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steam-trends-mcp

Steam Trends MCP Works with Claude Works with Cursor

Steam-Spieler-Trenddaten für KI-Assistenten Verfolgen Sie gleichzeitige Spielerzahlen und Spielmomentum für jeden Titel auf Steam. Spieler-Trenddaten zeigen, welche Spiele wachsen, welche zurückgehen und wann große Veröffentlichungen oder Updates zu Spitzen führen.

Vollständige Dokumentation und Live-Demo: https://trendsmcp.ai/steam-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


Erste Schritte in 2 Schritten

Schritt 1: Holen Sie sich Ihren kostenlosen API-Schlüssel unter trendsmcp.ai 100 Anfragen pro Tag, keine Kreditkarte erforderlich.

Schritt 2: Fügen Sie zu 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 -> Verbindungen -> Benutzerdefinierte Verbindung hinzufügen:

https://api.trendsmcp.ai/mcp

Related MCP server: Steam Review and Forum MCP

Beispielabfrage

Nach der Verbindung fragen Sie Ihre KI:

get_trends(keyword='Counter-Strike 2', source='steam', data_mode='weekly')

Verfügbare Werkzeuge

Werkzeug

Was es tut

get_trends

Zeitreihe für ein Schlüsselwort in dieser Quelle

get_growth

Wachstum % über 1W, 1M, 3M, 6M, 1Y Zeiträume

get_top_trends

Was gerade in dieser Quelle im Trend liegt

get_ranked_trends

Top-Themen nach Volumen sortiert


Häufig gestellte Fragen (FAQ)

Normalisierte Trends der gleichzeitigen Spielerzahlen (Skala 0-100) für jedes Spiel auf Steam. Gibt wöchentliche Zeitreihen, Wachstumsraten und Spitzen-Spielerdaten zurück. Nützlich zur Verfolgung des Spielmomentums vor und nach Veröffentlichungen, Updates oder Verkaufsaktionen.

Wie identifiziere ich ein Spiel für die Abfrage?

Verwenden Sie den Anzeigenamen des Spiels, wie er auf Steam erscheint – zum Beispiel 'Counter-Strike 2', 'Palworld' oder 'Elden Ring'. Der MCP-Server löst den Namen automatisch zur korrekten Steam-App-ID auf.

Was ist eine gleichzeitige Spieleranzahl?

Die Anzahl der Spieler, die gleichzeitig aktiv im Spiel sind, erfasst von Steam. Die Spitzenzahl der gleichzeitigen Spieler (PCU) ist eine Standardmetrik zur Messung der Beliebtheit eines Spiels und des Live-Engagement-Levels.

Wie ist dies für Investitionen oder Marktforschung nützlich?

Spielestudios, Investoren und Analysten nutzen Steam-Spielertrends, um das kommerzielle Momentum eines Titels zu messen, Ausreißer-Spiele frühzeitig zu identifizieren und zu verfolgen, wie ein Spiel Spieler im Laufe der Zeit bindet.

Wie weit reichen die Daten zurück?

Bis zu 5 Jahre wöchentliche Daten, die Ihnen die vollständige Veröffentlichungshistorie, Saisonzyklen und langfristige Bindungstrends für jedes Spiel auf Steam geben.


Alle Datenquellen

Trends MCP deckt über 12 Quellen in einer Verbindung ab: Google Search, YouTube, TikTok, Reddit, Amazon, Wikipedia, Nachrichtenstimmung, Webverkehr, App-Downloads, Steam, npm und mehr.

Alle durchsuchen: https://trendsmcp.ai/data-sources


Funktioniert auch als Python-Client

Der gleiche API-Schlüssel funktioniert direkt in Python – kein MCP-Host erforderlich.

pip install steam-trends-mcp
import os
from steam_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="Steam", limit=10)

Vollständige Python-Dokumentation: trendsmcp.ai/docs

Lizenz

MIT © Trends MCP

Available Tools

3 tools
get_growthA
Read-onlyIdempotent
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
sourceYesOne 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'.
keywordYesWhat 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_growthNoGrowth 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.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.7/5.0
Behavior5/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

get_time_seriesA
Read-onlyIdempotent
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
sourceYesExactly 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'.
keywordYesWhat 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.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv1.0.7
    • First observedget_growth
    • First observedget_time_series
    • First observedget_top_trends

TDQS

A4.7/5.0

Scored across 3 tools

Disambiguation5/5

Each tool targets a distinct query type: get_growth handles point-to-point growth, get_time_series provides full historical series, and get_top_trends returns live top-trending boards. The descriptions explicitly cross-reference when to prefer one tool over another, so there is little risk of misselection.

Naming Consistency5/5

All three tool names follow the same get_<specific_noun> pattern using snake_case. The verb prefix is consistent and the object distinguishes the data shape clearly.

Tool Count5/5

Three tools is within the ideal 3-15 range and each one earns its place by covering a distinct data retrieval mode. No redundant or filler tools exist.

Completeness4/5

The set covers the core needs for this domain: historical series, growth calculations, and live trend boards. A minor gap is that there is no explicit tool for listing available sources or categories, though the descriptions mention them inline.

Maintenance

ActivitySlowing
ResponsivenessNo issues

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