{"id":"disler-beyond-mcp","name":"beyond-mcp","af_score":51.5,"security_score":46.2,"reliability_score":20.0,"what_it_does":"Provides four example integration patterns for accessing Kalshi prediction market data with AI agents: an MCP server wrapper around a CLI, a standalone CLI with JSON/Human output and local caching, progressive-disclosure file-system scripts, and a Claude Code “skill” that reuses those scripts with team sharing via git.","best_when":"You need agent-accessible market read tools and want to compare/implement patterns that preserve context (scripts/skills) or standardize integration (MCP) while using local caching for search.","avoid_when":"You require robust, documented enterprise-grade interfaces (OpenAPI/SDK, guaranteed error semantics, explicit rate-limit/error recovery guarantees) and audit-ready security controls for production traffic.","last_evaluated":"2026-03-30T13:43:46.854308+00:00","has_mcp":true,"has_api":false,"auth_methods":[],"has_free_tier":false,"known_gotchas":["MCP tool calls are stateless per README (each call loses conversational context).","MCP wrapper delegates to CLI via subprocess, which may add latency and complicate debugging.","Search-first-run cache build can take several minutes; agents/users may see progress while cache is generated.","Skill approach is Claude Code specific (not portable to other MCP/agent runtimes).","File scripts require local filesystem access and correct tool/script loading for progressive disclosure."],"error_quality":0.0}