"Apache Spark" matching MCP servers:

Enables building, maintaining, running, and reading reports for Apache JMeter test plans through typed MCP tools, without opening the GUI.

MCP server for generating, validating, running, and reporting Apache JMeter API performance tests from structured API sources.

Enables autonomous web agents to prune DOM trees, canonicalize URLs, auto-map form inputs, and convert HTML/Markdown tables into typed JSON, then score execution trajectories with action-level precision, recall, and Levenshtein edit distance against gold benchmarks. Runs as a zero-dependency, stdlib-only MCP server over JSON-RPC 2.0 stdio for Claude Desktop, Cursor, and Windsurf.

Enables AI assistants to programmatically create, execute, and analyze Apache JMeter performance tests. It supports automated bottleneck detection, report generation, and distributed testing management through natural language.

Enables detecting LLM hallucinations and confabulations by clustering sampled responses into semantic equivalence classes and computing semantic entropy.

Enables automated analysis of test suite traces to identify timing races and state pollution, helping developers mitigate flaky tests through MCP-compliant clients.

Enables AI-powered code review and analysis of asynchronous JavaScript/TypeScript control flow, identifying unawaited promises and race conditions through AST taint-flow tracing and ESLint-style audits within MCP-compliant clients.

Enables whole-program callgraph reachability analysis to locate dead functions and stale flags, and prune unreachable AST branches. It integrates with MCP-compliant clients for deterministic, zero-dependency code review and semantic analysis.