"Understanding and Using JSON" matching MCP servers:

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Connects to SlimSnap to provide recent screen captures as structured JSON data (text and annotations) for AI to read, avoiding screenshot limitations.

MCP server for parsing, searching, and extracting branches from XMind mind map files, supporting Markdown/JSON output, token-aware formatting, and Claude Desktop integration.

Local-only MCP server for saving and restoring conversation context across AI agent sessions using JSON files on disk.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

A lightweight Notion MCP server that minimizes token usage by returning Markdown instead of raw JSON, enabling efficient read/write operations on Notion pages.

Provides brain-inspired long-term memory for MCP clients, enabling persistent user understanding across sessions with Chinese-optimized hybrid retrieval and evolving user profiles.

An MCP server for Capacities API 2.0 that enables AI agents to discover, create, read, update, and delete objects, blocks, and daily notes with structured JSON or Markdown, using personal API tokens.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

Persistent memory server for MCP clients that stores structured facts as JSON across sessions, enabling atomic add, remove, replace, and view operations.

An MCP server that provides persistent memory for database schemas by storing table structures and metadata in a local SQLite file. It enables LLMs to save, retrieve, and manage database snapshots to maintain a structured understanding of database architectures.

A cross-platform MCP server providing persistent storage for AI assistants to store, retrieve, and manage memories across conversations. It features keyword and tag-based search capabilities using a local JSON file for data persistence.

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

SourceSage is an MCP (Model Context Protocol) server that efficiently memorizes key aspects of a codebase—logic, style, and standards—while allowing dynamic updates and fast retrieval. It's designed to be language-agnostic, leveraging the LLM's understanding of code across multiple languages.

A Model Context Protocol server that enables Claude to persistently store, search, and manage text memories with tags in a local JSON file.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

A Model Context Protocol server implementation that enables LLMs to interact with NebulaGraph database for graph exploration, supporting schema understanding, queries, and graph algorithms.

Enables AI tools to query context from a local JSON data source via stdio, demonstrating the Model Context Protocol.

Provides intelligent manuscript analysis and writing assistance for markdown projects, including semantic search, quality checks, terminology consistency, link validation, progress tracking, and comprehensive writing statistics.