Enables benchmarking of Large Language Model APIs by measuring performance metrics such as generation throughput, prompt throughput, and Time To First Token (TTFT) with configurable concurrency levels and parameters.
Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.
Probes your live API and classifies why each endpoint failed (root cause, evidence, and a calibrated confidence level), exposed over MCP so your AI assistant debugs from evidence instead of guessing. Works with FastAPI, Express, Next.js, tRPC, and GraphQL.
Turns natural-language API descriptions into runnable k6 load tests, executes them, and returns structured performance metrics for AI-assisted reporting.
Data freshness verification for AI agents. Probes endpoints for HTTP cache staleness, latency percentiles, content fingerprinting, TLS certificate health, DNS timing, and redirect chains.
Returns deterministic FRESH/STALE/UNKNOWN JSON verdicts with policy evaluation and NIST AI RMF mapping.
