"Cómo ver errores y advertencias en Visual Studio Code" matching MCP servers:

Enables coding agents to develop on one machine and verify results on another, using branch-bound runbooks, isolated checkpoints, and structured receipts to guide repair iterations.

Reviews every PR for security, bugs, and performance, then fixes the findings through MCP, keeping you in control of what merges.

This server enables AI assistants to directly control the Unity Editor via MCP, supporting scene manipulation, script editing, level generation, monetization management, build pipelines, and more—all with zero external dependencies.

Enables AI assistants to review GitLab merge requests by fetching changes, analyzing diffs, adding comments, and managing approvals through the GitLab API. Supports complete merge request analysis, file-specific reviews, and version comparisons.

A hosted remote MCP server for managing Android release gates, with tools to check release status, explain permission blockers, issue release receipts, summarize device matrices, and export release logs.

MCP server for IBM Code Engine — build, push, and deploy containers from Cursor, Copilot, Claude, and Cline using natural language.

Enables AI-assisted development by running and testing code directly on Databricks clusters via natural language, then deploying Databricks Asset Bundles.

Generates VS Code installation buttons and markdown badges for MCP servers, supporting both Stable and Insiders versions with configurable inputs and commands.

Enables automated AI-powered code review for pull requests across GitHub, GitLab, Bitbucket, and Azure DevOps via webhooks, and manual code review through MCP tools using Groq, Claude, or GPT-4.

Enables coding agents to access approved user stories and acceptance criteria via MCP, with tools to list, retrieve, update, and check story requirements against the working tree.

RegressGuard is an MCP server that records a known-good baseline and lets AI coding agents detect regressions (broken API contracts, failing tests, schema changes) during their edit loop, allowing them to self-correct before committing.

Agent-loop coverage governance that provides coverage feedback to AI coding agents before commit, preventing regressions from reaching CI.

An MCP server for automated code reviews on Azure DevOps pull requests, enabling AI assistants to fetch PR details, validate requirements, post inline comments, and set review votes.

Enables automated Python code quality checks including linting, complexity analysis, typo detection, structure validation, duplicate detection, and test coverage, with integration into Cursor IDE and CLI.

Enables AI assistants to interact with ArgoCD APIs through standardized MCP tools for managing applications, resources, and deployments.

An MCP server that provides typed, audited, least-privilege tools for monitoring AWS infrastructure and GitHub pipelines, including instance health, service status, logs, deployment status, CI workflow runs, failed jobs, and PR checks.

A three-layer funnel AI code review engine that reduces LLM token consumption by 70%+ while providing deep code analysis. It integrates with GitHub PRs via MCP protocol to automate code review with static analysis, RAG, and LLM-based checks.

A Model Context Protocol server that monitors shadow-cljs builds and provides real-time build status updates for ClojureScript projects, allowing LLMs to verify build status after making code changes.

Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp

MCP server that lets coding agents test AI agents. Create YAML test cases, snapshot golden baselines, check for regressions, and generate visual reports all from inside Claude Code or any MCP-compatible tool. Works with LangGraph, CrewAI, OpenAI, Claude, Mistral, and any HTTP API.