"Troubleshooting and debugging errors" matching MCP servers:

Enables deployment and management of applications on the Citizen platform from git repositories or local files. It allows AI assistants to monitor deployment logs, handle authentication, and automatically fix build errors using intelligent error analysis.

Exposes tools to list, run, and inspect GitHub Actions workflows locally via act, enabling debugging and fixing failures through an agent loop.

An MCP server designed for React Native and Expo development that provides specialized tools for project scaffolding, architectural best practices, and troubleshooting. It enables AI assistants to guide users through setup, navigation configuration, and CI/CD processes using modern stacks like NativeWind and Zustand.

Enables natural language interaction with Kitware CDash for browsing dashboards, triaging CI failures, inspecting build errors and test details, and analyzing coverage.

Enables monitoring and debugging of Hatchet workflows and jobs through MCP clients like Claude Code. Users can list workflows, track run statuses, query queue metrics, and retrieve job results using natural language commands.

MCP server for Xcode build automation and log parsing that opens Xcode projects, triggers builds directly in Xcode, and parses build logs to extract errors and warnings.

Integrates Honeybadger error tracking with Cursor IDE, allowing developers to fetch, analyze, and troubleshoot application errors directly from their development environment.

Enables AI assistants to interact with Netflix Conductor Workflow Engine for troubleshooting workflows, managing executions, and creating/updating workflow and task definitions through natural language.

Enables searching, reading, and analyzing discussions on GitLab's community forum for troubleshooting CI/CD issues and GitLab features. Pre-configured with GitLab-specific search filters and optimized workflows for support scenarios.

A Model Context Protocol server that provides comprehensive Electron application automation, debugging, and observability capabilities through Chrome DevTools Protocol integration.

Bridges AI IDEs with Tencent CloudBase for seamless deployment, enabling users to go from AI-generated code to live applications with automatic cloud resource configuration, database setup, and intelligent debugging.

Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.

A production-ready template for building Model Context Protocol (MCP) servers in Python, using Docker Compose for containerized development and CI/CD.

Enables AI agents to monitor and debug GitLab CI/CD pipelines by providing tools to list pipelines, retrieve job logs, and analyze failure root causes. It features automated error pattern extraction and failure analysis to streamline pipeline troubleshooting.

Query Prometheus from CLI or any MCP client, with retries, caching, and an optional metrics exporter.

Enables AI agents to interact with an Oracle CPQ BML workspace, allowing operations like pulling, validating, debugging, and deploying BML functions locally without exposing credentials.

A boilerplate for building FastMCP servers, providing a basic setup for testing, linting, formatting, and publishing to NPM.

Enables AI assistants to interact with Jenkins CI/CD systems for build management, job monitoring, console log analysis, and debugging through natural language commands.

An MCP server that enables AI assistants to interact with WeChat Mini Programs, allowing developers to publish versions, analyze package size, diagnose compilation errors, and manage projects via natural language.

Provides Kubernetes deployment intelligence with typed query tools and AI-synthesized risk briefs, enabling users to list workloads, get detailed snapshots, query Prometheus metrics, record deployment history, and generate risk assessments before promoting to production.