List all AI systems in your workspace with registration status and evidence coverage to identify unregistered systems and gaps before the EU AI Act deadline.
MIT
636,132 tools. Updated 2026-10-04 02:24
"Memory and Database Solutions for AI Systems" matching MCP tools:
- Retrieve database engine, name, memory, address, and port by providing the database ID.MIT
- Get AI-synthesized fix suggestions for current errors by retrieving relevant past solutions from the archive, providing contextual recommendations.MIT
- Checks the health and scale of your AI memory database by showing total memories, categories, and embedding status.Apache 2.0
- Extract decisions, patterns, debugging solutions, and architecture insights from recent Claude Code session logs. Summarize transcripts and store results in memory for daily reflection.MIT
- Configures a 3-layer AI memory system for any project. Automatically generates copilot-instructions.md so AI recalls known solutions before tasks and saves lessons after each attempt.Apache 2.0
Matching MCP Servers
- AlicenseNot gradedqualityAmaintenanceTurns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.MIT
- AlicenseBqualityDmaintenanceMCP server that allows Claude AI to interact directly with MySQL databases, enabling query execution and table information retrieval through natural language.13 npm4MIT
Matching MCP Connectors
BOLD Systems (Barcode of Life Data System, University of Guelph) — the global DNA barcode…
- Kagura Memory CloudOAuth
Persistent memory for AI assistants: store, search, and connect knowledge across conversations.
- Find lab systems by hardware attributes and ownership. Filter by CPU, memory, architecture, hostname, owner, or pool to identify matching machines.MIT
- List database agent hosts to view their URL and memory/disk budget, identifying machines that run the Calagopus database agent for provisioning containerised database instances.MIT
- Retrieve the canonical Self-Dialectical AI Systems methodology with HUMMBL Base120 mappings for structured problem-solving.Apache 2.0
- Provides operational guidance for AI systems during cloud connectivity failures, ensuring continued local inference using offline-capable models.MIT
- Evaluates a webpage's structure and support for passages that AI systems can extract as citations, indicating readiness for AI-generated citations.MIT
- Record session learnings, discovered solutions, and bugs to a persistent, searchable memory. Retain insights and project context for future retrieval across AI agent sessions.MIT
- Find why a code file exists by searching team memory for linked decisions, solutions, and gotchas, returning ranked answers.MIT
- Store AI session summaries in persistent memory to retain key decisions, patterns, and solutions learned during coding sessions.MIT
- Assess HIPAA compliance of AI systems processing PHI. Evaluates safeguards across administrative, physical, and technical domains for healthcare AI.MIT
- Check database health and record counts to ensure reliable cross-session memory.MIT
- Assess EU AI Act compliance, prohibited uses, and bias risks for biometric AI systems, including facial recognition, emotion detection, and behavioral biometrics.MIT
- Retrieve recent and upcoming Australian AI regulatory changes from APRA, OAIC, ASIC, TGA, and Privacy Act reform to meet compliance obligations when building AI systems.MIT
- Evaluate workplace monitoring systems for compliance with EU AI Act, employee rights, proportionality, and the Platform Workers Directive.MIT
- Create a structured EU AI Act inventory document for a project, listing all detected AI systems with risk classifications and compliance documentation requirements.MIT
- Assess regulatory compliance for AI-based hiring systems against NYC Local Law 144, EEOC, and EU AI Act, covering bias auditing and candidate rights.MIT