"Non-Docker Implementation of Jira with MCP" matching MCP servers:

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

Provides Prolog logic programming integration for Claude via a Docker SWISH container, with persistent notebooks and cognitive architecture support.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.

AoT MCP server enables AI models to solve complex reasoning problems by decomposing them into independent, reusable atomic units of thought, featuring a powerful decomposition-contraction mechanism that allows for deep exploration of problem spaces while maintaining high confidence in conclusions.

Enables sophisticated reasoning workflows using graph-based representations for AI models.

A scientific reasoning framework that leverages graph structures and the Model Context Protocol (MCP) to process complex scientific queries through an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) approach.

Provenance tree for work: every node knows which node it was born from

Memory bank with Server as SSH support for central knowledge base

A read-only MCP server that lets AI agents search and retrieve the Wheel of Heaven corpus, including source-grounded facts, interpretations, and comparative traditions, all with full epistemic metadata.

Enables chat with multiple LLM providers (OpenAI and Anthropic) while maintaining persistent conversation memory. Provides extensible tool framework for various operations including echo functionality and conversation storage/retrieval.

MCP server for alive-analysis. Query, search, and retrieve structured analysis history written with the ALIVE loop (Ask→Look→Investigate→Voice→Evolve), plus dashboard JSON export.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Enhances user interaction through a persistent memory system that remembers information across chats and learns from past errors by utilizing a local knowledge graph and lesson management.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

Connects Bear Notes to AI assistants using semantic search and RAG (Retrieval-Augmented Generation), allowing AI systems to access and understand your personal knowledge base through meaningful search rather than just keyword matching.

Transforms Claude into a persistent intelligence layer with automatic checkpointing, crash recovery, semantic memory, and cross-project learning. It provides 128+ tools for session management, adversarial testing, and browser automation to eliminate state loss and context rebuild costs.

Provides persistent local memory of Claude Code sessions with TODO management and searchable activity logs via MCP tools.

A multi-agent AI tutor that delivers personalized lessons, resolves doubts with RAG, generates quizzes, and tracks progress, all accessible via MCP for Claude Desktop.

Enables context capture and reinforcement learning by recording successful work patterns and creating reasoning chains for cross-conversation continuity. Automatically captures positive feedback through Claude Code hooks to build reusable success patterns.