"How to fix SonarQube findings" matching MCP servers:

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

Provides tools for managing qualitative research knowledge graphs, enabling structured representation of research projects, participants, interviews, observations, codes, themes, and findings.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Serves as a durable catalog for report definitions and gathered outputs, enabling agents to save findings with source URLs and retrieve the freshest output to compile cited reports.

Enables AI agents to search and submit solutions, ask and answer questions, share findings, and vote on content via DebugBase's collective knowledge base.

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.

An architecture consulting server that reviews multi-agent systems against a knowledge graph of patterns derived from expert literature. It provides grounded recommendations with chapter citations, maturity scoring, and interactive architecture diagrams to identify and fix structural gaps.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Gives Claude persistent, local context about how you work, your weekly agenda, and shipped work, with automatic work logging and git integration—all stored privately on your machine.

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

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.

Local-first error memory for AI coding agents, enabling them to search past fixes before attempting new repairs and save verified cases as Markdown.

Persistent, local memory for AI coding agents that learns how you work, not just what you said. Supports Claude Code, Codex CLI, Cursor, and any MCP client.

Persistent memory server for CMS Combine lessons, enabling saving and searching confirmed error-cause-fix chains across sessions and users.

Autonomous spec-to-product coding-agent CLI. Its MCP server exposes 34 tools over stdio: project state and task-queue ops, memory retrieve/store, code search, quality and verification reports, repo hotspots/co-changes, and structured findings/learnings.

Serves portable behavioral identity models via MCP. Extracts how a person thinks, communicates, and makes decisions from text, then provides always-on identity context, semantic fact retrieval, keyword search, and provenance tracing through 4 tools and 1 resource.

Crypto-aware project memory for AI coding agents. Typed entities for Solana Programs/PDAs and EVM Contracts across Base, Optimism, Polygon, Arbitrum, Ethereum — plus chain-agnostic Decisions, Findings, and Integrations. Anchor + Hardhat auto-ingest, SQLite + FTS5 BM25 ranking, append-only versioning, git-aware diffs.

Local-first semantic memory server with project indexing for AI assistants. It enables AI assistants to store, retrieve, and search memories and project code using embeddings and vector search.