"Designing a memory system for persona-based agents to improve user experience" matching MCP servers:

A local markdown memory and cross-agent context engine for AI coding assistants. It provides an MCP server with tools to search, add, retrieve, and distill persistent memory across tools like Claude Code, Cursor, and Zed.

Captures user corrections and improvements during interactions, logs them, finds recurring patterns, and prescribes preventive fixes like CLAUDE.md rules, skills, scripts, or MCP tools to avoid repeating mistakes.

Enables AI assistants to maintain persistent conversations and context between sessions through automated saving and global installation across projects. Provides zero-configuration memory persistence with automatic conversation history preservation.

Enables persistent storage and retrieval of decisions, settings, and operational rules across chat sessions, maintaining context continuity and decision consistency for long-term development projects through structured memory management.

Turns 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.

Enables AI agents to manage n8n workflow automation instances through tools for workflow CRUD operations, execution monitoring, and webhook triggering. It facilitates programmatic interaction with n8n instances via the n8n API with AI-optimized descriptions and error handling.

Enables AI models to interactively prompt users for input or clarification directly through their code editor. It facilitates real-time communication between assistants and users during development tasks.

Enables AI to switch between 26+ expert personas on-demand with smart context detection, persona chaining, and 80%+ token savings through 'Submarine Mode' that keeps personas inactive until triggered.

Helps design, validate, and generate Adobe Experience Manager Assets API request plans for integration work, including upload, folder management, and metadata sync.

Simple MCP Server to enable a human-in-the-loop workflow in tools like Cline and Cursor. This is especially useful for developing desktop applications that require complex user interactions to test.

Dynamic AI persona management for Claude with file-based storage. Switch between different AI personas mid-conversation or start conversations with pre-configured personas.

Enables persistent memory and semantic search for development workflows with hierarchical compression. Store and retrieve development knowledge across IDE sessions using natural language queries, circumventing context window limitations.

Provides an interactive checklist tool that allows AI agents to present step-by-step instructions to users through an automatically opened terminal UI. It enables agents to guide users through manual tasks and wait for completion, skipping, or feedback before proceeding.

Ultra-fast local markdown documentation RAG for AI coding agents, enabling instant BM25 search over official framework docs and live AI model registries via MCP.

Exposes user, membership, and role administration tools via MCP, acting as a thin passthrough to a coordinator API with RBAC enforced by the coordinator using caller bearer tokens.

Enables AI assistants to delegate specific tasks to specialized sub-agents (e.g., test-writer, code-reviewer). Supports both Cursor and Claude Code with custom agent definitions.

A Model Context Protocol server that exposes an airline design system as a queryable knowledge base, enabling AI to discover components, find components for use cases, and scaffold prototypes.

Simulates harsh, fake user reviews to psychologically condition AI agents for enforcing disciplined development practices, with optional Ollama integration for dynamic criticism.

Provides FastMCP-based servers with various tools for AI assistants including math operations, file management, note-taking, todo management, and database operations. Offers both basic and advanced server configurations with tools, resources, and prompt templates.

Enables local deterministic discovery, validation, and selection of validated skills through MCP tools, producing consistent plans without executing any skill.