Overview

Azure AI combines AI Search, Speech, OpenAI, and Document Intelligence to enable full-text, vector, and hybrid search, as well as speech-to-text, text-to-speech, transcription, and OCR. This skill supports AI Search, semantic search, and document intelligence workflows within Azure, with MCP-enabled management for streamlined operations.

How This Skill Works

When MCP is enabled, you manage AI services via commands like search_index_list, search_index_get, and search_query for AI Search; speech_transcribe and speech_synthesize for Speech; and utilize Document Intelligence for form extraction and OCR. SDK references provide programmatic access to these services (AI Search, OpenAI, Vision, Document Intelligence). This orchestration enables integrated search, transcription, and document processing in Azure-based apps.

When to Use It

• You need full-text, vector, or hybrid search over enterprise documents or content using AI-powered relevance.
• You require real-time or batch speech-to-text transcription from audio or video sources.
• You want natural-sounding text-to-speech output with neural voices for accessibility or experiences.
• You need OCR and form/data extraction from documents using intelligent enrichment.
• You plan to incorporate OpenAI models or embeddings within Azure for generation or embedding-based search.

Quick Start

1. Step 1: Enable MCP and ensure azure-ai services are available (Run /azure:setup or enable via /mcp).
2. Step 2: List and query AI Search indexes (azure__search: search_index_list, search_index_get, search_query).
3. Step 3: Use Speech tasks (azure__speech: speech_transcribe, speech_synthesize) or Document Intelligence for forms/OCR.

Best Practices

• Prefer hybrid search to balance keyword accuracy and semantic meaning for large datasets.
• Fine-tune domain vocabulary and custom analyzers to improve search relevance.
• Combine Document Intelligence OCR with enrichment to extract structured data from forms.
• Use MCP commands (search_index_list, search_query, speech_transcribe, speech_synthesize) for efficient operations and monitoring.
• Secure access, implement proper role-based access control, and monitor costs across AI services.

Example Use Cases

• Build an enterprise search across PDFs, Word docs, and intranet pages using AI Search with embeddings.
• Transcribe customer support calls and make transcripts searchable with Speech and AI Search.
• Deliver accessible content by converting product guides and trainings to high-quality speech using Text-to-Speech.
• Automate data extraction from invoices and forms with Document Intelligence OCR and enrichment.
• Create AI-powered assistants by combining OpenAI models with Azure AI Search for contextual answers.

Frequently Asked Questions