AdMeter
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GapQuery
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GapQuery is an app ecosystem intelligence platform for developers and micro SaaS founders. It scans 11 major app ecosystems โ Shopify, WordPress, QuickBooks, Atlassian, Xero, Slack, Monday, GitHub, Freshworks, Zendesk, and Zoho โ covering 35,600+ apps to surface market gaps, pricing opportunities, and missing integrations. Connect GapQuery to Claude Code via MCP and use 17 AI-powered tools to discover underserved categories, spot overpriced apps, identify developer whitespace, and analyze cross-ecosystem patterns. Save opportunities to your pipeline and run deep research across market validation, competition, revenue, technical feasibility, keywords, and go-to-market strategy.
AdMeter GapQueryGapQuery's answer:
GapQuery connects directly to your AI coding environment via MCP. Instead of browsing dashboards, you query 11 app ecosystems and 35,600+ apps through natural language, discovering market gaps, pricing opportunities, and missing integrations right where you code. It's market research that meets you in your terminal.
GapQuery's answer:
Most market research tools focus on consumer app stores or require expensive subscriptions. GapQuery is purpose built for B2B app ecosystems like Shopify, QuickBooks, and Atlassian, the platforms where micro SaaS businesses actually get built. It's a one time purchase starting at $29, not a recurring fee, and it integrates directly into Claude Code so insights turn into action immediately.
GapQuery's answer:
Solo developers, indie hackers, and micro SaaS founders who want to build apps for established platforms like Shopify, WordPress, or QuickBooks and want data to validate their ideas before writing code.
GapQuery's answer:
Laravel 12, Livewire 4, MySQL 8, Python (scrapers), Tailwind CSS, and Anthropic's Model Context Protocol (MCP) for AI tool integration.
GapQuery's answer:
GapQuery started as a personal tool. I was building micro SaaS apps and kept manually searching app stores to figure out what was missing. I realized the same gap analysis I was doing by hand could be automated: scrape the ecosystems, normalize the data, and let AI surface the patterns. What began as a spreadsheet became a database of 35,600+ apps across 11 ecosystems.
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