Payload Sample MCP Server
OfficialRelated Servers
Alternatives to Payload Sample MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseBqualityBmaintenanceA deliberately small MCP server that demonstrates security hardening against the OWASP MCP Top 10 with tools for file search, record queries, and document fetching, each defended against path traversal, SQL injection, and SSRF.4MIT
- FlicenseNot gradedqualityDmaintenanceA deliberately insecure MCP server designed as a pentest lab to demonstrate common vulnerabilities in MCP deployments.-
- AlicenseNot gradedqualityDmaintenanceA production-ready MCP server template with OAuth 2.1, RBAC, and audit logging for building secure, observable tool servers.MIT
- AlicenseNot gradedqualityCmaintenanceA demo MCP server for validating security scanning capabilities, featuring intentional security anti-patterns such as email exfiltration, SSRF, and hardcoded fake secrets.364 npmMIT
- AlicenseAqualityDmaintenanceA compact MCP server demonstrating explicit tool boundaries, least-privilege discovery, execution-time authorization, destructive-action confirmation, and metadata-only audit logs using a local note store.3MIT
- FlicenseNot gradedqualityDmaintenanceAn intentionally vulnerable MCP server designed as a live demo target for the MCP Trust security scanner. It contains deliberate insecure patterns to demonstrate scanning capabilities.-
TDQS
Scored across 3 tools
word_count, summarize, and extract_keywords each target a clearly different text operation, though summarize and extract_keywords overlap somewhat in that both perform content-level analysis of the same input.
All names use snake_case and are readable, but the pattern varies: word_count is noun-based while summarize and extract_keywords are verb-based, so the convention isn't fully uniform.
Three tools is on the thin side even for a sample server; the text-utility domain could reasonably support a few more operations, but the small surface is defensible given the explicit 'sample' framing.
The set covers basic text stats, summarization, and keyword extraction, but leaves obvious gaps like sentiment, readability, or other transformations, so coverage is partial rather than a full text-analysis lifecycle.