"How to fix SonarQube findings" matching MCP servers:

A multi-agent research system that decomposes complex queries into targeted sub-questions, searches the web in parallel, scores source credibility, and synthesizes findings into structured markdown reports.

Collective memory for AI agents. One agent solves a bug - every agent in the world gets the fix instantly.

Pre-flight scoring for AI agents, returns Accelerate, Fix, or Stop with EUR value range, decision confidence, and pace-layer drag.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Bug-report triage and fix-dispatch MCP for vibe coders. Ask your AI coding agent for a plain-English diagnosis of any user-reported bug — with evidence, blast radius, and a paste-ready fix prompt — then dispatch an automated GitHub fix PR without a second LLM key. 71 tools, Supabase-backed, MIT SDK. npx @mushi-mushi/mcp

Turn ad-hoc second opinions into a repeatable plan-review checkpoint. One local MCP tool returns attributed findings and one structured verdict on demand.

Interactive feedback layer that lets users pin comments on live web apps with auto-captured context (failing requests, console, AI metadata), and coding agents fix issues via MCP, turning pins green upon verification.

Prevents coding agents from repeatedly attempting the same failed fix by tracking attempts and blocking further fixes until the agent uses its own web search tool.

Enables Codex to delegate coding tasks to an OpenCode CLI locally, returning structured results such as exit codes, session summaries, tool calls, and git diffs.

Reduces thousands of security findings to the smallest set of high-impact remediation actions, with fail-closed safety gates, typed execution plans, and verification that refuses false all-cleans.

Enables routing tasks to the right specialist agents, assembling the needed context from a layered catalog of agents, skills, knowledge, runbooks, topics, tools, and connectors, while remembering how a business wants work done.

Teach any AI agent how you use an MCP server – Soft-Landing to your AI integrations

An MCP server that enables AI clients to autonomously discover, analyze, fix, test, submit, and track open-source contributions on GitHub through specialized tools.

Enables AI coding agents to discover, understand, and fix product issues (UX inconsistencies, accessibility violations, quality problems) identified by Skippr's browser extension through natural language commands.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

AgentRoots is an MCP server for agent continuity that preserves goals, findings, and evidence across sessions. It enables agents to propose, review, and reuse compact, evidence-backed state instead of replaying transcripts.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server for integrating with various LLM clients like Claude Desktop.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.