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gpt-rag-mcp: Agent for Claude Code

.github/agents/architecture.agent.md

architecture is an agent for Claude Code from Azure/gpt-rag-mcp. It costs 41 tokens per session (230 once invoked), scanned A, original, MIT.

An architecture analysis guide for GPT-RAG MCP systems, covering how components communicate, where security boundaries sit, and which design trade-offs matter.

In plain words
What is it for?
Use it to compare architecture options, define protocol and system boundaries, record major decisions, and prepare an implementation handoff.
Why use it?
It helps teams assess structural or difficult-to-reverse changes before implementation, including compatibility, deployment, identity, and rollback concerns.

Agent for Claude Code ✓ vendor

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: mentions AGENTS.md.

This is Azure/gpt-rag-mcp's own configuration. It tells Claude Code how to work on gpt-rag-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gpt-rag-mcp configures →

not rated 22repo 10d ago A scan 41 tokens original MIT
View source ↗ Azure/gpt-rag-mcp
About the project

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.

Azure/gpt-rag-mcp · 22 stars · on GitHub

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Azure/gpt-rag-mcp/main/.github/agents/architecture.agent.md
Clone the repo
git clone --depth 1 https://github.com/Azure/gpt-rag-mcp

Made for: Claude Code.

Wrote 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.

agentmods badge for architecture

README.md
[![agentmods](https://agentmods.dev/badge/agents/azure/gpt-rag-mcp/architecture/github.svg)](https://agentmods.dev/agents/azure/gpt-rag-mcp/architecture)
Your own site
<a href="https://agentmods.dev/agents/azure/gpt-rag-mcp/architecture"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-mcp/architecture/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.

agentmods 80×15 button for architecture

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 41 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 230 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00041 $0.00230
Opus 5.5 $0.00016 $0.00092
Sonnet 5.5 $0.00008 $0.00046
Haiku 4.5 $0.00004 $0.00023

Measured 2mo ago against content hash ab69eb9ded89, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

.github/agents/architecture.agent.md · 27 lines

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.

Changes

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.

  1. 2mo ago First seen · 27 lines · 41 tokens per session scan A ab69eb9ded89

Subscribe to this mod's changes

architecture is an agent published in the GitHub repository Azure/gpt-rag-mcp (22 stars, last pushed 10d ago), licensed MIT. It adds 41 tokens to every session and 230 once invoked, about $0.0002 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.

Backend and APIsBuilding agents
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