Enables testing AI safety classifier robustness against query decomposition, obfuscation, and multi-agent attacks. Provides tools for full evaluation pipelines, query previews, and status checks.
Enables LLM evaluation and observability by uploading documents, building test sets, running RAG pipelines, and automatically scoring answers for groundedness, hallucination risk, retrieval quality, latency, and cost, with tools exposed to MCP-compatible clients.
Enables AI assistants to interact with Coval's evaluation platform for launching and monitoring evaluation runs, managing agents and test sets, and retrieving evaluation metrics.
Enables LLM clients to generate standardized test cases, perform quality control with lint scoring, convert to Xray/Jira format, and compose test suites (Smoke/Regression/E2E) with coverage analysis.
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.