"ZAP" matching MCP servers:

Enables a small local LLM to build and maintain a project map by offloading event ID/date generation, batching, synchronization, recovery, and finalization to the server, so the model only decides what to read and how to classify.

MCP server that provides a navigable map of an Obsidian vault for coding agents, with tools to outline notes, fetch sections, query frontmatter, and grep content, reducing token usage compared to reading whole notes.

Zep Cloud MCP Server: Provides memory management via the Zep Cloud API. Enables storing and retrieving user preferences, procedures, and factual relationships through a knowledge graph. Supports fallback mode for reliable operation when API is unreachable.

Generates structured project memory and exposes MCP tools for AI coding agents to query topology, read relevant files, and log changes in a repository.

An MCP server that reduces AI coding agent token usage by 80-99% through a queryable knowledge graph of code, change tracking, and persistent memory across sessions.

Enables Claude to store, search, and retrieve persistent memories using Zep Cloud's thread-based memory system for maintaining context across conversations.

MCP server for the Zep Cloud API — a context engineering platform for AI agent memory. Provides tools for memory management, knowledge graph operations, and admin features.

Enables creating and querying semantic knowledge graphs to model business logic, code relationships, and project structure across multiple projects.

Compresses context for AI agents by storing memories in compact markdown files with FTS5 search, enabling fast retrieval of relevant information while saving tokens.

An experimental MCP server that explores neuroscience-inspired approaches to software development analysis, featuring associative memory, context-aware caching, and cross-language API detection.

Enables managing personal vehicle history including mileage, maintenance, expenses, reminders, and document references through MCP tools and resources, all stored locally in SQLite.

Enables users to visualize and interact with Claude's chain-of-thought reasoning from extended thinking traces, turning them into interactive reasoning maps with typed moves, dependency graphs, timelines, and replay mode.

Enables AI agents to compress and selectively retrieve context, with measured recall rather than claimed performance. It provides tools to assess potential traffic and token savings, list compression dictionaries, and assemble relevant memory entries within a token budget.