"A server for finding information about Dooray (project management and collaboration tool)" matching MCP servers:

A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

A self-hosted remote MCP server for cross-model AI session handoff, allowing users to start a project in one AI assistant and continue in another via shared project logs, files with version history, and full-text search, all stored in a single SQLite file on their own server.

An intelligent personal CRM that processes WhatsApp conversations to build a searchable knowledge base about contacts using diarization, transcription, and PII sanitization. It exposes MCP tools for semantic search, contact summaries, and reminder management within Claude Desktop.

Project Memory stores decisions, evidence, outcomes and reviewed lessons in local SQLite. Assistants retrieve bounded context through three MCP tools. People follow and plan work in a live workspace with an overview, sprint board, decision history, readable documents, skills and project maps. Offline exports remain available.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

An MCP server that transforms text into knowledge graphs and autonomously generates insights by combining Montague Grammar with Zettelkasten methodology.

Local workspace memory for Claude Desktop. Indexes documents into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph.

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.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.

A focused project memory system that provides knowledge graph capabilities and SQLite database operations for intelligent entity tracking and workflow management. It enables AI models to maintain persistent project context through 17 specialized tools, 10 resources, and 27 pre-built prompts.

Provides a file-based, git-friendly memory store for coding agents to persist and recall project-specific lessons, bugs, and conventions via JSON files in the repository.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Provides AI agents with a governed, three-layer project memory (guide, code facts, and knowledge) through namespaced MCP tools for code search, context compilation, impact analysis, and proposal-driven documentation updates.

Stores manual project memories, enables semantic retrieval, and tracks per-memory confidence using PostgreSQL with pgvector and OpenAI-compatible embeddings.

Enables coding agents to persist notes, decisions, requirements, and learnings per project in SQLite and retrieve them across sessions via search and status tools.