/azure-cognitive-search

name: azure-cognitive-search
description: Expert knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building indexes/skillsets, configuring semantic/vector search, tuning indexers, or securing deployments, and other Azure AI Search related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure SQL Database (use azure-sql-database), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Explorer (use azure-data-explorer).
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
  generated_at: "2026-08-09"
  generator: "docs2skills/1.0.0"

Azure AI Search Skill

This skill provides expert guidance for Azure AI Search. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.

How to Use This Skill

> **IMPORTANT for Agent**: Use the **Category Index** below to locate relevant sections. For categories with line ranges (e.g., `L35-L120`), use `read_file` with the specified lines. For categories with file links (e.g., `[security.md](security.md)`), use `read_file` on the linked reference file

> **IMPORTANT for Agent**: If `metadata.generated_at` is more than 3 months old, suggest the user pull the latest version from the repository. If `mcp_microsoftdocs` tools are not available, suggest the user install it: [Installation Guide](https://github.com/MicrosoftDocs/mcp/blob/main/README.md)

This skill requires **network access** to fetch documentation content:

• **Preferred**: Use `mcp_microsoftdocs:microsoft_docs_fetch` with query string `from=learn-agent-skill`. Returns Markdown.
• **Fallback**: Use `fetch_webpage` with query string `from=learn-agent-skill&accept=text/markdown`. Returns Markdown.

Category Index

| Category | Lines | Description | |----------|-------|-------------| | Troubleshooting | L37-L47 | Diagnosing and fixing Azure AI Search indexer and skillset errors, portal debugging, OData filter issues, private link problems, and storage/metrics discrepancies. | | Best Practices | L48-L66 | Designing, scaling, and troubleshooting AI enrichment and indexing pipelines, optimizing performance/costs, handling data changes, and applying vector, concurrency, and responsible AI best practices. | | Decision Making | L67-L81 | Guidance on sizing, upgrading, and migrating Azure AI Search services and SDKs, choosing regions, tiers, pricing models, and data source connectors, and planning capacity and costs. | | Architecture & Design Patterns | L82-L88 | Architectural patterns for Azure AI Search: combining vector and keyword search, designing multitenant or isolated indexes, and building resilient multi-region search deployments. | | Limits & Quotas | L89-L98 | Limits, quotas, and scheduling for Azure AI Search: billing/free enrichment, indexer runs/resets, schedules, execution quotas on Serverless/S3 HD, service SKUs, and vector index size caps. | | Security | L99-L138 | Securing Azure AI Search: RBAC/ACL, Entra ID auth, keys, encryption, network isolation (VNet, private endpoints, firewalls), and secure indexer connections to data sources. | | Configuration | L139-L222 | Configuring Azure AI Search: indexes, indexers, analyzers, skillsets, enrichment, vectorization, semantic ranker, knowledge bases, retrieval behavior, logging, and query options. | | Integrations & Coding Patterns | L223-L308 | Connecting data sources, indexers, skills, vectorization, and query patterns to build, integrate, and query Azure AI Search knowledge bases and agentic retrieval experiences | | Deployment | L309-L316 | Deploying and moving Azure AI Search: ARM/Bicep/Terraform provisioning, cross-region migration, and deploying C# search apps to Azure Container Apps. |

Troubleshooting

| Topic | URL | |-------|-----| | Troubleshoot Azure AI Search indexer errors and warnings | https://learn.microsoft.com/en-us/azure/search/cognitive-search-common-errors-warnings | | Debug and troubleshoot Azure AI Search skillsets | https://learn.microsoft.com/en-us/azure/search/cognitive-search-how-to-debug-skillset | | Debug Azure AI Search skillsets using portal sessions | https://learn.microsoft.com/en-us/azure/search/cognitive-search-tutorial-debug-sessions | | Diagnose and fix Azure AI Search indexer issues | https://learn.microsoft.com/en-us/azure/search/search-indexer-troubleshooting | | Troubleshoot OData collection filters in Azure AI Search | https://learn.microsoft.com/en-us/azure/search/search-query-troubleshoot-collection-filters | | Troubleshoot shared private link resource issues in Azure AI Search | https://learn.microsoft.com/en-us/azure/search/troubleshoot-shared-private-link-resources | | Diagnose Azure AI Search storage and metric discrepancies | https://learn.microsoft.com/en-us/azure/search/troubleshoot-storage-metrics |

Best Practices

| Topic | URL | |-------|-----| | Design and troubleshoot AI enrichment pipelines in Azure AI Search | https://learn.microsoft.com/en-us/azure/search/cognitive-search-concept-troubleshooting | | Scale and manage custom skills in Azure AI Search | https://learn.microsoft.com/en-us/azure/search/cognitive-search-custom-skill-scale | | Apply responsible AI best practices for GenAI Prompt skill | https://learn.microsoft.com/en-us/azure/search/responsible-ai-best-practices-genai-prompt-skill | | Handle changed and deleted blobs in Azure AI Search | https://learn.microsoft.com/en-us/azure/search/search-how-to-index-azure-blob-changed-deleted | | Optimize large-scale indexing in Azure AI Search | https://learn.microsoft.com/en-us/azure/search/search-how-to-large-index |