@applyra/mcp-server
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AlicenseAqualityAmaintenanceScans websites to evaluate agent-readiness and produce an ASO Score Report across 34 signals, helping improve discoverability, trust, and interoperability for AI agents.5440 npm1MIT
@sonarapp/mcpofficial
AlicenseAqualityCmaintenanceProvides App Store Optimization tools for AI agents, enabling app lookup, keyword research, ASO audit, review mining, and revenue estimation across iOS and Google Play.43203 npmMIT
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TDQS
Scored across 25 tools
Each tool targets a distinct resource and action: tracking, inspecting, ranking history, metadata checks, simulations, competitors, autocomplete, niche analysis, and charts are clearly separated. Descriptions explicitly cross-reference related tools to prevent misuse, so even with many list-history tools the boundaries remain unambiguous.
Almost all tools follow a consistent snake_case verb_noun pattern (e.g. list_applications, track_keywords, get_aso_health). The single outlier is top_charts, which omits the verb prefix, but the rest of the set is highly predictable.
25 tools cover a broad ASO feature set but sit at the heavy end of the range, with many separate list/get tools for tracked items, inspections, simulations, analyses, and history. The count feels borderline for a single server even though each tool has a distinct purpose.
Core workflows are covered for keywords, competitors, metadata, and research, but application lifecycle management is incomplete: add_application and list_applications exist with no update or delete counterpart. Agents cannot remove or modify a tracked app, which is a notable gap for the domain.