP サーバーを LLM クライアントで使用するように正常に構成し、PlayFab のサービスとのシームレスな対話が可能になります。
PlayFab get all players
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the action without disclosing behavioral traits such as pagination, rate limits, authentication needs, or what 'all players' entails (e.g., active only, includes metadata). This leaves significant gaps for safe and effective use.
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 phrase 'PlayFab get all players', which is concise but under-specified. It front-loads the core action but lacks necessary context to be fully helpful. While not verbose, it misses opportunities to add value in a compact form.
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 no annotations, no output schema, and a simple but vague description, this is incomplete for a tool that likely returns a list of players. The description doesn't explain return values, error handling, or operational constraints, leaving the agent with insufficient information for reliable 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 tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. Baseline is 4 for zero parameters, as the schema fully covers the absence of inputs.
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 'PlayFab get all players' states the action ('get') and resource ('players'), but lacks specificity about scope or format. It distinguishes from siblings like 'get_all_segments' by resource type, but doesn't clarify if this retrieves all players globally or within a context. The purpose is understandable but vague.
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_items'. The description doesn't mention prerequisites, limitations, or typical use cases. Without annotations or context, the agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
PlayFab get all segments
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. The description only states the action ('get all segments') without any details on what this entails—e.g., whether it's a read-only operation, if it requires authentication, how it handles pagination or rate limits, or what the output looks like. This is inadequate for a tool with no annotation support.
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 ('PlayFab get all segments'), which could be efficient, but it under-specifies the tool's purpose and context. While it avoids unnecessary words, it fails to provide essential information that would help an agent use the tool correctly, making this brevity more of a deficiency than a strength.
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 lack of annotations, no output schema, and a simple input schema with no parameters, the description is incomplete. It does not explain what 'segments' are, how the tool behaves, or what to expect in return, leaving significant gaps for an agent to understand and invoke the tool effectively in the context of sibling tools like 'get_all_players'.
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 0 parameters with 100% coverage, meaning there are no parameters to document. The description does not add parameter details, which is appropriate here. A baseline of 4 is applied since no parameters exist, and the description does not mislead about inputs.
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 'PlayFab get all segments' restates the tool name with minimal elaboration. It specifies the action ('get all') and resource ('segments'), but lacks specificity about what 'segments' are in the PlayFab context or how this differs from sibling tools like 'get_all_players' or 'search_items'. This is borderline tautological, as it essentially repeats the name with the platform prefix added.
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 sibling tools like 'get_all_players' or 'search_items', nor does it specify contexts, prerequisites, or exclusions for usage. This leaves the agent with no information to make an informed choice among available tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
PlayFab search items
| Name | Required | Description | Default |
|---|---|---|---|
| continuationToken | No | An opaque token used to retrieve the next page of items, if any are available. | |
| count | Yes | Number of items to retrieve. This value is optional. Maximum page size is 50. Default value is 10. | |
| filter | No | An OData filter used to refine the search query (For example: "type eq 'ugc'"). More info about Filter Complexity limits can be found here: https://learn.microsoft.com/en-us/gaming/playfab/features/economy-v2/catalog/search#limits | |
| orderBy | No | An OData orderBy used to order the results of the search query. For example: "rating/average asc" | |
| search | No | The text to search for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It reveals nothing about whether this is a read/write operation, authentication requirements, rate limits, pagination behavior (beyond what's implied by continuationToken in schema), error conditions, or response format. For a search tool with 5 parameters and no output schema, this is critically inadequate.
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?
While technically concise (two words), this is under-specification rather than effective brevity. The description fails to front-load essential information and doesn't earn its place - it provides almost no value beyond the tool name itself. A truly concise description would still convey core purpose and context.
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 (5 parameters, no output schema, no annotations), the description is completely inadequate. It doesn't explain what 'items' are, what search capabilities exist, how results are returned, or any behavioral characteristics. For a search operation that likely returns structured data, the absence of output schema means the description should at minimum indicate the nature of returned results.
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 100%, so the schema fully documents all 5 parameters. The description adds no parameter information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.
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 'PlayFab search items' is a tautology that merely restates the tool name with a platform prefix. It doesn't specify what 'items' refers to (catalog items, inventory items, etc.), what action 'search' performs (filtering, text search, or both), or what resource domain this operates in. While it distinguishes from the sibling tools (which target players/segments rather than items), the purpose remains vague.
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. There's no mention of prerequisites, appropriate contexts, or comparison with other search methods. The sibling tools (get_all_players, get_all_segments) target different resources, but the description doesn't help an agent decide between searching items versus retrieving players/segments.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose targeting different PlayFab resources: players, segments, and items. There is no overlap in functionality, making it easy for an agent to select the correct tool without confusion.
All tool names follow a consistent verb_noun pattern (get_all_players, get_all_segments, search_items), using snake_case throughout. This predictability enhances readability and usability.
With only 3 tools, the server feels under-scoped for a platform like PlayFab, which typically involves more operations such as creating/updating players, managing items, or handling segments. This limited set may hinder agent workflows.
The toolset is severely incomplete for PlayFab's domain, covering only retrieval operations (get_all and search) without any create, update, or delete capabilities. This leaves significant gaps that will likely cause agent failures in broader tasks.
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GibsonAI MCP server: manage your databases with natural language
Official Microsoft MCP Server to query Microsoft Entra data using natural language
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
The Ramp MCP server enables users to securely connect Ramp with AI assistants like ChatGPT and Claude to query financial data and take actions using natural language. It transforms Ramp's developer API into a SQL interface that LLMs can query, allowing admins to analyze spend trends, identify cost savings, and run complex SQL analyses on comprehensive datasets (transactions, purchase orders, vendors, users), while all users can manage cards, view transactions, request reimbursements, and get expense policy answers.
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