Model Context Protocol (MCP) に基づいた製品検索サーバーです。Claudeがキーワードに基づいて製品カタログを検索し、構造化データを取得できるようにします。
Claudeが呼び出せる3つのツールを提供します：
ツール | 説明 |
| キーワードで製品を検索。カテゴリ、価格、評価によるフィルタリングをサポート |
| すべての製品カテゴリと件数を一覧表示 |
| IDを指定して単一製品の完全な情報を取得 |
Related MCP server: MCP Merchant Scout
git clone <your-repo-url>
cd mcp-product-searchmacOS / Linux：
python3 -m venv .venv
source .venv/bin/activate
pip install "mcp[cli]"Windows：
python -m venv .venv
.venv\Scripts\activate
pip install "mcp[cli]"Python 3.10以上が必要です。
python3 --versionで確認してください。
mcp dev server.pyブラウザが自動的にMCP Inspectorを開きます。開かない場合は、ターミナルに出力されたアドレス（通常は http://localhost:6274）に手動でアクセスしてください。
接続手順：
左側の Command を仮想環境内のPythonの絶対パスに変更します：
macOS/Linux：/絶対パス/mcp-product-search/.venv/bin/python
Windows：C:\絶対パス\mcp-product-search\.venv\Scripts\python.exe
Arguments に server.py を入力します
Connect をクリックし、左下に Connected と表示されれば成功です
上部の Tools タブをクリックし、ツールを選択してパラメータを入力し、Run Tool をクリックします
設定ファイルを見つけます：
macOS： ~/Library/Application Support/Claude/claude_desktop_config.json
Windows： %APPDATA%\Claude\claude_desktop_config.json
以下の内容を追加します（実際の絶対パスに置き換えてください）：
{
"mcpServers": {
"product-search": {
"command": "/绝对路径/mcp-product-search/.venv/bin/python",
"args": ["/绝对路径/mcp-product-search/server.py"]
}
}
}現在のディレクトリの絶対パスを確認する方法：
# macOS / Linux
pwd
# Windows
cd設定ファイルを保存した後、Claude Desktopを完全に終了して再起動してください。
claude mcp add product-search \
/绝对路径/mcp-product-search/.venv/bin/python \
/绝对路径/mcp-product-search/server.pyClaudeに接続後、以下のように質問できます：
帮我搜索苹果品牌的笔记本电脑
找一款评分 4.8 以上、价格不超过 300 美元的耳机
列出所有产品分类
查询产品 P003 的详细信息Claudeは自動的にどのツールを呼び出すべきか判断し、返された構造化データに基づいて回答します。
searchパラメータ | 型 | 必須 | デフォルト値 | 説明 |
| string | はい | — | 製品名、説明、ブランド、カテゴリ、タグをマッチング |
| string | いいえ | — | カテゴリでフィルタリング（例: |
| float | いいえ | — | 最高価格（USD） |
| float | いいえ | — | 最低評価（0–5） |
| int | いいえ |
| 結果の最大件数（最大50） |
結果は評価の高い順、価格の安い順にソートされます。
戻り値の例：
{
"keyword": "apple",
"filters": { "category": "Laptops", "max_price": null, "min_rating": null },
"total_results": 1,
"products": [
{
"id": "P001",
"name": "Apple MacBook Pro 14-inch M3",
"category": "Laptops",
"brand": "Apple",
"price": 1999.0,
"currency": "USD",
"stock": 42,
"rating": 4.8,
"description": "14-inch Liquid Retina XDR display, M3 chip, 18GB RAM, 512GB SSD.",
"tags": ["laptop", "apple", "macbook", "m3", "professional", "portable"]
}
]
}list_categoriesパラメータなし。すべてのカテゴリ名と対応する製品数を返します。
get_productパラメータ | 型 | 必須 | 説明 |
| string | はい | 製品ID（例: |
mcp-product-search/
├── server.py # MCP 服务器，定义工具
├── products.py # 产品目录与搜索逻辑
├── pyproject.toml # 项目依赖
└── README.md現在の製品カタログは products.py に記述された12件のサンプルデータです。実際のデータに置き換えるには search_products() 関数を修正するだけでよく、server.py を変更する必要はありません。
データソース | 修正方法 |
ローカル JSON/CSV | 起動時にファイルを読み込んで |
SQLite / PostgreSQL | ループ処理をSQLクエリに置き換える |
EC API | 関数内でHTTPリクエストを送信する |
Elasticsearch | ESの全文検索インターフェースを呼び出す |
Retrieve full details for a single product by its ID (e.g. "P001").
Args: product_id: The product's unique identifier.
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist; description implies read-only but fails to disclose any behavioral details beyond 'retrieve'.
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, front-loading the core purpose and listing the parameter clearly.
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 simple single-parameter input and presence of an output schema, the description is sufficiently complete for the task.
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 meaning to the product_id parameter with an example and context, compensating for 0% schema coverage.
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 it retrieves full details for a single product by ID, distinguishing it from siblings like list_categories and search.
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 on when to use this tool versus siblings like search or list_categories is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return all available product categories and their item counts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description only states the basic return value, omitting additional behavioral details such as ordering, limits, or side effects.
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?
A single, clear sentence with no unnecessary words, perfectly front-loaded.
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?
For a simple read operation with no parameters and an output schema present, the description sufficiently covers what the tool returns.
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?
Since there are no parameters, the description does not need to add parameter information. Baseline 4 for 0 parameters.
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 explicitly states the verb 'Return' and resource 'all available product categories and their item counts', distinguishing it from sibling tools like 'get_product' and 'search'.
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' or 'get_product', leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search products by keyword with optional filters.
Args: keyword: Search term matched against name, description, brand, category, and tags. category: Filter by exact category name (e.g. "Laptops", "Headphones"). max_price: Upper price limit in USD. min_rating: Minimum rating (0–5). limit: Maximum number of results to return (default 10, max 50).
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | ||
| category | No | ||
| max_price | No | ||
| min_rating | No | ||
| limit | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that keyword matches multiple fields (name, description, brand, category, tags) and explains filters, but does not mention that the operation is read-only, result ordering, or error behavior. Adequate but missing some context.
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 concise, with a clear introductory sentence followed by structured 'Args' bullet points. It front-loads the purpose and efficiently details parameters without unnecessary text.
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 5 parameters and existence of an output schema, the description covers input well but omits output format details. It could mention response structure or behavior like empty results. Acceptable but not fully complete.
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 description must compensate. It fully explains each parameter beyond the schema: keyword matches specific fields, category is exact name, max_price is in USD, min_rating is 0-5, limit has default and max. This adds significant meaning.
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 'Search products by keyword with optional filters.' It specifies the verb 'search' and the resource 'products', distinguishing it from sibling tools 'get_product' (retrieves a single product) and 'list_categories' (lists categories).
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 searching products but does not explicitly contrast with siblings or provide when-not-to-use guidance. The context is clear from names, but the description lacks explicit exclusions or alternative recommendations.
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.
v0.1.0get_productlist_categoriessearchScored across 3 tools
Each tool has a distinct purpose: get_product retrieves by ID, list_categories returns category overview, search does keyword-based filtering. No ambiguity in their roles.
All tool names follow a consistent verb_noun pattern (get_product, list_categories, search) using snake_case, making them predictable.
With 3 tools, the server is well-scoped for a search-focused service. Each tool earns its place without bloat or deficiency.
The tool set covers core search and retrieval operations (get by ID, list categories, keyword search). Missing a general 'list all products' operation, but search likely covers this with an empty keyword, though not documented; minor gap.
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