/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 configuring indexes, vector/semantic search, indexers & skillsets, RBAC/ACL security, or deployment automation, and other Azure AI Search related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Data Explorer (use azure-data-explorer), Azure SQL Database (use azure-sql-database), Azure Synapse Analytics (use azure-synapse-analytics).
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
  generated_at: "2026-11-08"
  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-L48 | Diagnosing and fixing Azure AI Search indexer/skillset errors, filter and metric issues, permission-filtered results, and private link or storage discrepancies. | | Best Practices | L49-L66 | Best practices for designing, scaling, and troubleshooting enrichment/indexing pipelines, optimizing vector search, performance, and costs, and safely updating Azure AI Search resources. | | Decision Making | L67-L82 | Guidance on choosing regions, tiers, pricing, capacity, and connectors, plus migration/upgrade paths for APIs and SDKs to plan, scale, and modernize Azure AI Search solutions. | | Architecture & Design Patterns | L83-L88 | Patterns for building scalable, resilient Azure AI Search apps, including multitenant SaaS designs and multi-region deployment, failover, and data replication strategies. | | Limits & Quotas | L89-L98 | Limits, quotas, and capacity planning for Azure AI Search: indexer schedules, execution/concurrency, runtime quotas (Serverless/S3 HD), enrichment limits, and vector index size/scale. | | Security | L99-L140 | Securing Azure AI Search: RBAC/Entra auth, keys, encryption, ACL-based document security, indexer permissions, private networking, firewall, and policy/compliance controls. | | Configuration | L141-L231 | Configuring Azure AI Search: data sources, knowledge bases, skillsets, analyzers, vectorization, semantic ranking, and query options for agentic retrieval and enrichment. | | Integrations & Coding Patterns | L232-L309 | Integrating Azure AI Search with data sources, skills, vectorization, and client apps; building and querying indexes with filters, semantic ranking, hybrid search, and knowledge stores. | | Deployment | L310-L318 | Automating Azure AI Search deployment with ARM/Bicep/Terraform, moving services across regions, checking regional feature support, 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 | | Troubleshoot Azure AI Search indexer issues without errors | https://learn.microsoft.com/en-us/azure/search/search-indexer-troubleshooting | | Diagnose and fix Azure AI Search collection filter errors | 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 | | Troubleshoot SharePoint permission-filtered search results | https://learn.microsoft.com/en-us/azure/search/troubleshoot-sharepoint-query-permission-filtering | | 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