steam-feedback-inspector

Introduction

Data analysis involves inspecting, transforming, and modeling information to reveal beneficial insights and guide subsequent actions. This tool focuses a specific lens on consumer feedback data within the digital games sector, similar to how text analytics applies statistical methods to textual material. It aims to transform raw player submissions into structured data points relevant for comprehensive game evaluation.

Features

This MCP service assists intelligent systems in obtaining detailed records and descriptive metadata for Steam-distributed games:

• Fetch player critiques, including sentiment distribution (positive/negative counts) and associated score metrics.
• Retrieve core product specifications, such as the formal title and comprehensive product narrative.
• Synthesize player feedback to derive summary assessments detailing observed strengths and acknowledged shortcomings.

Setup

Installing via Smithery

Installation of the Steam Feedback Inspector for Claude Desktop can be automated utilizing the Smithery utility. Execute the following command to deploy the necessary components directly:

npx -y @smithery/cli install @fenxer/steam-review-mcp --client claude

To initiate execution immediately without a formal system-wide setup, use the following invocation:

npx steam-review-mcp

Alternatively, integration into a local configuration file is possible by including this structure within the relevant settings:

{
  "mcpServers": {
    "steam-review-mcp": {
      "command": "npx",
      "args": [
        "steam-review-mcp"
      ]
    }
  }
}

Usage

Tools

The core functionality of this MCP service is exposed via the get_steam_review operational tool. This mechanism requires supplying the Steam application identifier (appid) to acquire both player commentary and associated product metadata. Refer to the official Steamworks documentation for deeper context regarding user review retrieval mechanisms: User Reviews - Get List.

The output structure is compartmentalized into two primary data groups:

1. game_reviews:
2. success: Indicates the operational outcome of the data query.
3. review_score: The aggregated rating assigned to the product.
4. review_score_desc: A textual designation corresponding to the numerical rating.
5. total_positive: The accumulated count of favorable player endorsements.
6. total_negative: The accumulated count of unfavorable player criticisms.

7. reviews: The raw textual content of individual player submissions, excluding ancillary data.

8. game_info:

9. name: The registered commercial designation of the software.
10. detailed_description: The extensive informational text provided by the developers.

Prompts

summarize-reviews

This specific prompt structure is designed for deriving a holistic assessment of a title, producing a summary that delineates its perceived advantages against its disadvantages.

Parameters

• appid (required): The unique identifier assigned by Steam to the product, for instance, 570 (representing Dota 2).

recent-reviews-analysis

This tool invocation focuses on synthesizing contemporary player responses to gauge the current operational status and recent community sentiment regarding the software.

Parameters

• appid (required): The unique identifier assigned by Steam to the product, for instance, 570 (representing Dota 2).

Development

For those interested in local modification or maintenance of this component, the following operational commands are relevant for managing the source code and deployment cycle:

# Install dependencies
npm install

# Build project
npm run build

# Run service
npm start

Related Topics

• Descriptive statistics
• Exploratory Data Analysis (EDA)
• Confirmatory Data Analysis (CDA)
• Predictive analytics
• Business intelligence aggregation
• Unstructured data processing

Extra Details

While this utility focuses on retrieving specific textual and aggregated feedback, broader data analysis principles often involve techniques like data mining. Data mining emphasizes statistical modeling primarily for discovering knowledge that facilitates predictive forecasting rather than just describing current states. Techniques such as EDA aim to uncover unexpected features hidden within datasets, which can be crucial when analyzing large volumes of disparate user reviews for emergent issues not immediately apparent from aggregate scores.

Conclusion

This inspection utility transforms raw Steam feedback into quantifiable data useful for informed conclusions within the data-analytics sphere. By systematically extracting and structuring player sentiments, it supports rigorous, fact-based assessments of digital product reception. Effective utilization contributes to making game evaluation processes significantly more scientific and systematic.