"Information on Air Quality Index (AQI)" matching MCP servers:

Audits your Claude Code memory store with 4-dimension quality scoring and actionable cleanup recommendations, including a visual dashboard.

Governed knowledge base for AI agents via the Model Context Protocol (MCP), enabling agents to search, read, and contribute persisted knowledge with versioning, audit trails, and approval workflows.

A lightweight MCP server that maps keywords to file paths instantly using a JSON index, enabling fast context retrieval for AI agents without embeddings or vector databases.

Enables indexing and retrieving notes with full-text search using SQLite, plus building knowledge graphs to find relationships between concepts. Supports natural language note management, tagging, and semantic connections.

Enables voice captures from a smart ring to search and append notes in a local Obsidian vault and query an OpenAI-compatible AI agent, returning answers as phone notifications.

AI memory that works like yours. Neural pathway architecture gives your AI persistent recall, contextual awareness, and cross-session continuity the way human memory actually works. 14 MCP tools. Works with Claude Code, Cursor, Windsurf, and any MCP client. $50/mo unlimited.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

docshelf-mcp manages AI-friendly document collections — converts PDFs and Markdown into chapter-split shelves with a single navigation INDEX, so AI agents can fetch only the relevant section by raw URL instead of choking on a 4 MB datasheet. Repo: https://github.com/ignatenkofi/docshelf-mcp

Exposes a Neo4j knowledge-graph index over personal markdown notes to LLM agents via MCP, enabling semantic search, tag navigation, and relationship discovery.

Search past OpenCode conversation history before starting new work on a module or file, via a local read-only FTS5 index built from OpenCode's own SQLite database. No network calls, fully local. 7 tools for keyword search, file lookup, and session browsing.

A memory system for AI agents that maintains a human-readable, typed Markdown knowledge base with an always-in-context index, enabling recall, correction, and trust decay through MCP.

Zero-trust, air-gapped Enterprise GraphRAG MCP server. Build knowledge graphs from local documents and run multi-hop, citation-grounded queries entirely offline with Ollama.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

Provides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.

A system for defining, composing, and managing task-specific contexts for AI-powered development, enabling consistent, high-quality results.

Local index and hybrid search (SQLite FTS5 + on-device vector KNN) over your AI coding-agent conversation history across 11 tools (Claude Code, Codex, Cursor, and more). Exposes search_threads, search_current_project, recent_threads, get_thread, list_tags, and list_open_todos so any agent can recall its own past work.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

An MCP server that provides AI assistants with persistent, semantic memory using Turso for storage and OpenAI for vector search. It enables natural language operations to store, retrieve, and refine information with automatic duplicate detection and quality validation.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.