Azure/gpt-rag-mcp is a Python server that exposes GPT-RAG capabilities through the Model Context Protocol. It is deployed with Azure resources and consumed by GPT-RAG through its MCP strategy, while the catalogue provides instructions, skills, and agents for operating it.
Borrowing it
Nothing to install: this file belongs to Azure/gpt-rag-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Azure/gpt-rag-mcp/main/.github/skills/engineering-principles/SKILL.mdgit clone --depth 1 https://github.com/Azure/gpt-rag-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/azure/gpt-rag-mcp/engineering-principles)<a href="https://agentmods.dev/skills/azure/gpt-rag-mcp/engineering-principles"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-mcp/engineering-principles.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.00229 |
| Opus 5 | $0.00018 | $0.00114 |
| Sonnet 5 | $0.00007 | $0.00046 |
| Haiku 4.5 | $0.00004 | $0.00023 |
Grade A, and why
engineering-principles scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
GPT-RAG MCP engineering principles
Load only the references needed:
| When the task involves | Read |
|---|---|
| Repository purpose, ownership, or runtime boundaries | MCP server architecture |
| Tools, resources, prompts, schemas, or protocol compatibility | MCP contracts and safety |
| Python design, async behavior, or maintainability | Python and async |
| Identity, secrets, deployment, networking, or operations | Security and operations |
| Tests, validation, compatibility, or evidence | Testing and evidence |
Use these principles as design questions rather than dogma. Task requirements, the current implementation, executable configuration, and protocol contracts remain the sources of truth.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 21 lines · 37 tokens per session scan A a080313f1f5c
engineering-principles is a skill published in the GitHub repository Azure/gpt-rag-mcp (22 stars, last pushed 6d ago), licensed MIT. It adds 37 tokens to every session and 229 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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