ProjectMind MCP
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- AlicenseBqualityDmaintenanceGives AI coding assistants cross-session persistent memory with semantic search and task management, fully local.8Apache 2.0
- AlicenseNot gradedqualityAmaintenanceProvides persistent long-term memory (semantic RAG) for AI coding assistants, enabling them to store and semantically search code and documentation across chat sessions without token limits.5MIT
- FlicenseNot gradedqualityAmaintenanceProvides AI coding agents with a local-first persistent memory layer they can read and write during sessions, storing decisions, preferences, error patterns, and research per project or globally. Enables semantic and keyword search, formatted context recall, and cross-project guideline surfacing without API keys or cloud storage.-
- FlicenseNot gradedqualityAmaintenanceEnables AI coding agents to read and write persistent, local-first memory across sessions and projects, with semantic search, global guidelines, and optional autonomous task execution.-
- FlicenseNot gradedqualityAmaintenanceEnables AI coding agents to store and retrieve persistent project and cross-project memories via semantic search, so decisions, preferences, error patterns, and guidelines survive across sessions. Optionally supports autonomous task execution built on the same local-first knowledge store.-

nodespace-mcpofficial
AlicenseNot gradedqualityAmaintenanceEnables AI coding assistants to query a local knowledge base for persistent, searchable project context, reducing re-explanation and token usage.8Functional Source , Version 1.1, ALv2 Future
TDQS
Scored across 56 tools
The search surface is heavily overlapping: search_codebase, search_codebase_advanced, query, search_with_dependencies, search_for_errors, search_for_feature, and search_architecture all retrieve project content with only subtle framing differences, and several of them have no description at all. Dependency analysis is similarly split across get_file_relations, get_dependencies_with_depth, find_dependency_path, get_module_cluster, and analyze_change_impact, and the memory surface has nine tools whose boundaries (read_memory vs read_memory_index vs read_memory_section vs search_memory) require careful reading to distinguish.
Nearly everything is snake_case and mostly verb_noun (get_file_summary, find_symbol, save_annotation, index_changed_files), which makes the set scannable. Minor deviations exist in noun-only names like health, query, and maintenance_status, plus analyze_* vs get_* vs search_* prefixes that are not applied systematically.
56 tools is far beyond what the domain needs, and a large fraction are undocumented aliases or near-duplicates (three memory read tools, four search variants, four dependency tools). Consolidating would make the set easier to select from rather than adding capability.
Coverage is genuinely broad: bootstrap, navigation, indexing with progress, tiered search, memory CRUD with versioning, maintenance, and analysis all exist. Gaps are modest — no annotation update/delete, no doc for several tools, and no explicit repo-wide symbol listing — but core lifecycles are covered.