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/documentation-consistency/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/documentation-consistency)<a href="https://agentmods.dev/skills/azure/gpt-rag-mcp/documentation-consistency"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-mcp/documentation-consistency/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/azure/gpt-rag-mcp/documentation-consistency"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-mcp/documentation-consistency.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00039 | $0.00237 |
| Opus 5 | $0.00019 | $0.00118 |
| Sonnet 5 | $0.00008 | $0.00047 |
| Haiku 4.5 | $0.00004 | $0.00024 |
Grade A, and why
documentation-consistency 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 9d 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 documentation consistency
- Identify the user, orchestrator, or operator behavior that changed.
- Search this repository for the capability, configuration key, endpoint, parameter, and previous terminology.
- Update service setup and Inspector guidance in
README.mdand operational diagnosis inTROUBLESHOOTING.md. - If umbrella GPT-RAG deployment, configuration, or user experience changes,
search and update the
docsbranch ofAzure/GPT-RAGin the same coordinated change. - Keep repository READMEs focused; link to published GPT-RAG documentation instead of duplicating broad product guidance.
- Verify examples against executable defaults and current protocol contracts.
- Report every documentation file, branch, or pull request in the implementation handoff.
A visible change is incomplete until documentation is updated or the search demonstrates that no documentation is affected.
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.
- 9d ago First seen · 24 lines · 39 tokens per session scan A d87d23b4e6ca
documentation-consistency is a skill published in the GitHub repository Azure/gpt-rag-mcp (22 stars, last pushed 7d ago), licensed MIT. It adds 39 tokens to every session and 237 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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