azure-cognitive-search
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 data sources, skillsets, vector/semantic search, indexers, or multi-region search architectures, 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 Synapse Analytics (use azure-synapse-analytics).
- Installs
- 226
- Last 24h
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- Trending rank
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- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/microsoftdocs/agent-skills --skill azure-cognitive-search- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads azure-cognitive-search automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
- Preferred: Use
mcp_microsoftdocs:microsoft_docs_fetchwith query stringfrom=learn-agent-skill. Returns Markdown. - Fallback: Use
fetch_webpagewith query stringfrom=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 | 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 capacity planning for Azure AI Search: billing/free enrichment, indexer schedules/concurrency/runtime, service capacity limits, and vector index size/throughput constraints. |
| Security | L99-L141 | Securing Azure AI Search: RBAC/Entra ID auth, keyless access, keys and encryption, indexer permissions (SQL, Storage, SharePoint, Cosmos), ACL/Purview label ingestion, and network isolation (firewall, private endpoints). |
| Configuration | L142-L234 | Configuring Azure AI Search: data sources, knowledge bases, skillsets, analyzers, vectorization, semantic ranking, and query options for agentic retrieval and enrichment. |
| Integrations & Coding Patterns | L235-L310 | Patterns and code for integrating Azure AI Search: indexers, skills, vectorization, query syntax (Lucene/OData), semantic ranking, filters, pagination, and app/Power BI integrations. |
| Deployment | L311-L319 | 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
Best Practices
Decision Making
Architecture & Design Patterns
| Topic | URL |
|---|---|
| Design multitenant and isolated content in Azure AI Search | https://learn.microsoft.com/en-us/azure/search/search-modeling-multitenant-saas-applications |
| Design multi-region architectures for Azure AI Search | https://learn.microsoft.com/en-us/azure/search/search-multi-region |
Limits & Quotas
| Topic | URL |
|---|---|
| Billing limits and free quotas for Azure AI Search enrichment | https://learn.microsoft.com/en-us/azure/search/cognitive-search-attach-cognitive-services |
| Manage indexer execution, duration, and concurrency | https://learn.microsoft.com/en-us/azure/search/search-howto-run-reset-indexers |
| Configure Azure AI Search indexer schedules and limits | https://learn.microsoft.com/en-us/azure/search/search-howto-schedule-indexers |
| Understand indexer runtime quotas on Serverless and S3 HD | https://learn.microsoft.com/en-us/azure/search/search-indexer-high-density-serverless-overview |
| Plan Azure AI Search capacity using service limits | https://learn.microsoft.com/en-us/azure/search/search-limits-quotas-capacity |
| Understand Azure AI Search vector index limits | https://learn.microsoft.com/en-us/azure/search/vector-search-index-size |
Security
Configuration
Integrations & Coding Patterns
Deployment
| Topic | URL |
|---|---|
| Deploy Azure AI Search via ARM templates | https://learn.microsoft.com/en-us/azure/search/search-get-started-arm |
| Deploy Azure AI Search using Bicep files | https://learn.microsoft.com/en-us/azure/search/search-get-started-bicep |
| Provision Azure AI Search services with Terraform | https://learn.microsoft.com/en-us/azure/search/search-get-started-terraform |
| Move Azure AI Search services across regions | https://learn.microsoft.com/en-us/azure/search/search-howto-move-across-regions |
| Check Azure AI Search regional feature availability | https://learn.microsoft.com/en-us/azure/search/search-region-support |
| Deploy C# Azure AI Search apps to Container Apps | https://learn.microsoft.com/en-us/azure/search/tutorial-csharp-deploy-web-search |
FAQ
What does the azure-cognitive-search skill do?
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 data sources, skillsets, vector/semantic search, indexers, or multi-region search architectures, 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 Synapse Analytics (use azure-synapse-analytics).
How do I install azure-cognitive-search?
Run npx skills add https://github.com/microsoftdocs/agent-skills --skill azure-cognitive-search in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/azure-cognitive-search/ (personal) or .claude/skills/azure-cognitive-search/ (project).
Which AI agents can use azure-cognitive-search?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is azure-cognitive-search safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains azure-cognitive-search?
It is published by microsoftdocs in microsoftdocs/agent-skills. It has 226 recorded installs.
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