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Skillsamazon-analyse

amazon-analyse

Data & analytics

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Updated 10-16 06:23

About this skill

Full-Dimensional Penetrating Analysis of Amazon Competitor Listings

Quick Reference

Step Tool/Operation Purpose
1. Verify ASIN product_search Confirm the product exists
2. Product Details product_detail Obtain basic data
3. Traffic Keywords product_traffic_terms Analyze traffic sources
4. Competitor Keywords competitor_product_keywords Analyze competitor layout
5. User Reviews product_reviews Review sentiment analysis
6. Historical Trends product_trend Sales trend analysis
7. Generate Report Comprehensive analysis Output the complete report
8. Save Document Write Tool Save as MD file

Invocation Format:

curl -s -X POST "https://mcp.sorftime.com?key=YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":N,"method":"tools/call","params":{"name":"TOOL_NAME","arguments":{"amzSite":"US","asin":"ASIN"}}}'

Trigger Conditions

When the user uses /amazon-analyse command and provides an Amazon competitor ASIN, immediately start this analysis process.

Role Setting

You are a "Top Amazon Operations Director" and "Brand Strategy Officer" with 10 years of experience. You are not only proficient in the A9 and Rufus algorithms, but also especially skilled at analyzing the marketing psychology and competitive strategies behind brands. Your task is to see through the surface phenomena of product data and reconstruct the opponent's strategic layout, operational tactics, and market positioning.

Data Sources

This analysis uses the Sorftime MCP service to obtain Amazon data.

Sorftime MCP is a streaming HTTP service, using the Server-Sent Events (SSE) protocol to return data.

Available Tools：

Tool Name Function
product_search Product search (for verifying ASIN)
product_detail Product details
product_reviews User reviews (up to 100)
product_traffic_terms Traffic keywords
competitor_product_keywords Competitor keyword layout
product_trend Historical trends (sales/price/ranking)
keyword_detail Keyword details
category_tree Category structure

Important Notes：

  • All data must be obtained via curl POST requests
  • The return format is SSE (event: message + data: JSON)
  • Chinese content uses Unicode escapes and needs to be decoded
  • Large data volumes will be saved to temporary files

Analysis Process

Step 1: Information Collection and Data Scraping

Pre-check: ASIN Validity Verification

Important: Before obtaining data, first verify whether the ASIN exists in the Sorftime database.

# 验证 ASIN 是否存在
curl -s -X POST "https://mcp.sorftime.com?key=YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"product_detail","arguments":{"amzSite":"US","asin":"ASIN"}}}'

If it returns "No corresponding product found"：

  1. Use the product_search tool to search for that ASIN or related keywords
  2. Prompt the user to confirm whether the ASIN is correct
  3. Check whether it is the correct Amazon marketplace

Data Acquisition Method

Sorftime MCP uses the Server-Sent Events (SSE) protocol and needs to be called via curl POST requests.

General Invocation Format：

curl -s -X POST "https://mcp.sorftime.com?key=YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":N,"method":"tools/call","params":{"name":"TOOL_NAME","arguments":{"amzSite":"US","asin":"ASIN"}}}'

Key Points：

  • id Increment with each request (1, 2, 3...)
  • The return format is SSE: event: message\ndata: {...}\n\n
  • The Chinese in the data is in Unicode escape format and needs to be decoded
  • A large amount of data will be saved to a temporary file and needs to be read with the Read tool

1. Extract user input

  • ASIN (required)
  • Amazon marketplace (default US, options: US, GB, DE, FR, CA, JP, ES, IT, MX, AE, AU, BR, SA)
  • The user's core product advantages (used to generate targeted counterattack suggestions)

Data acquisition steps

Acquire data in the following order (can be executed concurrently to improve efficiency):

  1. product_detail - Product details
  2. product_reviews - User reviews
  3. product_traffic_terms - Traffic keywords
  4. competitor_product_keywords - Competitor keyword layout
  5. product_trend - Historical sales trend

For the specific call format, see the Sorftime MCP tool reference section below

Step 2: Execute the four-dimensional analysis

Part 1: Copy construction logic and keyword analysis (The Brain)

Construction logic and methodology:

  • Deconstruct the text construction strategy of the title and five bullet points
  • Analyze whether it is based on "pain point triggering", "scenario driven", or "parameter suppression"
  • Identify the narrative template used

Keyword intelligence:

  • From product_traffic_terms extract the product's core traffic keywords
  • From competitor_product_keywords analyze the competitor's exposure position under each core keyword
  • Identify the competitor's organic exposure capability and traffic acquisition strategy

Data usage:

  • Use product_traffic_terms to analyze the product's traffic sources
  • Use competitor_product_keywords to evaluate the competitor's keyword layout
  • Use keyword_detail to deeply analyze core keyword metrics

Part 2: Product performance and market positioning (The Face)

Basic product data:

  • Price, rating, number of reviews, category ranking
  • Monthly sales volume, sales revenue estimate
  • FBA/FBM fulfillment method

Market performance:

  • Use product_trend to analyze historical sales/price trends
  • Identify seasonal fluctuations and the impact of promotional activities
  • Evaluate the product lifecycle stage

Competitiveness analysis:

  • Use product_report to evaluate the product's position in the category
  • Top100 ranking change trend
  • Price/feature comparison with competitors

Part 3: Quantitative and qualitative review analysis (The Voice)

Quantitative data overview:

  • Clarify the analysis sample size (up to 100 reviews)
  • Count the distribution of positive reviews (4-5 stars) and negative reviews (1-3 stars)

Qualitative deep-dive analysis:

  • Advantage clustering: advantages repeatedly mentioned in user reviews
  • Negative review deep dive: the core issues mainly reflected in negative reviews (product defects, description mismatch, experience issues)

Core summary (Top 3):

  • 3 core advantages (why users buy)
  • 3 core pain points (why users return/leave negative reviews)
  • 3 improvement suggestions (directions for optimizing our product)

Part 4: Market dynamics and blind spot scanning (The Pulse)

Keyword layout analysis:

  • From competitor_product_keywords identify the competitor's main traffic-driving keywords
  • Analyze the competitor's ranking capability under trending search terms
  • Discover competitors' long-tail keyword layout strategies

Market Opportunity Identification:

  • Identify high-value keywords not yet covered by competitors
  • Discover needs mentioned by users in reviews but unmet by products
  • Analyze category trends and competitive landscape

Blind Spot Scanning:

  • Identify potential threats (new products, price wars, brand differentiation)
  • Discover user pain points that are not fully addressed

Step 3: Output a Structured Report

Report Output Methods

  1. Terminal Output: Display the complete report directly in the conversation
  2. Document Saving: Save the report as a Markdown file for later reference

Report File Naming Rules：

analysis_{ASIN}_{站点}_{日期}.md
例如: analysis_B07PQFT83F_US_20260302.md

Save Location：

项目目录/reports/

Save Command：

# 1. 先检查/创建 reports 目录
mkdir -p reports/

# 2. 生成报告文件路径（使用当前日期）
FILENAME="reports/analysis_${ASIN}_${站点}_$(date +%Y%m%d).md"

# 3. 使用 Write 工具保存完整报告内容
Write $FILENAME

Report Saving Best Practices：

  1. Save each analysis as a separate file for easy historical comparison
  2. Include the date in the file name to support analyzing the same product multiple times
  3. Include a timestamp at the beginning of the report to ensure data timeliness
  4. It is recommended to regularly organize old reports and archive them to the reports/archive/ directory

Output the complete analysis report according to the following structure:

# 亚马逊竞品Listing全维度穿透分析报告

## 分析对象
- ASIN: [ASIN]
- 亚马逊站点: [站点]
- 分析时间: [时间]
- 数据来源: Sorftime MCP

## 第一部分：产品基础数据
### 核心指标
- 产品标题: [标题]
- 品牌: [品牌]
- 价格: [价格]
- 评分: [评分] / 5.0
- 评论数: [评论数]
- 月销量估算: [销量]
- 类目排名: [排名]
- 配送方式: [FBA/FBM]

### 市场表现
- 历史销量趋势: [分析]
- 价格波动规律: [分析]
- 生命周期阶段: [判断]

## 第二部分：关键词布局分析 (The Brain)
### 流量关键词
- 核心流量词列表
- 流量来源分布
- 自然曝光能力

### 竞品关键词布局
- 各热搜词下的排名位置
- 获流关键词数量
- 排名竞争力分析

### 文案构建逻辑
- 标题策略分析
- 五点描述策略
- 关键词埋点策略

## 第三部分：评论定性分析 (The Voice)
### 评论数据概览
- 总评分数: [评分]
- 好评率: [百分比]
- 分析样本: [评论数量]

### 核心优势 Top 3
1. [优势1]
2. [优势2]
3. [优势3]

### 核心痛点 Top 3
1. [痛点1]
2. [痛点2]
3. [痛点3]

### 改进建议 Top 3
1. [建议1]
2. [建议2]
3. [建议3]

## 第四部分：竞争策略分析 (The Pulse)
### 竞争优势
- [分析]

### 竞争劣势
- [分析]

### 市场机会
- [分析]

### 潜在威胁
- [分析]

## 战略反击建议
基于用户产品核心优势，提供针对性的竞争策略建议。

### 关键词策略
- [建议]

### 定价策略
- [建议]

### 产品优化方向
- [建议]

### Listing优化建议
- [建议]

Reference Documents

Quick Tool Reference

Tool Purpose Call Cost
product_detail Product Details 1
product_reviews User Reviews (up to 100) 1
product_traffic_terms Traffic Keyword Reverse Lookup 1
competitor_product_keywords Competitor Keyword Layout 1
product_trend Historical Trends 1
keyword_detail Keyword Details 1

Supported Sites

US, GB, DE, FR, IN, CA, JP, ES, IT, MX, AE, AU, BR, SA

Notes

  1. ASIN Format: Ensure the ASIN format is correct, usually a 10-character alphanumeric combination
  2. Site Selection: Use the US site by default
  3. Review Data: Return up to 100 reviews
  4. Concurrent Requests: Multiple requests can be initiated simultaneously to improve efficiency
  5. API Key Security: Do not hardcode the API Key in code

Reference Materials

Sorftime MCP Complete API Documentation

Detailed interface documentation has been saved in references/sorftime-mcp-api.md, including:

Product-related interfaces (9)

Interface Purpose Call Cost
product_detail Product Details 1
product_variations Product Child Item Details 1
product_trend Historical (Sales/Price/Rank) Trends 1
product_reviews User Reviews (up to 100) 1
product_traffic_terms Traffic Keyword Reverse Lookup 1
competitor_product_keywords Competitor Keyword Layout 1
product_keyword_rank_trend Keyword Ranking Trends 1
product_search Product Search/Filter 1
potential_product_search Potential Product Search 1

Category-related interfaces (7)

Interface Purpose Call Cost
category_name_search Category Name Search (Get nodeid) 1
category_tree Category Tree Structure 5
category_report Category Real-time Report (Top100) 1
category_history_report Category Historical Report (up to 40 days) 1
category_trend Category Trends (11 trend types) 1
category_market_search Category Market Search/Filter 1
category_keywords Category Core Keywords 1

Keyword-related APIs (4)

API Purpose Call Cost
keyword_detail Keyword Details 1
keyword_search_result Keyword Search Results Organic Position 1
keyword_trend Keyword Historical Trends 1
keyword_related_words Keyword Related Terms/Long-tail Terms 1

Keyword Library Management (5)

API Purpose Call Cost
add_keyword Add Keyword to Favorites 1
move_keyword Move to Favorites Folder 1
remove_keyword Delete Keyword 1
query_keyword_dict_list Query Favorites Folder List 1
query_keyword_dict Query Favorited Keywords 1

1688 Supply Platform (1)

API Purpose Call Cost
products_1688 1688 Product Search/Procurement Cost Analysis 1

TikTok E-commerce Platform (8)

API Purpose Call Cost
tiktok_product_search TikTok Product Search 1
tiktok_product_detail TikTok Product Details 1
tiktok_product_videos TikTok Sales Videos 1
tiktok_product_influencers TikTok Sales Influencer Analysis 1
tiktok_product_trend TikTok Product Trends 1
tiktok_influencer_search TikTok Influencer Search 1
tiktok_category_name_search TikTok Category Search 1
tiktok_category_report TikTok Category Report 1

Research Dimensions and API Reference Table

When users need to research specific dimensions, use the following APIs:

Amazon Product Research

Research Dimension API Used Key Parameters
Product Basic Information product_detail asin
Sales/Price Trends product_trend asin, productTrendType
User Reviews product_reviews asin, reviewType
Traffic Sources product_traffic_terms asin
Competitor Keyword Layout competitor_product_keywords asin
Keyword Ranking Monitoring product_keyword_rank_trend asin, keyword
Variation Details product_variations asin

Amazon Keyword Research

Research Dimension API Used Key Parameters
Keyword Data Analysis keyword_detail keyword
Keyword Search Results keyword_search_result searchKeyword
Keyword Historical Trends keyword_trend searchKeyword
Long-tail Keyword Mining keyword_related_words searchKeyword

Amazon Category Research

Research Dimension API Used Key Parameters
Category Search (Get nodeid) category_name_search searchName
Category Analysis category_report nodeId
Category Trends category_trend nodeId, trendIndex
Category Keywords category_keywords nodeId
Category Market Filter category_market_search Multiple Filter Parameters

Amazon Product Selection Research

Research Dimension API Used Key Parameters
Product Search/Filter product_search searchName + Filter Parameters
Potential Product Mining potential_product_search searchName, price_range, etc.

TikTok Cross-platform Research

Research Dimension API Used Key Parameters
Similar Product Analysis tiktok_product_search site, searchName
TikTok Product Details tiktok_product_detail site, productId
Sales Video Analysis tiktok_product_videos site, productId
Sales Influencer Analysis tiktok_product_influencers site, productId
Product Trend Tracking tiktok_product_trend site, productId
Influencer Search tiktok_influencer_search site, searchName
TikTok Category Analysis tiktok_category_report site, nodeId

Supply Chain Cost Research

Research Dimension API Used Key Parameters
1688 Procurement Cost products_1688 searchName

Supported Platform Sites

Platform Number of Sites Supported Sites
Amazon 14 US, GB, DE, FR, IN, CA, JP, ES, IT, MX, AE, AU, BR, SA
TikTok 6 US, GB, MY, PH, VN, ID
1688 - Domestic Wholesale Procurement Platform

This skill document version: v2.2 | Last updated: 2026-03-03