MCP server providing automated, context-aware documentation and code convention insights for GitHub repos.
Key Features: Compact pre-edit repo context including conventions and gaps Change impact analysis linking code paths to docs and conventions Structured API endpoint context for touched code paths Persistent memory MCP server with knowledge graph, semantic search, cloud sync, and cross-session context management.
Key Features: Persistent storage with SQLite or cloud sync (S3, R2, D1) Hierarchical memory organization with sections and subsections Semantic search using TF-IDF, sentence-transformers, and OpenAI embeddings MCP server providing safe read/write access and comprehensive operations on Obsidian vault notes via MCP.
Key Features: Safe YAML frontmatter parsing and AST-aware updates File operations: read, write, patch, delete, move notes/files Partial reads: get note outlines and specific line ranges Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
AI memory layer — one shared, persistent memory across every AI tool you connect.
Persistent personal memory for AI assistants — save, search, and recall across every MCP client.
Local code intelligence engine indexing repos into a persistent knowledge graph with fast structural queries across 159 languages.
Key Features: Indexes 159 languages using tree-sitter and Hybrid LSP Supports 15 MCP tools including graph search, tracing, and impact analysis Runs as a single static native binary with zero dependencies Shared, persistent semantic memory for your AI: save, search by meaning, recall with sources.
MCP server providing structured, offline access to ZIM knowledge archives like Wikipedia with advanced search and navigation.
Key Features: Supports ZIM archives from Kiwix Library Advanced 8-tool schema plus Simple natural-language query mode Archive-type presets for optimized retrieval per source Shared long-term memory for AI agents: save and recall context as a searchable knowledge graph.
Local-first MCP server that compresses and audits AI coding context to reduce cost and verify responses.
Key Features: Recoverable compression using BM25, entropy, and dependency graph knapsack Stable prompt prefixing for provider cache discounts Bayesian routing of tasks to cheaper models Run structured, persona-driven AI sessions for interview prep, coaching, reflection, and adaptive conversation flow.
Key Features: Persona-driven session management with detailed profiles Built-in timer controls: start, stop, and status checks Predefined and customizable session frameworks