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/instructions/documentation.instructions.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/instructions/azure/gpt-rag-mcp/documentation)<a href="https://agentmods.dev/instructions/azure/gpt-rag-mcp/documentation"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-mcp/documentation/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/instructions/azure/gpt-rag-mcp/documentation"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-mcp/documentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00230 | $0.00230 |
| Opus 5.5 | $0.00092 | $0.00092 |
| Sonnet 5.5 | $0.00046 | $0.00046 |
| Haiku 4.5 | $0.00023 | $0.00023 |
Grade A, and why
gpt-rag-mcp documentation.instructions.md 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 2mo 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
Repository documentation
- Describe the current FastMCP and Starlette service and the orchestrator
mcpstrategy; do not restore legacy runtime terminology. - Keep service setup, deployment, and Inspector guidance in
README.mdand operational diagnosis inTROUBLESHOOTING.md. - Keep contribution, security, support, and conduct policy in their dedicated files rather than duplicating it.
- Verify commands, paths, endpoints, configuration keys, labels, and defaults against executable repository sources.
- Link to the published GPT-RAG documentation instead of copying broad product guidance into this component repository.
- Coordinate changes on the
docsbranch ofAzure/GPT-RAGwhen this component changes umbrella deployment, configuration, or user experience. - Never put secrets, personal data, or private Azure validation environment and resource-group names in examples.
- Follow
SECURITY.md; do not direct vulnerability reports to public issues.
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.
- 2mo ago First seen · 22 lines · 230 tokens per session scan A 60abf309674a
gpt-rag-mcp documentation.instructions.md is an instructions file published in the GitHub repository Azure/gpt-rag-mcp (22 stars, last pushed 10d ago), licensed MIT. It adds 230 tokens to every session, about $0.0009 per session on Opus 5.5. 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.
gpt-rag-mcp documentation.instructions.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| gpt-rag-mcp documentation.instructions.mdAzure/gpt-rag-mcp | Instructions file | 22 | 10d ago | MIT | A | 230 | GitHub Copilot |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |