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.
76
93%
Does it follow best practices?
Impact
—
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), provides an explicit WHEN clause with extensive trigger terms, and occupies a distinct niche. The trigger terms are well-chosen and cover both high-level concepts (AI cost control, AI model governance) and specific tasks (add Azure OpenAI backend, import OpenAPI to gateway). The description is concise yet comprehensive.
| 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 with a clear niche: Azure API Management as an AI Gateway. The combination of Azure APIM, AI gateway policies, MCP tools, and specific features like jailbreak detection and AI Foundry models makes it very 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-crafted skill that serves as an effective gateway overview—concise, actionable, and well-organized with clear progressive disclosure to detailed references. The main weakness is the lack of explicit validation/verification steps in multi-step workflows like adding backends and applying policies, which is important for operations that can fail silently. The troubleshooting table is a nice touch that adds practical value.
Suggestions
Add explicit validation steps after key operations (e.g., 'Verify backend was created: az apim backend show ...' and 'Test policy application by sending a request and checking response headers').
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and well-structured. It avoids explaining what APIM is or how AI gateways work conceptually—it jumps straight to actionable tables, commands, and references. Every section earns its place. | 3 / 3 |
Actionability | Provides concrete, executable az CLI commands and curl examples with clear placeholders. The 'Add AI Backend' section gives a complete three-step workflow with real commands. The quick reference table maps policies to purposes with links to details. | 3 / 3 |
Workflow Clarity | The 'Apply AI Governance Policy' section provides a clear recommended ordering but lacks explicit validation checkpoints—there's no 'verify the policy was applied' or 'test after each step' guidance. The 'Add AI Backend' workflow similarly lacks verification steps after creating the backend or granting access. | 2 / 3 |
Progressive Disclosure | Excellent structure: concise overview with well-signaled one-level-deep references to policies.md, patterns.md, troubleshooting.md, and SDK references. The main file serves as a clear navigation hub without inlining excessive detail. References are clearly labeled with descriptive text. | 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
d02fd24
- Reviewed
Table of Contents
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