"How to use make.com" matching MCP connectors:
Matching Connector Tools:
Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
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).
Free platform to test MCP clients without installing anything. Create mock tools with dynamic templates, configurable delays, conditions (if/then), and response sequences. Supports JSON-RPC 2.0 over Streamable HTTP. Built-in text_echo and json_echo tools. Rate-limited tiers: anonymous (5 calls/min, 1 mock tool), registered (10 calls/min, 4 mock tools), premium (60 calls/min, unlimited). Zero setup — no install, no registration required. More info: https://www.testmcp.dev
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.
Agentic code review, no signup to try: reality gates + frontier-model review, with veto.
Real-browser WCAG audit that also finds keyboard-inoperable controls axe-core misses, with fixes.
Find MCP servers and check whether they actually respond, via live handshake probes.
Give AI coding agents access to your Vynix visual feedback, bug reports, and AI diagnosis.
Check if your MCP server is ready to publish on the MCP Registry, Smithery, or npm.
Grade MCP servers A to F with the open behavioral litmus. npm: full toolset; hosted: lookups only.
Scores any MCP server before you trust it: free quick check, full paid report, 2-5 way compare.
Runs your code against a contract; returns HELD or BROKE at the exact input. Deterministic.
Test the voice agents you run: scored transcripts, pass/fail verdicts, latency and WER metrics.
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
MCP-native AI evaluation: rubric audits, eval suites, and proof reports for AI/LLM output.
PQS scores any prompt before the model runs. 8 dimensions. 5 frameworks. Pre-flight, not post-hoc.
MCP-native AI browser testing for coding agents. Submit a URL + goal, get back action trail, bugs, screenshots, and WebM video your agent patches from directly. 43 tools, 12 AI evaluation personalities, combo tiers with auto-pause-on-bugs, throwaway email + SMS inboxes.