@operstack/mcp
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Alternatives to @operstack/mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceVendor-agnostic MCP server that audits, scores, and rewrites web pages for AI-citation eligibility. No API keys. No registration. Works in Claude Desktop, Cursor, Cline, Windsurf, VS Code (Copilot / Continue), and any client that speaks the Model Context Protocol.2058 npm3MIT
- AlicenseAqualityAmaintenanceEnables AI agents to check whether a public website is crawlable, understandable, and ready for AI search workflows through local-only audits of robots.txt, sitemaps, metadata, and llms.txt.345 npm1MIT
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- AlicenseNot gradedqualityCmaintenanceOpen-source technical SEO crawler MCP server built on LibreCrawl. Runs full audits inside Claude, Cursor, or Codex — 50+ checks (hreflang, schema.org, security headers, WAF detection on 200-OK pages), chunked-progressive engine for large sites, ephemeral by design (server forgets every audit after download).39MIT
- AlicenseNot gradedqualityAmaintenanceAn open-source, local-first MCP server for automated Schema.org JSON-LD generation, GA4 metric validation, /llm.txt auditing, and content stickiness analytics.MIT
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TDQS
Scored across 4 tools
Each tool has a fairly distinct purpose: single-site audit, multi-site comparison, llms.txt validation, and local content gates. However, compare_sites is essentially a multi-site extension of audit_site, and check_llms_txt overlaps with the AEO/GEO portion of audit_site, so an agent could occasionally waver between them despite the clarifying descriptions.
All four tools follow a clean verb_noun snake_case pattern: compare_sites, audit_site, check_llms_txt, run_gates. The convention is predictable throughout with no style mixing.
Four tools is a reasonable, well-scoped count for a site-measurement server, and each earns its place. It leans slightly thin, since there is no tool for remediation or reporting beyond the core diagnostics.
The surface covers the core diagnostic lifecycle: single-site audit, competitor comparison, a specific llms.txt check, and local corpus gates. Minor gaps remain—no historical tracking, export/reporting, or standalone robots/schema checks—but agents can work around these for the stated purpose.