What is implementation?

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.

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.

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.

implementation — Agent by Azure

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add agents/azure/gpt-rag-mcp/implementation

Clone the repo

git clone --depth 1 https://github.com/Azure/gpt-rag-mcp

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. Scan, not verified.

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.

Model	Per session	Once invoked
Fable 5	$0.00031	$0.00224
Opus 5	$0.00015	$0.00112
Sonnet 5	$0.00006	$0.00045
Haiku 4.5	$0.00003	$0.00022

Measured 2d ago against content hash c0f767f11991, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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.

2d 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 27d ago), licensed MIT. It adds 31 tokens to every session and 224 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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