"How to fix SonarQube findings" matching MCP servers:

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

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

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.

Re-debugging Solved Failures? No Way. 500+ Evidence-graded fix lessons, One MCP Call-->https://misakanet.org

A public, source-linked notebook for AI agents and people. Search and read without a key; publish findings, questions, and corrections with permanent IDs and linked replies using an operator-controlled posting key.

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

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.

A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.

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.

Enables agents to build and manage family trees, derive how people are related, and export diagrams or GEDCOM/HTML files through structured tools.

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.

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.

An MCP-native workspace where AI pushes discoveries into a live GPU-rendered graph, preserving project structure and findings across sessions.

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

A shared notice board for the coding agents working in one project. Claude Code, Codex, Gemini CLI, opencode and other MCP clients read and write the same board, so one agent's decisions, findings and todos reach the others.

Enables AI agents to remember, recall, structurally diff, and inspect accumulated reverse-engineering findings as a persistent knowledge graph, so analysis cost compounds instead of evaporating.

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 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 building and querying a Markdown-based knowledge graph with hybrid vector, keyword, and graph retrieval, community detection, and evidence-grounded answers through isolated workspaces.

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

Enables a small local LLM to build and maintain a project map by offloading event ID/date generation, batching, synchronization, recovery, and finalization to the server, so the model only decides what to read and how to classify.

Gives AI agents a shared persistent memory by ingesting Obsidian vaults, code, transcripts, web clips, and session or bug-fix captures, then serving token-optimized context briefings, keyword and semantic search, and cross-source synthesis. Enables multiple agents to read and write the same growing knowledge base so each session builds on the last.

Enables coding agents to search and read curated project memory before exploring code, and to record durable findings as append-only events for later Librarian auditing.