azure-aigateway
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.
95
93%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly defines its scope (Azure API Management as AI Gateway), lists concrete capabilities, and provides an extensive set of natural trigger terms via an explicit WHEN clause. The description is well-structured, uses third person voice, and occupies a distinct niche that minimizes conflict risk with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description lists multiple specific concrete actions: configuring Azure API Management as an AI Gateway, semantic caching, token limiting, content safety, load balancing, jailbreak detection, adding Azure OpenAI backends, converting APIs to MCP, importing OpenAPI specs, and more. | 3 / 3 |
Completeness | Clearly answers both 'what' (Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents) and 'when' with an explicit 'WHEN:' clause listing numerous specific trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would say: 'semantic caching', 'token limit', 'content safety', 'load balancing', 'jailbreak detection', 'Azure OpenAI backend', 'AI Foundry model', 'MCP rate limiting', 'convert API to MCP', 'import OpenAPI to gateway'. These are terms practitioners would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — the combination of Azure API Management, AI Gateway, MCP tools, and specific policies like jailbreak detection and semantic caching creates a very clear niche that is unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill that efficiently covers Azure AI Gateway configuration with executable commands, clear policy references, and excellent progressive disclosure. The main weakness is the lack of explicit validation steps after key operations like backend creation and policy application, which would strengthen the workflow clarity. The troubleshooting table is a nice touch for common failure modes.
Suggestions
Add validation/verification commands after key operations (e.g., after 'az apim backend create', show how to verify the backend was created successfully and is reachable)
Include a brief feedback loop in the 'Add AI Backend' workflow, such as testing the backend connection before proceeding to policy configuration
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It avoids explaining what APIM is or how AI gateways work conceptually, instead jumping straight to actionable commands, policy references, and configuration steps. Every section earns its place. | 3 / 3 |
Actionability | Provides executable CLI commands for getting gateway details, testing endpoints, adding backends, and granting access. The commands are copy-paste ready with clear placeholder patterns (<apim-name>, <rg>, etc.). | 3 / 3 |
Workflow Clarity | The 'Apply AI Governance Policy' section provides a clear recommended ordering, and 'Add AI Backend' has sequential steps. However, there are no explicit validation checkpoints or feedback loops—after creating a backend or applying policies, there's no 'verify it worked' step before proceeding. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a concise overview and well-signaled one-level-deep references to policies.md, patterns.md, troubleshooting.md, and SDK references. The quick reference table provides immediate value while pointing to detailed docs. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- microsoft/azure-skills
- Commit
742d20b
- Reviewed
Table of Contents
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