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., "@MCP Product Search Server search for wireless headphones with rating above 4.5"
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 product search server based on the Model Context Protocol (MCP), allowing Claude to retrieve product catalogs based on keywords and return structured data.
Provides three tools for Claude to call:
Tool | Description |
| Search for products by keyword, supports filtering by category, price, and rating |
| List all product categories and their counts |
| Query full information for a single product by ID |
Related MCP server: commerce-mcp-server
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]"Requires Python 3.10 or higher. You can check with
python3 --version.
mcp dev server.pyThe browser will automatically open the MCP Inspector. If it does not open automatically, manually visit the address output in the terminal (usually http://localhost:6274).
Connection Steps:
Change the Command on the left to the absolute path of the Python executable in your virtual environment:
macOS/Linux: /absolute/path/to/mcp-product-search/.venv/bin/python
Windows: C:\absolute\path\to\mcp-product-search\.venv\Scripts\python.exe
Fill in server.py for Arguments
Click Connect. If the bottom left shows Connected, it is successful
Click the Tools tab at the top, select a tool, fill in the parameters, and click Run Tool
Locate the configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Add the following content (replace with the actual absolute path):
{
"mcpServers": {
"product-search": {
"command": "/绝对路径/mcp-product-search/.venv/bin/python",
"args": ["/绝对路径/mcp-product-search/server.py"]
}
}
}To view the absolute path of the current directory:
# macOS / Linux
pwd
# Windows
cdAfter saving the configuration file, completely exit and restart Claude Desktop.
claude mcp add product-search \
/绝对路径/mcp-product-search/.venv/bin/python \
/绝对路径/mcp-product-search/server.pyAfter connecting to Claude, you can ask questions like:
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 will automatically determine which tool to call and provide an answer based on the returned structured data.
searchParameter | Type | Required | Default | Description |
| string | Yes | — | Matches product name, description, brand, category, and tags |
| string | No | — | Filter by category, e.g., |
| float | No | — | Maximum price (USD) |
| float | No | — | Minimum rating (0–5) |
| int | No |
| Maximum number of results (up to 50) |
Results are sorted by rating (descending) and price (ascending).
Return Example:
{
"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_categoriesNo parameters. Returns all category names and their corresponding product counts.
get_productParameter | Type | Required | Description |
| string | Yes | Product ID, e.g., |
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.mdThe current product catalog consists of 12 sample items written in products.py. To replace it with real data, simply modify the search_products() function; server.py does not need to be changed.
Data Source | How to modify |
Local JSON/CSV | Read the file and populate |
SQLite / PostgreSQL | Replace the traversal logic with SQL queries |
E-commerce API | Send HTTP requests inside the function |
Elasticsearch | Call the ES full-text search interface |
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.
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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