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gpt-rag-mcp: Agent for Claude Code

.github/agents/implementation.agent.md

implementation is an agent for Claude Code from Azure/gpt-rag-mcp. It costs 31 tokens per session (224 once invoked), scanned A, original, MIT.

A scoped implementation workflow for making, testing, and documenting changes to GPT-RAG MCP. GPT-RAG MCP is a server that connects tools or data sources to an AI system using the Model Context Protocol.

In plain words
What is it for?
Use it to inspect registrations and call sites, edit the appropriate runtime modules, add behavioral tests, validate protocol changes with MCP Inspector, and report changed files, results, dependencies, and risks.
Why use it?
It helps turn an agreed issue, plan, or architecture decision into a focused code change. It also checks compatibility, security, tests, documentation, and effects on related repositories before handoff.

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 31 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/implementation.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 implementation

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

Your own site · 80×15
<a href="https://agentmods.dev/agents/azure/gpt-rag-mcp/implementation"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-mcp/implementation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 31 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 224 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 $0.00031 $0.00224
Opus 5.5 $0.00012 $0.00090
Sonnet 5.5 $0.00006 $0.00045
Haiku 4.5 $0.00003 $0.00022

Measured 2mo ago against content hash c0f767f11991, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

implementation 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/implementation.agent.md · 27 lines

What it actually says

GPT-RAG MCP implementation

Follow AGENTS.md, .github/copilot-instructions.md, and every scoped instruction that applies.

Investigate registrations, call sites, contracts, scripts, and documentation. Make the smallest coherent change and preserve MCP protocol and orchestrator behavior by default. Keep src/server.py thin and put capability logic in the focused runtime module.

Before editing, confirm acceptance criteria, security and compatibility risks, affected repositories, and documentation impact. Add focused behavioral tests when feasible, run the existing validation for the changed boundary, and use the MCP Inspector for capability or contract changes.

Input handoff: an issue, plan, or ADR with high-impact decisions resolved.

Output handoff: delivered behavior, changed files, commands and results, contract impact, cross-repository dependencies, documentation status, and residual risks.

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 · 31 tokens per session scan A c0f767f11991

Subscribe to this mod's changes

implementation is an agent published in the GitHub repository Azure/gpt-rag-mcp (22 stars, last pushed 10d ago), licensed MIT. It adds 31 tokens to every session and 224 once invoked, about $0.0001 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 APIsTesting
Related

implementation compared with agents from other repositories

implementation next to 6 agents from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
implementationAzure/gpt-rag-mcpAgent2210d agoMITA31Claude Code
Salesforce Apex & Triggers Developmentgithub/awesome-copilotAgent41kyesterdayMITA26Claude Code
API Tester Specialistfugazi/test-automation-skills-agentsAgent26211d agoMITA34Claude Code
qa-executorrlajous/claude-code-commandsAgent303d agoMITA21Claude Code
scaffolderfuxicodex/FuxiAgent3.2k17d agononeA49Claude Code
pact-coveragepactflow/pactflow-agent-skillsAgent72d agoMITA103Claude Code
n8n-node-verifierneurawork-git/n8n-autopilotAgent184d agoMITA83Claude Code