Provides repository-aware documentation context, convention checks, API context, and proposed documentation updates through MCP.
Key Features: Repository-aware AI context Convention discovery with citations Documentation impact analysis Connects Scrivener 3 projects to AI clients for editing, analysis, search, and writing support.
Key Features: Scrivener 3 document access and editing Deterministic writing analysis Keyword and offline semantic search Offline persistent memory for AI coding agents — 37 tools, 80% fewer calls, no LLM
Indexes repositories into a local code graph for structural search, call tracing, impact analysis, and architecture exploration.
Key Features: Persistent local code knowledge graph Tree-sitter parsing for 162 languages Hybrid LSP semantic resolution Local MCP memory for coding assistants with SQLite storage, structured knowledge, hybrid search, and no cloud dependency.
Key Features: Local SQLite persistent memory BM25 and semantic hybrid search Namespaces and typed knowledge graph Fetches current, version-aware library documentation and code examples for AI coding agents.
Key Features: Documentation and code example retrieval Version-aware documentation queries Local MCP memory server with encrypted three-tier storage and cue-based recall for AI coding assistants.
Key Features: Three-tier memory storage: episodic, semantic, procedural AES-256-GCM encryption at rest Multiple recall methods including cue, temporal, structural, and contradiction The private, owned conversation-memory layer for AI. Record, transcribe, and search every meeting.
Butterbase MCP server — manage your backend: schemas, auth, functions, storage, RAG, deploys.
Local memory layer indexing coding agent session histories for fast recall and cross-machine sync over SSH.
Key Features: Zero-dependency single binary, fully local operation Search 3.5 GB of session history in ~1.5 ms Supports multiple coding agents via MCP protocol Connects AI assistants to Tencent Lexiang for knowledge search, document editing, file handling, and knowledge-base management.
Key Features: Keyword and semantic vector search Entry and folder management Block-level document editing Agent-agnostic memory backend that preserves continuity between humans and AI over time.