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

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.

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

Enables MCP clients to browse a read-only catalog of published products and their documented interfaces, listing entries, fetching one by stable ID, and searching names, descriptions, and limitations with optional protocol filters. It also exposes the versioned manifest as a resource and a prompt that helps clients match a task to catalog entries without inventing unsupported capabilities.

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.

An MCP server that makes project documentation instantly accessible in Claude Code through @ mentions, allowing Claude to understand your codebase's conventions and architecture.

Enables converting a public release-note URL into ordered upgrade tasks with source excerpts, anchors, confidence, and uncertainty, without executing release text or project code.

A Model Context Protocol server that converts Markdown documents to plain text with flexible options and dual protocol support.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Enables AI assistants to search and retrieve listings from the OmniTravel travel industry directory of MCP servers, agent skills, and APIs, with typo-tolerant search, full details, and category browsing.

Enables multi-project workspaces to share structured notes, API contracts, and handoff messages via a local SQLite database, with versioning and read tracking.

Enables hybrid retrieval-augmented generation with semantic and keyword search over documents, supporting multiple formats and providing MCP integration for Claude Desktop.

Converts any OpenAPI/Swagger spec into an MCP server, exposing REST API endpoints as tools for Claude.

Enables an MCP client to retrieve explicitly allowlisted Markdown context, create and read local task contracts, write local handoffs, and return locally configured routing metadata without invoking a model, executing commands, or writing to the vault.

Provides read-only tools to search a dated catalog of MCP servers, retrieve public Index capabilities, and get static instructions for obtaining OpenAI, OpenRouter, Anthropic, and Gemini usage exports.

Enables AI agents to search, link, create, and update local Markdown architecture notes through MCP, with previews, write gates, revision checks, and source-hash drift detection.

Enables converting Markdown text into clean HTML for headings, lists, code blocks, tables, links, and images, with optional full-document wrapping via x402 micropayments.

Routes coding agents to the most relevant project documentation (decisions, intent, constraints) with provenance and freshness, providing tools for task routing, knowledge search, and document context.

A minimal MCP server that auto-generates tools from your OpenAPI spec, optimized for Vercel.

An MCP server that reads any codebase (GitHub repo or local folder) and explains it using AI. Enables users to ask questions about project structure, key files, auth flows, and more in plain language.

Provides project-aware context for coding agents by selecting relevant AGENTS.md, docs, module paths, and decision records based on task, reducing redundant context loading via MCP stdio.

Provides AI agents with a governed, three-layer project memory (guide, code facts, and knowledge) through namespaced MCP tools for code search, context compilation, impact analysis, and proposal-driven documentation updates.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables semantic search over a software project's Markdown and text documentation by indexing document chunks in Qdrant and exposing retrieval through MCP tools.

Enables local conversion of PDFs to Markdown, with automatic OCR fallback for scanned pages, image extraction, and page-by-page structure preservation.

Provides project-specific guides and instructions by selecting modules based on a project profile, enabling developers to get contextual instructions for their tech stack and architecture.

Fetches website content and converts it to Markdown format with AI-powered content cleanup, ad removal, and full OpenAPI/Swagger specification support for easy processing by AI assistants.

Converts Markdown files to Atlassian Document Format and publishes them to Confluence, with support for rendering Mermaid diagrams as PNG attachments.

Converts Markdown files and raw content into professionally styled PDFs with full support for Mermaid diagrams and syntax highlighting. It offers customizable page formats, margins, and modern typography for high-quality document generation.

Provides AI assistants with read-only access to live construction project data and document libraries, enabling issue investigation, schedule risk scoring, and evidence-backed project briefs.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

Provides AI assistants with access to OpenAPI specifications, enabling API discovery, schema retrieval, and direct API execution with support for OAuth 2.0 and other authentication methods.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

Exposes a small catalog of programming study topics through the Model Context Protocol (MCP) for local learning.

An educational repository designed to practice and understand the Model Context Protocol through simple server implementations. It demonstrates core MCP concepts such as tools, resources, and communication via stdio and SSE transport methods.

Enables AI assistants to analyze project folders for dependencies and frameworks and fetch current documentation or datasheets from the web.

A multi-server system that converts PDF documents to Markdown format using FastMCP architecture with upload and convert servers orchestrated by a reactive client agent.

Enables AI editors to collaboratively manage project documentation, including todos, decisions, pitfalls, and interfaces, through MCP protocol tools.

Serves a single resource (project_info.md) as text/markdown over MCP HTTP transport, for use by MCP-aware clients like Claude Desktop.