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

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.

An MCP server that exposes the llms.txt file and its referenced local or external resources from a project root to provide context for AI models. It automatically parses documentation links and URLs to make them accessible as additional MCP resources.

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

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

MCP server that exposes GDB debugging as tools. An AI assistant can set breakpoints, run programs, step through code, inspect variables and memory, and examine registers — all via structured tool calls. Reverse debugging with rr is also supported.

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

MCP server for testing GitHub Actions workflows locally using nektos/act.

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.

MCP server for SecretiveShell/Awesome-llms-txt. Add documentation directly into your conversation via MCP resources.

Enables submitting FMF-based test requests to Testing Farm and listing available OS composes for testing.

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 fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.

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.

Persistent memory and pattern learning for AI coding assistants via MCP.

A generic MCP (Model Context Protocol) server for any Hugo blog, deployed on Cloudflare Workers. It reads your blog's llms.txt and llms-full.txt files and exposes them as structured tools that AI assistants can query to understand your content.

Provides a production-oriented MCP server exposing tools, resources, and prompts over Streamable HTTP with JWT authentication and per-tool RBAC, enabling Amazon Bedrock Claude clients to securely invoke MCP capabilities.

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