"A server to manage API rate limiting based on requests per minute" matching MCP servers:

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables enterprise knowledge search through a chat interface, comparing traditional RAG with MCP-driven retrieval using hybrid BM25 and dense vector search, Ollama-powered answer generation, and question routing to domain-specific retrieval tools.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Unofficial Notion MCP server built on Notion's private API (token_v2 cookie). Gives LLM agents full read/write access to the entire workspace — no integration token and no per-page sharing.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

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.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

MCP server exposing eight esoteric calculation systems (Western/Vedic astrology, Human Design, Gene Keys, and more) through 14 tools, with deterministic profiles, partial success, and OAuth integration.

Enables managing Zotero reference libraries with full CRUD operations, collections, notes, annotations, and attachments via the Zotero API.

Built-in MCP server that gives AI agents direct access to an Obsidian vault for reading, writing, searching notes, and executing commands.

AI assistant integration with Logseq knowledge graph: 21 tools to read, write, query, and search notes, enabling seamless interaction with your notes.

MCP server that enables AI assistants to interact with the MemOS API, providing memory search, feedback management, and knowledge base operations via MCP-compliant tools.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

Enables AI agents to read, create, update and delete any file in a mounted folder of markdown notes, patch individual headings, blocks or frontmatter keys, search by text or JsonLogic queries over frontmatter, tags and links, and stream note changes as events. Runs as a standalone container requiring no Obsidian installation and binds only to private networks such as Tailscale or WireGuard.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.