Amazon-Suchtrenddaten für KI-Assistenten Messen Sie echte Kaufabsichten von Verbrauchern. Das Amazon-Suchvolumen zeigt, wonach Menschen aktiv suchen – nicht nur, wonach sie recherchieren. Historische Nachfragekurven und Wachstumssignale, abfragbar durch Ihre KI.
Vollständige Dokumentation und Live-Demo: https://trendsmcp.ai/amazon-search-trends
Teil von Trends MCP – dem MCP-Server für Live-Trenddaten aus über 12 Quellen. Siehe das Haupt-Repository: https://github.com/trendsmcp/trends-mcp
Schritt 1: Holen Sie sich Ihren kostenlosen API-Schlüssel unter trendsmcp.ai 100 Anfragen pro Tag, keine Kreditkarte erforderlich.
Schritt 2: Fügen Sie ihn Ihrem KI-Client hinzu (ersetzen Sie YOUR_API_KEY):
+ Zu Cursor hinzufügen (ein Klick)
Cursor / Windsurf / Cline (~/.cursor/mcp.json oder entsprechend)
{
"mcpServers": {
"trends-mcp": {
"url": "https://api.trendsmcp.ai/mcp",
"transport": "http",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}VS Code / GitHub Copilot (.vscode/mcp.json)
{
"servers": {
"trends-mcp": {
"type": "http",
"url": "https://api.trendsmcp.ai/mcp",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}Claude Desktop (claude_desktop_config.json)
Benutzer → Einstellungen → Entwickler → Konfiguration bearbeiten – innerhalb von mcpServers hinzufügen
{
"mcpServers": {
"trends-mcp": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://api.trendsmcp.ai/mcp",
"--header",
"Authorization:${AUTH_HEADER}"
],
"env": {
"AUTH_HEADER": "Bearer YOUR_API_KEY"
}
}
}
}Claude.ai (Browser) Einstellungen -> Connectors -> Benutzerdefinierten Connector hinzufügen:
https://api.trendsmcp.ai/mcpRelated MCP server: amazon-trends-mcp
Fragen Sie nach dem Verbinden Ihre KI:
get_trends(keyword='air fryer', source='amazon', data_mode='weekly')Tool | Was es tut |
| Zeitreihe für ein Keyword in dieser Quelle |
| Wachstum in % über Zeiträume von 1W, 1M, 3M, 6M, 1Y |
| Was ist gerade in dieser Quelle im Trend |
| Top-Themen, sortiert nach Volumen |
Amazon-Produktsuchvolumen-Trends – normalisierte Interesse im Zeitverlauf, die Kaufabsichten widerspiegeln. Dies unterscheidet sich von Google-Suchdaten, da Amazon-Anfragen aktive Kaufabsicht und nicht allgemeine Informationssuche darstellen.
Ja. Vergleichen Sie die Nachfrage nach Produkt-Keywords auf Amazon vs. Google, um zu verstehen, ob die Verbraucherabsicht primär informativ oder transaktional ist. Steigende Amazon-Suche ist ein starkes Kaufsignal.
Ja. Verwenden Sie ein Kategorie-Keyword wie 'bluetooth headphones' oder 'protein powder' und das Signal aggregiert die Nachfrage in dieser Kategorie auf Amazon.
Alle Werte werden auf eine Skala von 0-100 normalisiert, um einen konsistenten Vergleich zu ermöglichen. Eine absolute Volumenschätzung wird ebenfalls zusammen mit dem normalisierten Signal zurückgegeben.
Trends MCP deckt über 12 Quellen in einer Verbindung ab: Google Search, YouTube, TikTok, Reddit, Amazon, Wikipedia, Nachrichtenstimmung, Web-Traffic, App-Downloads, Steam, npm und mehr.
Alle durchsuchen: https://trendsmcp.ai/data-sources
Derselbe API-Schlüssel funktioniert direkt in Python – kein MCP-Host erforderlich.
pip install amazon-trends-mcpimport os
from amazon_trends_mcp import TrendsMcpClient, SOURCE
client = TrendsMcpClient(api_key=os.environ["TRENDSMCP_API_KEY"])
series = client.get_trends(source=SOURCE, keyword="your keyword")
growth = client.get_growth(source=SOURCE, keyword="your keyword", percent_growth=["1M", "3M", "12M"])
top = client.get_top_trends(type="Amazon", limit=10)MIT © Trends MCP
Point-to-point growth for a keyword on one or more sources. Each window is a preset string (12M, 3M, YTD, and the other listed periods). Values are on a 0-100 scale, plus absolute volume when available. Prefer this over get_time_series for growth questions. app downloads and app rankings are keyword sources (Android bundle ID). They are not the App Store / Google Play live boards on get_top_trends. If the request is rate limited or the monthly quota is used up, tell the user their plan limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | One source, or comma-separated sources (e.g. 'amazon, tiktok, youtube'). Valid: 'google search', 'google images', 'google news', 'google shopping', 'youtube', 'wikipedia', 'tiktok', 'reddit', 'amazon', 'news sentiment', 'news volume', 'npm', 'python', 'steam', 'app downloads', 'app rankings'. | |
| keyword | Yes | What to look up. The string format is required by source. Standard sources (google search, google images, google news, google shopping, youtube, wikipedia, tiktok, reddit, amazon, news sentiment, news volume): any name or phrase, e.g. 'nike'. npm: exact npmjs.com package name, case-sensitive. Right: 'react', '@babel/core'. Wrong: 'React', 'React.js'. python: exact PyPI project name. Right: 'pandas', 'requests'. Wrong: 'Pandas'. steam: game display name in plain English, not a Steam App ID. Right: 'Elden Ring', 'CS2'. First Steam store search result wins, so use an unambiguous name. app downloads and app rankings: Android bundle ID only (the id= value on Google Play). Right: 'com.openai.chatgpt', 'com.whatsapp'. Wrong: 'ChatGPT', 'WhatsApp', an iOS App Store ID, or a bundle ID that is not Android. Find it at play.google.com/store/apps/details?id=THIS_PART. If the request includes app downloads or app rankings with other sources, keyword must still be the Android bundle ID. | |
| percent_growth | No | Growth windows. Default if omitted: ['12M']. Each item must be a preset string: '7D', '1W', '14D', '2W', '30D', '1M', '2M', '3M', '6M', '9M', '12M', '1Y', '18M', '24M', '2Y', '36M', '3Y', '48M', '4Y', '60M', '5Y', 'MTD', 'QTD', 'YTD'. Every preset is a two-date comparison. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint, openWorldHint, and idempotentHint annotations, the description adds useful behavioral context: values are on a 0-100 scale with absolute volume when available, and the agent should tell the user their plan limit is reached on rate limiting or quota exhaustion. These details are not present in the structured fields and do not contradict the 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 compact and every sentence earns its place: core operation, output scale, sibling preference, app-source caveat, and rate-limit handling. It front-loads the primary purpose and avoids redundant phrasing.
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?
With a detailed input schema, an output schema, and safety annotations, the description fills the remaining gaps: tool selection, output scale, source disambiguation, and error behavior. It is complete enough for an agent to invoke the tool correctly without further inference.
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 already documents all parameters thoroughly, including valid sources, keyword formatting rules per source, and every preset growth window. The description references preset windows and Android bundle IDs but does not add meaning beyond the schema, so a baseline of 3 is appropriate.
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 opens with a specific action and resource: 'Point-to-point growth for a keyword on one or more sources.' It also names sibling tools to disambiguate itself, stating it is preferred over get_time_series for growth questions and clarifying that app sources are not the live boards on get_top_trends.
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?
It gives an explicit selection rule: 'Prefer this over get_time_series for growth questions.' It also warns against confusing app downloads/app rankings with get_top_trends live boards, and specifies how to handle rate limits or quota exhaustion. This gives the agent clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full historical series for one keyword and one source (0-100 values, plus volume when available). Use for charting or custom math. Not for live 'what's trending now' boards (use get_top_trends). For most growth questions, use get_growth. If the request is rate limited or the monthly quota is used up, tell the user their plan limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Exactly one source per request. Valid: 'google search', 'google images', 'google news', 'google shopping', 'youtube', 'wikipedia', 'tiktok', 'reddit', 'amazon', 'news sentiment', 'news volume', 'npm', 'python', 'steam', 'app downloads', 'app rankings'. | |
| keyword | Yes | What to look up. The string format is required by source. Standard sources (google search, google images, google news, google shopping, youtube, wikipedia, tiktok, reddit, amazon, news sentiment, news volume): any name or phrase, e.g. 'tesla'. npm: exact npmjs.com package name, case-sensitive. Right: 'react', '@babel/core'. Wrong: 'React', 'React.js'. python: exact PyPI project name. Right: 'pandas', 'requests'. Wrong: 'Pandas'. steam: game display name in plain English, not a Steam App ID. Right: 'Elden Ring', 'CS2'. First Steam store search result wins, so use an unambiguous name. app downloads and app rankings: Android bundle ID only (the id= value on Google Play). Right: 'com.openai.chatgpt', 'com.whatsapp'. Wrong: 'ChatGPT', 'WhatsApp', an iOS App Store ID, or a bundle ID that is not Android. Find it at play.google.com/store/apps/details?id=THIS_PART. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint and idempotentHint. The description adds behavioral context beyond those hints: the 0-100 value range, volume availability, and rate-limit/quota handling instructions. This provides useful operational detail that annotations do not convey, though the core safety profile is already covered by hints.
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?
Three sentences, each earning its place: purpose and scope, usage routing, and error handling. Front-loaded with the core purpose; no repetition or fluff. The alternative tool routing is included efficiently.
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 a two-parameter tool with a full output schema, the description covers purpose, usage scenarios, alternatives, and error handling. An agent has all necessary information to decide when to call it and what to expect, with no critical gaps.
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% and both parameters have detailed, source-specific descriptions (e.g., keyword format per source, valid source list). The tool description adds no additional parameter meaning beyond restating 'one keyword and one source', so the baseline for full schema coverage applies.
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 it returns 'Full historical series for one keyword and one source' with a value range and volume detail. It distinguishes itself from siblings get_top_trends and get_growth by stating what it is not for (live trending, growth questions), making the tool's purpose 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?
Directly states when to use it: 'Use for charting or custom math' and when not to: 'Not for live trending (use get_top_trends)' and 'For most growth questions, use get_growth'. Also provides explicit handling for rate-limited or quota-exhausted requests, which is actionable guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', and 'Substack by Category', always pass category. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. For an app's history use get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Do not use get_time_series for live boards. If the request is rate limited or the monthly quota is used up, tell the user their plan limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | How to rank the board. 'rank' (default): current leaders. 'rank_change': biggest climbers vs a prior snapshot. Mover rows include rank, keyword, prev_rank, and rank_change. | rank |
| type | Yes | Exactly one live feed. Valid: 'Amazon Best Sellers Top Rated', 'Amazon Best Sellers by Category', 'App Store Top Free', 'App Store Top Paid', 'GitHub', 'Google News Top News', 'Google Play', 'Google Trends', 'Google Trends by Category', 'IMDb MOVIEmeter', 'Open Library Trending Books', 'Reddit Hot Posts', 'Reddit World News', 'Top Websites', 'Spotify Top Podcasts', 'Steam Most Played', 'Substack', 'Substack by Category', 'TikTok Trending Hashtags', 'TikTok Trending Searches', 'Wikipedia Trending', 'X (Twitter) Trending', 'YouTube Trending'. | |
| limit | No | Max rows to return. Default 25, min 1, max 200. | |
| offset | No | Rows to skip for pagination. Default 0. | |
| window | No | Lookback used only when sort is 'rank_change'. One of '1d', '3d', '7d', '14d', '30d'. Default '30d'. Short windows only work on daily feeds; weekly and monthly feeds return a note pointing to a longer window. | 30d |
| category | No | Pass this whenever type is 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', or 'Substack by Category'. Use the official name. Without it those feeds mix every board. Only omit on a first pull to learn the official names. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond readOnlyHint/idempotentHint, the description adds important behavioral context: rate limits and monthly quota handling, default sort behavior, rank-change snapshot windows, category omission behavior, and feed-type quirks. It discloses how the tool behaves in edge cases, which is exactly the kind of transparency that helps an agent.
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 dense but every sentence earns its place: purpose, category rule, sort semantics, alternative tool routing, and rate-limit behavior are all covered in a compact, front-loaded format. No filler or repetition.
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 six parameters, sibling tools, and an output schema, the description covers everything an agent needs: valid feed types, category requirements, sort/window semantics, pagination defaults handled in schema, alternatives, and rate-limit response. Nothing essential is missing.
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 coverage is 100%, so the baseline is 3. The description adds valuable non-obvious semantics: category is mandatory for four specific feed types and mixing occurs otherwise, and sort='rank_change' compares against a prior snapshot. This goes beyond the schema's own parameter descriptions.
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?
Description states a specific verb and resource: it returns a live top-trending board for exactly one feed type, and explicitly says 'No keyword.' This clearly distinguishes it from sibling tools like get_growth and get_time_series without needing to open the schema.
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 explicit when-to-use guidance: pass category for certain feeds, use sort='rank_change' for climbers, and use get_growth or get_time_series for app history. It even warns 'Do not use get_time_series for live boards,' giving clear exclusions and alternatives.
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_growthget_time_seriesget_top_trendsScored across 3 tools
Each tool targets a distinct purpose: get_growth for point-to-point growth, get_time_series for full historical data, and get_top_trends for live boards. The descriptions explicitly cross-reference and disambiguate overlaps, making misselection unlikely.
All tool names follow the same get_ verb-prefix pattern with clear resource nouns (growth, time_series, top_trends). The naming is uniform and predictable.
Three tools cover the core actions of a trends MCP server well: growth lookup, historical series, and live top trends. The scope is tight and each tool earns its place.
The surface covers primary workflows: historical, growth, and live trending. Minor gaps exist, such as no explicit tool for listing available feed types, categories, or sources, but agents can infer them from descriptions and the core functionality is complete.
Amazon product search volume and best-seller trends. Free key at trendsapi.ai
Real-time Amazon product, seller, and search data for AI agents across 21 marketplaces.
Product and category demand signals across Amazon and social. Free key at trendsapi.ai
Amazon keyword volume, reverse-ASIN, and SERP data across 11 marketplaces.
