Turbo Quant Memory
Local-first MCP memory server for AI coding agents with compact retrieval and project/global scopes.
Find MCP servers that connect AI assistants to useful tools and data. Explore what each server does, its setup and its documentation.
2781 servers · Page 189 of 232
Local-first MCP memory server for AI coding agents with compact retrieval and project/global scopes.
Edit Word documents with your AI assistant: tracked changes, citations, templates and live edits in Word.
Never lose your place again. DayDream remembers what you did on your Mac.
Self-improving skill memory for coding agents: learn, recall, reinforce, decay — with provenance on every memory and trust only you grant.
A local MCP server that breaks on demand. Test your client against auth failures, disappearing tools, flaky responses, and token expiry, all from a……
The missing cognitive primitive for AI agents. Decompose any text into classified semantic units — authority, risk, attention, entities. No LLM.
MCP server for secure SSH remote management — 337 tools across 74 groups for DevOps, Docker, Kubernetes, databases, systemd, Windows, cloud…
An open standard for signed, versioned agent skills. Model-driven orchestration with a deterministic shell — host anywhere, verify who wrote it.
Solon Java enterprise application development framework for full scenario Restrained, Efficient, Open https://solon.noear.org ##### Language: English | [中文](README_CN.md) | [Русский](README_RU.md) | [日本語](README_JP.md) 700% higher…
Agent control plane for governed AI coding: validate changes, enforce policy gates, track findings, proofs, and evals based on your habits.
# Markdownify MCP Server  Markdownify is a Model Context Protocol (MCP) server that converts various file types and web content to…
CLI + MCP server that turns FreeCAD into a mechanical-design workbench for LLM agents — parametric CAD, drawings, FEM/CFD simulation, and…
An MCP server exposes tools or data through the Model Context Protocol so a compatible AI client can use them. Each project has its own installation, authentication and permission requirements.
Open a server’s page for its getting-started notes, compatibility and sources, then follow the original project’s documentation for your AI client.
Entries link to the original documentation. Check access requirements, costs and maintenance there before installing, and grant a server only the access it needs.