A
licenseA
qualityB
maintenanceEnables investigating Steam games through player-written reviews, rating history, patch notes, and gaming press articles, with review filtering by sentiment, time, and playtime.
5
MIT
steam-mcp
by 312-dev
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- AlicenseNot gradedqualityBmaintenanceEnables AI agents to recommend Steam games from natural language by searching games, finding similar titles, filtering by tags and platform, and retrieving detailed game metadata.1MIT
- FlicenseNot gradedqualityCmaintenanceHelps users choose which Steam games to play or buy by analyzing their library, playtime, discounts, and reviews.-
- AlicenseAqualityAmaintenanceAbout Search Steam games, fetch user reviews, and analyze sentiment with topic drill-down to make informed purchasing decisions.724 npm3MIT
- AlicenseAqualityAmaintenanceEnables interaction with Steam: search games, get store details, reviews, prices, discounts, news, and player profiles, libraries, and achievements via the Steam Web API.2594 npm4MIT
- AlicenseNot gradedqualityCmaintenanceEnables exploring Steam store reviews and community discussion threads with server-side filtering, temporal analysis, and metadata context for understanding player sentiment and feedback beyond store page noise.12 npm1BSD 3-Clause
TDQS
A4.4/5.0
Scored across 10 tools
Disambiguation5/5
Each tool targets a distinct action and data source: taste profiling, recommendation, raw search, details, reviews, similar games, tags, and player-specific library/wishlist/recent activity. Even the three discovery tools are clearly separated by input type (preference statement, structured filters, seed games) and output purpose.
Naming Consistency5/5
All tool names follow a consistent lower_snake_case verb_noun pattern, with get_ as the dominant verb and action-specific verbs like recommend, search, find, and list used uniformly. There is no mixing of conventions or vague generic names.
Tool Count5/5
The 10 tools are well-scoped for a Steam-focused MCP server: they cover discovery, personalization, game details, reviews, and player data without unnecessary duplication or bloat. Each tool earns its place in the workflow.
Completeness5/5
The tool surface forms a complete read-only pipeline: understand the player, query the catalog, refine by tags, get details/reviews, and generate or sanity-check recommendations. The only possible gaps (e.g. account actions) are outside the server's apparent purpose.
Maintenance
ActivityMaintained
ResponsivenessNo issues