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.
npx agentmods add instructions/azure/gpt-rag-mcp/python-mcpgit clone --depth 1 https://github.com/Azure/gpt-rag-mcpWhat 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 | $0.00226 | $0.00226 |
| Opus 5 | $0.00113 | $0.00113 |
| Sonnet 5 | $0.00045 | $0.00045 |
| Haiku 4.5 | $0.00023 | $0.00023 |
Grade A, and why
gpt-rag-mcp python-mcp.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 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.
What it actually says
Python MCP implementation
- Python 3.12 and
pyproject.tomlare authoritative. - Prefer explicit types, intent-revealing names, small cohesive functions, and public docstrings that explain behavior and contract.
- Keep
src/server.pyfocused on application composition and registration. Put capability logic in the appropriatesrc/tools/,src/resources/, orsrc/prompts/module. - Reuse existing helpers before adding dependencies or abstractions.
- Preserve async correctness. Do not perform blocking network, disk, process, or provider I/O on an async request path.
- Preserve the original cause when translating exceptions and use configured logging for runtime diagnostics.
- Do not add broad catches, silent defaults, success-shaped fallbacks, or mutable global request state.
- Keep imports package-correct for both local
uvexecution and the container entry point. - Restore with
uv sync; run the narrowest available checks and an import or startup check for changed runtime modules.
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 · 24 lines · 226 tokens per session scan A 49b2a02394c5
gpt-rag-mcp python-mcp.instructions.md is an instructions file published in the GitHub repository Azure/gpt-rag-mcp (22 stars, last pushed 27d ago), licensed MIT. It adds 226 tokens to every session, about $0.0011 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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