"How to fix SonarQube findings" matching MCP servers:

Enables an MCP-capable assistant to review GitLab merge requests, GitHub pull requests, and pre-push local branches against ticket requirements, domain rubrics, compiler/lint pre-passes, blast-radius analysis, and SonarQube findings, and to draft inline comments or follow-up tasks in the user's chosen language. Nothing reaches a remote host until a human approves the exact content in an interactive terminal.

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Coordination protocol for parallel coding agents, built above Git. The MCP server exposes the full 17-tool lifecycle over stdio: register agents, publish intents with semantic scopes and declared operations, claim work, check for conflicts before code is written, publish ChangeSets, run trusted named verification checks, and record accepted work. Deterministic rules raise findings when two agents'

Enables scanning Python repos for dead code and automatically drafting a branch + PR for the safe subset (unused single-name imports) after running the repo's full test suite. All other findings are reported without modification, and a human always merges.

A read-only Model Context Protocol server that converts local Git changes and JUnit XML reports into traceable release evidence, risk findings, and a rollback checklist.

Enables coding agents to retrieve pull request review findings, reproduce and fix them locally, reply on review threads, report reproduction results, and request fresh reviews.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

Local MCP server for AI agents and vibe coding safety: deterministic risk checks (payments, auth, database, secrets, infrastructure), sessions, checkpoints, policy inspection, and fix prompts. Runs over stdio against a local git repository; no language model judges risk.

Enables humans to review AI-generated code changes through a GUI: agents freeze git diffs and add findings/diagrams, while reviewers annotate and save decisions returned as an exact correction prompt.

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

An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.

Enables AI agents to screen GitHub repositories and pull requests for risk analysis, generating risk scores, findings, and merge-readiness signals.

Manages parallel Git worktrees, enabling work on multiple branches simultaneously without stashing or context switching.

Integrates Zoho Projects, Git, and SonarQube to automate task management, branch creation, code quality checks, and reporting for React developers.

Marketplace where AI coding agents fix GitHub bugs for cash bounties. Posters draft and fund bounties from chat (Stripe Checkout); solvers browse open work, request repo access, submit PRs, and get paid in USDC, ETH, or BTC. 11 tools.

Deterministic, offline root-cause analysis for coding agents: find the commit that introduced a bug, and verify a fix is complete before committing.

Enables full-stack compiler development across the hw-native-sys workspace by combining repository operations (git health, code search, task running) with a knowledge layer for architecture docs, task routing, and abstraction/pass pipeline information.

Enables coding agents to query a codebase's git-history coupling graph to find blast radius, missing coupled files, explain why files change together via PRs/issues, and report how much of the graph was inspected.

Enables coding environments to discover and inspect GitHub-hosted agent skills and sub-agent definitions, review provenance, policy findings, and dry-run plans, and access read-only configuration analysis while installation approval remains in the local CLI.

Inspects workspace-scoped review findings and hosted runs, and can prepare and, after explicit user confirmation, start or rerun an existing hosted review. It avoids exposing raw diffs, full reports, artifacts, screenshots, credentials, or arbitrary local repository access.

A comprehensive MCP server providing secure tools for filesystem operations, Git management, web search, document conversion, npm/.NET project management, and AI generative capabilities (image/video/audio generation and processing) via PiAPI.ai integration.

Enables an OAuth-signed-in user to list their workspaces and inspect concise, workspace-scoped review findings and hosted run summaries. It can also prepare a pull request review and start or rerun an existing hosted review once the user explicitly confirms.

Helps AI read GitHub repository structure and important files. Want to quickly understand what a repo is about? Prompt it with "read https://github.com/adhikasp/mcp-git-ingest and determine how the code technically works".

Enables AI assistants to inspect a Git repository read-only and get a plain-English diagnosis of what state it is in, along with the exact commands to fix it. It also explains pasted Git error messages and can return a neutral summary of branch, upstream, staged and changed files, without ever modifying the repository.

Provides a read-only MCP bridge that lets Claude Web inspect workspace files, git state, and recorded test results while Codex remains the sole executor of changes.

Enables coding agents to read locally submitted code-review comments and diff context over MCP, so they can fix the code and request another review.

An MCP server that enables Claude agents to interactively merge security audit findings by grouping duplicates, selecting canonicals, preserving authorship, and generating edit instructions, all against a GitHub issues repository.

Enables MCP-compatible clients to list workspaces, inspect workspace-scoped review findings and hosted run summaries, and prepare, then after explicit user confirmation start or rerun, hosted reviews without exposing raw diffs, artifacts, or credentials.

Enables MCP-compatible clients to audit public GitHub repositories and receive structured launch-readiness reports with evidence-backed findings, reproducible scoring, and ready-to-paste launch copy.

Enables coding agents to query the dependency graph of a GitHub backlog, retrieve ordered ready work and prerequisite chains, atomically claim issues, and report blockers—so agents work in the correct order and safely in parallel.