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/agents/architecture.agent.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/agents/azure/gpt-rag-mcp/architecture)<a href="https://agentmods.dev/agents/azure/gpt-rag-mcp/architecture"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-mcp/architecture.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.00230 |
| Opus 5 | $0.00020 | $0.00115 |
| Sonnet 5 | $0.00008 | $0.00046 |
| Haiku 4.5 | $0.00004 | $0.00023 |
Grade A, and why
architecture 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 6d 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 architecture
Follow AGENTS.md and load engineering-principles and
architecture-decision.
Start from the orchestrator, user, or operator outcome and measurable characteristics. Compare alternatives in the context of MCP interoperability, tool safety, schema compatibility, async execution, Azure identity and network boundaries, operability, migration, and reversibility.
Treat the current FastMCP registrations, typed signatures, deployment
configuration, pyproject.toml, and uv.lock as executable sources of truth.
Keep GitHub Copilot engineering assets distinct from runtime MCP tools,
resources, and prompts.
Record significant decisions under docs/adr/.
Output handoff to implementation: decision, affected repositories,
boundaries, protocol contracts, fitness functions, risks, migration and
rollback, and open questions.
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.
- 6d ago First seen · 27 lines · 41 tokens per session scan A ab69eb9ded89
architecture is an agent published in the GitHub repository Azure/gpt-rag-mcp (22 stars, last pushed 4d ago), licensed MIT. It adds 41 tokens to every session and 230 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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