"An official MCP server for Jira" matching MCP servers:

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

Free Industry 4.0 / IIoT tools, no API key or account: lint MQTT Sparkplug B topics, check a Unified Namespace (UNS) for naming and hierarchy problems, and run an industrial AI-readiness diagnostic for manufacturing digital transformation and smart-factory projects. Works in Claude Code, Codex and any MCP client.

Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

Automatically provides AI agents with proven instructions and past failure warnings for common tasks like deployment, auth, and payments, enabling flawless execution without manual configuration.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Extends large language model context length up to 60 times using gravitational memory, designed for Claude Code and Anthropic AI models.

Gives agents a stable interface for durable memory, combining exact, lexical, semantic, and graph search results.

Provides persistent session memory for AI assistants, enabling them to store, search, and retrieve conversation summaries across sessions via the Model Context Protocol.

Extends LLM context windows by 15-60× using gravitational memory compression, enabling massive context extension for codebases, research papers, and long documents.

A personal digital twin MCP server that manages professional context, including skills, experience, and project learnings. It allows AI assistants to retrieve and update user preferences and career interests across different development environments.

Provides a shared, persistent memory layer for AI assistants, letting them read and write to a local Obsidian vault through MCP with validation, secret rejection, and deduplication.

Local MCP server that automates an open Obsidian vault through Obsidian's official CLI, offering tools for note creation/update, search, tasks, daily notes, and organization. It uses MCP over STDIO, requires no cloud services, and provides safety/audit features for autonomous writes.

An MCP server for Claude Code that enables semantic search of past session transcripts and intelligent session forking, allowing users to find and resume from relevant previous conversations with full context.

An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.

Interacts with LogSeq via its API.

Turn any data source into an MCP server in 5 minutes. Build knowledge bases that AI assistants like Claude and Cursor can query directly.

Wraps the official Obsidian CLI to expose vault tools for MCP clients, enabling note management, search, tasks, properties, and more through Obsidian's internal API.

Provides local-first, zero-LLM memory retrieval for AI agents like Claude Code and Codex, enabling verbatim recall of stored notes and context through tools such as fidelis_recall and fidelis_orient.

An MCP server that exposes unified tools for HTTP requests, web search and fetching, weather/time/currency, file and SQLite sandboxes, GitHub, email, calendars, and a self-learning SQLite FTS5 knowledge base. It supports stdio, SSE, and streamable-HTTP transports with Bearer-token auth and SSRF protection so AstrBot agents can call these capabilities directly.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

MCP server for managing AI prompts with CRUD operations, categorization, and tagging. Enables users to store, organize, and retrieve their favorite prompts efficiently.

An intelligent, agentic MCP server gateway to OpenRouter's 200+ AI models. It provides budget controls, security features, semantic memory, and code search capabilities.

A productivity coaching assistant designed for ADHD support that provides intelligent task management, goal tracking, and personalized recommendations. It utilizes a persistent memory system to learn user patterns and preferences, helping to reduce decision paralysis through actionable suggestions.

An MCP server for storing and retrieving memories using Qdrant vector search, acting as a semantic memory layer.