/azure-content-understanding

name: azure-content-understanding
description: Expert knowledge for Azure Content Understanding in Foundry Tools development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when building Content Understanding analyzers, RAG document flows, agent integrations, RPA workflows, or REST/SDK apps, and other Azure Content Understanding in Foundry Tools related development tasks. Not for Azure Speech in Foundry Tools (use azure-speech), Azure AI Search (use azure-cognitive-search), Azure AI Vision (use azure-ai-vision), Azure AI Document Intelligence (use azure-document-intelligence).
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
  generated_at: "2026-11-01"
  generator: "docs2skills/1.0.0"

Azure Content Understanding in Foundry Tools Skill

This skill provides expert guidance for Azure Content Understanding in Foundry Tools. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. 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 | L36-L40 | Using diagnostics from the Content Understanding REST API to investigate failures, interpret error codes, and troubleshoot processing or configuration issues. | | Best Practices | L41-L46 | Improving Content Understanding accuracy using layout, labels, and feedback, plus using confidence scores and grounding to validate and refine document analysis results. | | Decision Making | L47-L55 | Guidance on choosing Azure AI tools, model deployments, and Studio vs Foundry, plus migration from preview to GA and estimating/optimizing Content Understanding costs. | | Architecture & Design Patterns | L56-L62 | Guidance on when to use agentic mode, how to design RAG-based document solutions, and how to build RPA workflows using Azure Content Understanding. | | Limits & Quotas | L63-L68 | Guidance on safe use of synchronous Content Understanding calls and detailed quotas/limits (throughput, payload sizes, concurrency) for planning and troubleshooting usage. | | Security | L69-L74 | Configuring security for Content Understanding: setting guardrails on analyzer outputs, securing analyzers and data, access control, and safe handling of sensitive content. | | Configuration | L75-L87 | Configuring and customizing Content Understanding: analyzers, classifiers, splitting, workflows, routing, Markdown/audiovisual outputs, and managing/copying Foundry resources. | | Integrations & Coding Patterns | L88-L94 | Patterns and code samples for integrating Content Understanding with agents, LangChain, REST/SDK calls, and building custom analyzers via the API. |

Troubleshooting

| Topic | URL | |-------|-----| | Use diagnostics from Content Understanding REST API | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/how-to/retrieve-diagnostics |

Best Practices

| Topic | URL | |-------|-----| | Apply best practices for Content Understanding accuracy | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/best-practices | | Improve document analysis with confidence and grounding | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/analyzer-improvement |

Decision Making

| Topic | URL | |-------|-----| | Choose Azure AI tools for document processing | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/choosing-right-ai-tool | | Choose model deployments for Content Understanding analyzers | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/models-deployments | | Choose between Content Understanding Studio and Foundry | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/foundry-vs-content-understanding-studio | | Migrate Content Understanding from preview to GA | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/how-to/migration-preview-to-ga | | Estimate and optimize Azure Content Understanding costs | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/pricing-explainer |

Architecture & Design Patterns

| Topic | URL | |-------|-----| | Decide when to use agentic mode for documents | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/agentic-mode | | Design a RAG solution with Content Understanding | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/tutorial/build-rag-solution | | Design RPA workflows using Content Understanding | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/tutorial/robotic-process-automation |

Limits & Quotas

| Topic | URL | |-------|-----| | Use synchronous Conte