@darkmoon_ai/mcp-server
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Alternatives to @darkmoon_ai/mcp-server
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Related Servers
- AlicenseNot gradedqualityAmaintenanceEnables authorized penetration testers to run reconnaissance, enumeration, vulnerability scanning, brute-force checks, traffic capture, and exploitation workflows via MCP tools, with STDIO/HTTP/SSE transport support. It also manages engagement records and generates client reports.98 npmGPL 3.0
- AlicenseNot gradedqualityAmaintenanceEnables authorized penetration-testing workflows through MCP by exposing tools for network scanning, web fuzzing, subdomain enumeration, vulnerability scanning, brute-force checks, packet capture, and report generation with HTTP/STDIO/SSE transports and bearer/OIDC authentication.98 npmGPL 3.0
- AlicenseNot gradedqualityAmaintenanceEnables authorized penetration testers to run reconnaissance, scanning, brute-force, exploitation, traffic capture, wordlist generation, and client-report workflows through MCP clients, with stdio/HTTP/SSE transport and OIDC bearer authentication.98 npmGPL 3.0
- AlicenseCqualityBmaintenanceEnables authorized pentest and bug bounty workflows from any MCP client, with scoped recon, per-host rate limits, and scanner output turned into deduplicated, triaged finding cards.941 PyPI2MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI clients to drive professional penetration-testing workflows—subdomain enumeration, host probing, content fuzzing, vulnerability scanning, brute-force, traffic capture, and exploitation sweeps—through a single MCP interface with stdio, Streamable HTTP, and legacy SSE transports plus bearer/OIDC auth. It also records engagement artifacts and generates client-ready reports with scope handling, cutting manual pentest administration.98 npmGPL 3.0
- AlicenseNot gradedqualityAmaintenanceEnables authorized penetration testers to run reconnaissance, scanning, brute-force, traffic-capture, and exploitation tools through MCP clients, with authentication, engagement recordkeeping, and client report generation.98 npmGPL 3.0
TDQS
Scored across 4 tools
Each tool targets a distinct action (start, status, list campaigns, get findings), but the relationship between a 'run' and a 'campaign' is not fully clear, which could cause slight misselection between get_run_status and list_campaigns when checking progress.
All names follow a consistent snake_case verb_noun pattern (run_pentest, get_run_status, list_campaigns, get_findings), with clear verb prefixes that are easy to predict.
Four tools is well-scoped for a focused pentest service; each tool (start, monitor, list campaigns, read findings) earns its place without redundancy or bloat.
Core start-monitor-results workflow is covered, but notable gaps exist: no cancel/stop for long-running runs, and no explicit way to map a run_id to its resulting campaign, forcing agents to guess from list_campaigns.