Side-by-side comparison of two Model Context Protocol servers — install paths, tools, quality signals, and directory engagement so you can pick the right one for Claude, Cursor, and other MCP clients.
Real-time security framework for MCP servers that detects and blocks malicious AI agent behavior by analyzing tool call patterns and intent across multiple threat detection engines.
Cryptographic provenance, bilateral blind reputation scoring, and tamper-evident logging for AI agent interactions. 7 interlocking trust protocols (CoC, ARP, ASA, AJP, ALP, AMP, CWEP) available in Python (pip) and TypeScript (npm). 663 tests. Bitcoin-anchored provenance chains, anti-Goodhart reputation scoring, machine-readable contracts, dispute resolution, lifecycle management, trust-weighted matchmaking, and context-window cost allocation. Also on Smithery and PyPI.