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Azure AI

MicrosoftOfficialCreated by Microsoft

  • Skill
  • Tool integration
  • Cloud and DevOps
  • MIT
  • Low risk
  • Has scripts
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From the author

Build and troubleshoot Azure AI Search, Speech, OpenAI, and Document Intelligence apps. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, Speech SDK logs, session ID, result ID, OCR, convert text to speech.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a centralized reference for integrating and debugging Microsoft Azure AI services. It covers AI Search, Speech, OpenAI, and Document Intelligence, offering guidance on MCP tool usage, CLI commands, and SDK patterns. It includes specific troubleshooting resources for Speech SDK diagnostics and best practices for secure authentication across development and production environments.

What it does

  • Provides MCP tool mappings for Azure AI Search and Speech services.
  • Offers quick-reference guides for various Azure AI SDKs across multiple languages.
  • Outlines best practices for secure authentication using managed identities.
  • Includes troubleshooting patterns for Speech SDK logs and diagnostics.
  • Summarizes capabilities for vector, hybrid, and semantic search.
  • Details patterns for document intelligence and content safety implementations.

When to use it

  • When integrating Azure AI Search, Speech, or OpenAI into an application.
  • When troubleshooting Speech SDK connectivity or transcription issues.
  • When setting up authentication for Azure services in production versus local environments.
  • When looking for language-specific SDK patterns for Azure AI services.
  • When needing to perform OCR or form extraction via Document Intelligence.

Advantages

  • Centralizes documentation for multiple Azure AI services.
  • Provides clear guidance on moving from local development to production security.
  • Includes actionable MCP tool commands for rapid integration.
  • Offers language-specific SDK snippets for quick implementation.
  • Simplifies complex troubleshooting tasks like Speech SDK log analysis.

Usage examples

“List the available search indexes in my Azure AI Search service.”
The agent uses the azure__search MCP tool with the search_index_list command to retrieve and display the indexes.
“How should I authenticate my Azure AI client in a production environment?”
The agent provides the recommended pattern using ManagedIdentityCredential instead of DefaultAzureCredential to ensure secure, deterministic authentication.
“Transcribe this audio file using Azure Speech.”
The agent utilizes the azure__speech tool with the speech_transcribe command to process the audio file.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/azure-ai/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-ai.zip -d .agents/skills

Documentation by Microsoft

Azure AI Services

Services

ServiceUse WhenMCP ToolsCLI
AI SearchFull-text, vector, hybrid searchazure__searchaz search
SpeechSpeech-to-text, text-to-speech, SDK logsazure__speech-
OpenAIGPT models, embeddings, DALL-E-az cognitiveservices
Document IntelligenceForm extraction, OCR--

MCP Server (Preferred)

When Azure MCP is enabled:

AI Search

  • azure__search with command search_index_list - List search indexes

  • azure__search with command search_index_get - Get index details

  • azure__search with command search_query - Query search index

Speech

  • azure__speech with command speech_transcribe - Speech to text

  • azure__speech with command speech_synthesize - Text to speech

If Azure MCP is not enabled: In Copilot CLI or Claude Code, ask the user to run /mcp and enable Azure MCP; in other hosts, ask the user to enable Azure MCP through the host's MCP configuration.

AI Search Capabilities

FeatureDescription
Full-text searchLinguistic analysis, stemming
Vector searchSemantic similarity with embeddings
Hybrid searchCombined keyword + vector
AI enrichmentEntity extraction, OCR, sentiment

Speech Capabilities

FeatureDescription
Speech-to-textReal-time and batch transcription
Text-to-speechNeural voices, SSML support
Speaker diarizationIdentify who spoke when
Custom modelsDomain-specific vocabulary
Client SDK diagnosticsSpeech SDK client diagnostics for log collection, offline analysis, features, and session/result IDs (no MCP or login required)

SDK Quick References

For programmatic access to these services, see the condensed SDK guides:

Service Details

For deep documentation on specific services:

Files

references/sdk/azure-ai-document-intelligence-ts.md

Azure Document Intelligence — TypeScript SDK Quick Reference

Condensed from azure-ai-document-intelligence-ts. Full patterns (custom models, classifiers, batch polling) in the azure-ai-document-intelligence-ts plugin skill if installed.

Install

npm install @azure-rest/ai-document-intelligence @azure/identity

Quick Start

Auth: DefaultAzureCredential is for local development. See auth-best-practices.md for production patterns.

import DocumentIntelligence, { isUnexpected, getLongRunningPoller, AnalyzeOperationOutput } from "@azure-rest/ai-document-intelligence";
const client = DocumentIntelligence(endpoint, new DefaultAzureCredential());

Non-Obvious Patterns

  • REST client — DocumentIntelligence is a function, not a class

  • Analyze path: client.path("/documentModels/{modelId}:analyze", "prebuilt-layout").post({...})

  • Must use getLongRunningPoller(client, initialResponse) then poller.pollUntilDone()

  • Local file: send as base64Source in body, not as binary stream

  • Pagination: import { paginate } from "@azure-rest/ai-document-intelligence"

Best Practices

  1. Use getLongRunningPoller() — document analysis is async, always poll

  2. Check isUnexpected() — type guard for proper error handling

  3. Choose the right model — prebuilt when possible, custom for specialized docs

  4. Handle confidence scores — set thresholds for your use case

  5. Use paginate() helper for listing models

  6. Prefer neural mode for custom models over template

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Node.js

Security

What our scanner noticed· Low risk

  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-skills/skills/azure-ai · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from .github/plugins/azure-skills/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Also distributed in
microsoft/azure-skills

AutoGineer is not affiliated with or endorsed by Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-ai.zip