"Information about Claude AI or Claude code examples" matching MCP connectors:

Evidence-gated task verification for AI agents. Decompose goals into acceptance criteria, attach proof (screenshot, curl, file), independent LLM judge accepts or rejects. 24 tools. Hosted remote MCP (streamable-http, OAuth 2.1 + DCR).

Check that your AI is being logical. Free tool that mathematically catches contradictions in agent reasoning. No account needed. Also offers paid guardrails that converts natural language to formal verification proofs, that anyone can check succinctly.

Read-only, deterministic AI triage and readiness tools implementing Sophon's published rubrics.

Check if your MCP server is ready to publish on the MCP Registry, Smithery, or npm.

Deterministic validation for AI-generated artifacts: JSON Schema, OpenAPI response, SQL syntax.

Hire a real human for real-world verification, product testing, AI output review, and errands.

Deterministic recipe verification engine — validates AI-generated recipes against master SOPs.

Webhook relay for AI agents: keyless quickstart, event inspection, replay, mock payloads.

Voice-powered bug reporting with 13 MCP tools. Record bugs by talking; let AI find and fix them.

Promotion gate for AI agents: leakage audits, exact-statistics verdicts, and a live report card.

Generate realistic, FK-consistent synthetic test data for your databases from your AI assistant.

Runs your code against a contract; returns HELD or BROKE at the exact input. Deterministic.

Generate deterministic placeholder image URLs and packs for docs, staging, testing, and AI agents.

Read-only MCP server for the OPERANT AI operating-agent calibration benchmark.

AI website growth audits: SEO, performance, AI readiness (GEO), conversion, a11y, security.

Machine-readable taxonomy of 100+ AI system failure modes spanning factuality, alignment, planning, code generation, and instruction following.

MCP server for the Fail Modes taxonomy — a knowledge base of AI system failure modes

Scan URLs for WCAG 2.1 violations, generate AI fixes, and produce VPAT 2.5 compliance reports.

MCP-native AI evaluation: rubric audits, eval suites, and proof reports for AI/LLM output.

A flock of AI users tests your deployed app and reports where real people get stuck, with fixes.