"Automating Testing and Debugging for LLMs in Production" matching MCP servers:

An MCP server that gives AI agents direct access to VMware vSphere infrastructure, enabling VM lifecycle management, snapshots, datastore operations, networking, and more via 55 typed tools built on govc.

Enables LLMs to automatically diagnose coding errors through codebase search, test execution, and live debugger integration (DAP/V8 CDP). Provides a secure, policy-gated environment for investigating failures while preventing destructive operations.

Enables triggering, streaming, and reviewing autonomous production-incident investigations directly from your editor, with human-gated approval for any mutating actions.

Enables auditing any website for AI-answer readiness, returning a score and actionable fix list based on llms.txt, schema, sitemap, and related checks.

Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.

Provides testing and quality assurance tools for AI agents via MCP, enabling generation of test cases, mock data, API mocks, coverage analysis, and assertions.

Code-aware browser testing agent — reads your codebase, understands functionality, tests every element, reports with screenshots. Works as MCP server for Cursor/Claude Code or standalone CLI.

MCP server for the Production Master incident-investigation service, enabling any MCP client to drive investigations via tool calls with pass-through authentication.

A production-ready MCP server template with OAuth 2.1, RBAC, and audit logging for building secure, observable tool servers.

Provides intelligent error detection and debugging capabilities across multiple programming languages with real-time monitoring of build, lint, runtime, console, and test errors. Offers AI-enhanced error analysis with automated resolution suggestions and context-aware debugging.

Enables agents to discover the llms.txt index covering any site, fetch its linked documentation pages as markdown, and search across parsed indexes by section heading, link title, and notes.

A hexagonal TypeScript starter for production MCP servers with built-in authentication, billing, and tool scaffolding.

An LLM-driven API testing platform exposed over MCP, enabling declarative test definition and DAG-based execution.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

MCP server for amazon.in — cheapest in-stock, best-value picks, and price history links.

Provides coding agents with visibility into test health through tools for flaky test detection, test quality linting, and LLM evaluation harness, enabling them to triage failures, review test quality, and check prompt changes for regressions.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

A lightweight, local-first MCP server for executing HTTP requests and managing API collections and environments without cloud dependencies. It enables testing APIs, handling authentication, and importing OpenAPI specifications directly within MCP-compatible workflows.

Connects AI agents to live observability stacks including Sentry, GitHub, Vercel, Better Stack, and Cloudflare, enabling end-to-end incident investigation, deployment correlation, root cause analysis, and regression triage.

Provides tools for frontend testing including code analysis, test generation, test execution, and React component testing for Jest and Cypress frameworks.