SystemDox MCP Server
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- AlicenseAqualityAmaintenanceEnables AI coding agents to keep durable project memory across sessions and retrieve token-budgeted context packs, so they can recall decisions, gotchas, and TODOs and locate relevant code without rereading entire repositories.8MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI coding agents with persistent architectural memory of codebases, enabling impact analysis, test generation, and code generation with reduced token usage.MIT
- AlicenseNot gradedqualityBmaintenanceGives AI coding agents on-demand retrieval of workspace context, documentation, lessons, decisions and handoffs from a five-level (GLOBAL → WORKSPACE → PROJECT → FEATURE → SESSION) store, so they start with a small rule set instead of re-reading bloated docs. Exposes tools for dependency routing, knowledge search, doc reading and health checks backed by a zero-dependency SQLite + FTS5 memory with cross-project sharing of global-scoped knowledge.MIT
- AlicenseAqualityBmaintenanceEnables teams to share structured, git-synced context among AI coding agents working on the same repository, including living plans, task declarations, handoff briefs, file-provenance history, and conflict detection.1220 npm3MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to retrieve precise, real-time architectural context—such as domains, models, routes, and events—from a codebase, avoiding the need to dump entire files into prompts.6 npm1MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI coding agents to share a persistent, local-first memory of architectural decisions, conventions, bug root causes, and session handoffs, searchable via full-text ranking, so context carries across tools and sessions without any cloud dependency.MIT
TDQS
Scored across 27 tools
Most tools have clearly distinct targets: checks vs rules vs specs vs docs vs graph vs skills vs memory. The main ambiguities are the aggregator `get_context` (which overlaps the individual `get_*` retrievers, though its description explains the ranking/token-budget role) and `get_plan_board` vs `get_planning_context`, plus the explicitly-deprecated `get_guardrails`/`create_guardrail` aliases that duplicate `get_checks`/`create_check`.
Almost all tools follow a snake_case verb_noun pattern (get_*, create_*, publish_*, list_*, install_*, sync_*). The only deviations are the legacy `guardrail` names kept for compatibility, which still fit the pattern but add a second name for the same concept.
27 tools is above the comfortable range, and two are deprecated duplicates that inflate the count. The surface is genuinely broad (context retrieval, graph, checks, specs, skills, setup, memory), so the volume is partly justified, but several retrieval tools could be consolidated.
Read coverage is strong (tech stack, checks, specs, docs, graph, boards, team setup, memory) and writes exist for checks, rules, prompts, decisions, and spec updates. However, there is no update/delete for checks, rules, or prompt templates, no create/delete for specs (only update of drafts), and no way to unpublish skills or setup parts, leaving notable lifecycle gaps.