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- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to maintain persistent, cross-session memory of codebase architecture, naming conventions, and decisions through MCP tools. Eliminates repetitive project re-explanation by automatically injecting stored context into every session with local-first SQLite storage and optional team sharing capabilities.4MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents and assistants to share a local-first, token-efficient persistent memory layer across MCP-compatible runtimes, storing typed project facts, decisions, failures, and solutions with temporal validity. It supports offline use and Postgres-compatible storage, so agents can retrieve relevant context without re-embedding entire repositories.Apache 2.0
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to share a persistent local-first memory hub, storing and recalling architectural decisions and context across different tools via MCP, so users can switch assistants without losing context.Apache 2.0
- AlicenseAqualityDmaintenanceMCP server that provides a shared semantic memory layer for AI coding agents, enabling teams to store, search, and sync context, decisions, and knowledge across projects with project-based isolation and multi-backend support.141MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that provides persistent, cross-session memory and team knowledge sharing for AI development workflows. It enables project DNA scanning, semantic search, context budgeting, and git-aware indexing to prevent AI context loss between sessions.9 npmMIT
- AlicenseAqualityDmaintenanceA 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.865 PyPIMIT
TDQS
Scored across 24 tools
Each tool has a clearly distinct purpose, reinforced by explicit 'Do NOT use...' guidance that steers agents to the correct tool for each operation. Categories (artifact, code, task, session, project, build, history) are well-separated, and even similar tools like semantic_search vs search_project_artifacts are differentiated by semantic vs full-text search.
Tool names consistently follow a verb_noun pattern (e.g., list_project_artifacts, save_project_artifacts, get_task_details, run_project_build). The few exceptions like semantic_search still fit the pattern as a search operation, and there is no mixing of naming conventions.
With 24 tools, the count is on the heavier side but justified by the server's broad scope covering artifacts, code, tasks, sessions, projects, builds, and history. Each tool earns its place; the count feels comprehensive rather than bloated.
The tool surface provides full lifecycle coverage for its domain: artifact CRUD plus move/delete/restore/history, code search and reading, task management from creation to completion, session context retrieval, project initialization and listing, and build execution. No obvious gaps that would cause agent failures.