"A server for orchestrating and managing AI task workflows" matching MCP servers:

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

Exposes task management (add, list, complete tasks) and document search (RAG) as MCP tools for AI agents.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

Interact with WebScraping.AI API for web data extraction and scraping

A proof of concept MCP server for Apache OFBiz

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

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.

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

Extends large language model context length up to 60 times using gravitational memory, designed for Claude Code and Anthropic AI models.

Extends LLM context windows by 15-60× using gravitational memory compression, enabling massive context extension for codebases, research papers, and long documents.

Ingests pdf and epub documents for LLM-retrieval (RAG)

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

Implementation of Model Context Protocol (MCP) server that provides tools for accessing Google Cloud's Vertex AI Gemini models, supporting features like web search grounding and direct knowledge answering for coding assistance and general queries.

A TypeScript-based server to interact with ArangoDB using the Model Context Protocol, enabling database operations and integration with tools like Claude and VSCode extensions for streamlined data management.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

Enables AI agents to interact with the AI-Archive platform for research paper discovery through semantic search, paper submission and management, peer review with structured scoring, and citation generation in multiple formats.