"How to implement Active Directory authentication in a project" matching MCP servers:

This read-only MCP Server allows you to connect to Active Directory data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

This read-only MCP Server allows you to connect to Azure Active Directory data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

An MCP (Multi-Agent Conversation Protocol) server that enables interaction with Google's Managed Service for Microsoft Active Directory through its OpenAPI, allowing users to manage identity resources through natural language.

Provides VS Code active editor context (open file, cursor position, selection) to MCP-compatible AI tools.

Search, inspect and compare infrastructure tools for AI agents from a checked directory.

Project Memory stores decisions, evidence, outcomes and reviewed lessons in local SQLite. Assistants retrieve bounded context through three MCP tools. People follow and plan work in a live workspace with an overview, sprint board, decision history, readable documents, skills and project maps. Offline exports remain available.

Intent-based project documentation server that automatically maps natural language queries to the right sources (plans, todos, roadmap, docs) and provides comprehensive task management with dependency tracking, backlog promotion, and archival workflows.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

A FastMCP server that exposes predefined artifacts as Resources and project lifecycle operations as Tools for creating, updating, deploying, debugging, testing, monitoring, and configuring projects with minimal token usage.

Provides access to the APIs.guru directory of 3,000+ OpenAPI specs and supports custom spec import, enabling API discovery, analysis, and integration via natural language.

Provides a minimal MCP server with safe tools for AI assistants to search project files, read files, search local documentation, and run pre-approved checks within a single project directory.

Deep directory tree visualization tool enabling efficient visualization of complex directory structures with AI-powered analysis capabilities.

Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.

Enables coding agents in different editors and MCP clients to share messages, project context, notes, task ownership, handoffs, and advisory file claims through a local SQLite-backed coordination server. Supports MCP stdio, MCP Streamable HTTP, a JSON HTTP API, and a local CLI for multi-project agent collaboration.

Enables AI agents to query a repository's verified semantic model, including capabilities, ownership, contracts, constraints, impact, health, and verification status, without re-deriving project facts. It exposes read-only tools for inspecting, reasoning about, and verifying the provenance-tracked surface.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

A local, provider-neutral MCP server for repository-scoped issue handling. It provides a guarded interface to Linear, GitHub Issues, GitHub Projects v2, and Jira Cloud, with preview/apply safety and host-local configuration.

Enables coding agents to publish hierarchical project plans and task status changes to a mobile app in real time, letting developers monitor progress and blockers without checking their computer.

A production-ready MCP server that enables AI assistants to manage enterprise Active Directory environments through 45 specialized tools for users, groups, and GPOs. Its multi-client architecture allows for the secure management of multiple AD domains from a single codebase using natural language commands.

Enables any MCP client to drive the user's real Chrome profile through a Chrome extension bridge, with tools for navigation, accessibility snapshots, ref-based interactions, console/network reading, screenshots, tab management, and restricted script evaluation.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe a fine-tuning workload and receive ranked GPU cloud recommendations based on required VRAM/FLOPs, cost, and runtime across multiple providers.

Enables users to describe an LLM fine-tuning workload once and receive ranked cloud GPU recommendations with estimated VRAM/FLOPs requirements, cost, runtime, and data-source transparency.

Enables users to describe an LLM fine-tuning workload and receive VRAM/FLOPs estimates plus ranked GPU recommendations across multiple cloud providers, optimizing for cost, speed, or balance.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs calculations with cheapest, fastest, and balanced GPU recommendations across a dozen cloud providers. It supports CLI, REST API, and natural-language MCP integration without manual price comparison.

Enables users to describe an LLM fine-tuning workload and automatically estimate VRAM/FLOPs to recommend the cheapest, fastest, and balanced GPU options across multiple cloud providers. It supports natural-language requests through MCP, returning structured ranked hardware recommendations without manual price comparison.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables coding agents to persist notes, decisions, requirements, and learnings per project in SQLite and retrieve them across sessions via search and status tools.

Enables MCP clients to turn Make blueprints and Zapier exports into n8n workflow files, with a per-step report of what converted, what to review, and what must be finished by hand. Also lists zaps, explains how individual steps are handled, checks workflow files for broken links or placeholders, and can upload the result to an n8n instance as an inactive workflow.

MCP server for structured Laravel project planning. Scans projects, creates tickets, and exports as JSON.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Local MCP server giving AI coding agents (Claude Code, Cursor, VS Code/JetBrains Copilot) a shared, persistent memory of your projects and every bug/issue faced during development. Stateless, plain-file storage (AGENTS.md + issues.jsonl) — no database.

A cross-project knowledge graph for MCP that maps features across mobile, backend, and admin codebases, giving AI agents full-stack context.

MCP server for AI agents to send notifications, copy to clipboard, request confirmations, and collect text input from users across their devices.