"How to fix SonarQube findings" matching MCP servers:

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.

CodebaseAgent-MCP is an MCP server that uses a lightweight RAG pipeline to delegate repository analysis to a dedicated OpenAI-compatible LLM, reducing token consumption by having the primary assistant receive only the relevant findings instead of repeatedly scanning the entire codebase.

Enables AI assistants to perform comprehensive research by searching Google, mining Reddit discussions, scraping web content with JS rendering, and synthesizing findings with citations into structured context.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Persistent AI memory for Claude Code, Cursor, GitHub Copilot & Windsurf — sessions, lessons learned, semantic search, and team brain. 38 MCP tools. Free tier, EU servers.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables read-only search over a curated, provenance-preserving corpus of EVM smart-contract security knowledge, providing tools for retrieving audit findings, document context, and source information.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables AI agents to access and search up-to-date Solana documentation, get canonical spec references, and fix Anchor/Pinocchio Solana programs via MCP tools.

Enables read-only querying of a verified knowledge library through an API and MCP interface, letting people and agents ask questions about systems discovery findings.

Enables MCP clients to query previously analyzed GitHub repositories for architecture analysis, code review findings, and single-turn code Q&A with verifiable file and line references.

Enables running durable, traceable AI agents via LangGraph through a universal MCP interface, integrating with Hatchet for orchestration, logging, and retries. Provides tools for knowledge management (ingestion, RAG) and Kubernetes operations (diagnosis, auto-fix).

Enables scientific literature research through multi-agent search, analysis, and semantic memory, exposing 9 MCP tools for querying, storing, and retrieving research findings.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables Claude AI coding assistants to persist and restore context across sessions, including saved findings, decisions, progress, checkpoints, file caches, and searchable topic channels. Supports shared memory across parallel sessions and Git-aware context organization.

Enables AI agents to share persistent memory by capturing, classifying, synthesizing, and retrieving knowledge through token-optimized, three-tier retrieval with hybrid keyword and semantic search. Supports bidirectional Obsidian sync, session and bug-fix capture, and a self-learning loop that compounds context across sessions.

Enables debugging by analyzing bug reports, tracebacks, or code snippets to retrieve similar historical fixes and generate JSON answers with root cause, fix, and confidence.

Enables AI clients to search curated, cited knowledge bases built from founders', CEOs', and investors' public talks, essays, and posts, returning passages with source links and dates. It also exposes available source collections via MCP tools so users can ask how those individuals might approach business, product, or growth questions.

Enables AI coding agents to store and search team and personal know-how as small entries across local SQLite and hosted Postgres stores, with federated search and writes to the chosen store so new sessions build on prior knowledge.

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.

A lab demonstrating how to deploy a Model Context Protocol (MCP) server and integrate it with Microsoft Copilot Studio, allowing users to connect AI models to different data sources and tools through a standardized protocol.

Enables AI agents to access detailed research findings, implementation patterns, and best practices for building graph-enhanced retrieval-augmented generation systems.

Provides AI assistants a local, hybrid semantic+keyword memory vault to save research findings, notes, and project context, and recall them across sessions with session boot/save tools.

This MCP server replicates how a creative strategist gathers context by expanding queries and performing hybrid search against a Google Drive knowledge base, delivering structured, role-categorized context for ad copy and creative briefs.

A Model Context Protocol server that helps users migrate subscription businesses from RevenueCat to Adapty through natural language interactions with LLMs like Claude Desktop.