Borrowing it
Nothing to install: this file belongs to ZahiriNatZuke/whisper-transcribe-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/ZahiriNatZuke/whisper-transcribe-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/ZahiriNatZuke/whisper-transcribe-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/instructions/zahirinatzuke/whisper-transcribe-mcp/agents-md)<a href="https://agentmods.dev/instructions/zahirinatzuke/whisper-transcribe-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/zahirinatzuke/whisper-transcribe-mcp/agents-md/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.
<a href="https://agentmods.dev/instructions/zahirinatzuke/whisper-transcribe-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/zahirinatzuke/whisper-transcribe-mcp/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00699 | $0.00699 |
| Opus 5 | $0.00349 | $0.00349 |
| Sonnet 5 | $0.00140 | $0.00140 |
| Haiku 4.5 | $0.00070 | $0.00070 |
Grade A, and why
whisper-transcribe-mcp AGENTS.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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
whisper-transcribe-mcp is a Python MCP (Model Context Protocol) server that exposes audio transcription tools using either faster-whisper (local, offline) or the OpenAI Whisper API (cloud). Published on PyPI and consumed via uvx or pip.
Development Setup
# Install the locked development environment with all optional backends
uv sync --group dev --extra all
# Run the same checks as CI
uv run ruff check .
uv run ruff format --check .
uv run pytest -q
Running the Server Locally
# Local backend
WHISPER_MODEL=base python -m whisper_transcribe.server
# OpenAI backend
OPENAI_API_KEY=sk-... python -m whisper_transcribe.server
Building and Publishing
# Build the package
uv build
# Release a new version from a clean main branch. This synchronizes pyproject.toml,
# uv.lock, and server.json, waits for CI, tags the release, and creates the GitHub release.
./release.sh 1.2.3
Pushing the version tag triggers .github/workflows/publish.yml, which publishes first to PyPI
and then to the official MCP Registry using OIDC. See docs/publishing.md for the full checklist.
Architecture
The entire server lives in a single file: whisper_transcribe/server.py.
- Built on FastMCP (
fastmcp>=3.0), which handles MCP protocol, tool registration, and stdio transport. - Backend selection is determined at startup by the presence of
OPENAI_API_KEY— there is no runtime switching. - The local
WhisperModelis lazily loaded and cached in_local_model(module-level global), and reloaded only if the requestedmodel_sizechanges. transcribe_base64delegates totranscribe_fileafter writing a temp file, then cleans it up.
Optional dependency groups (pyproject.toml)
| Extra | Installs | Enables |
|---|---|---|
[local] |
faster-whisper |
Local CPU inference |
[openai] |
openai |
OpenAI Whisper API |
[all] |
both | Auto-selects based on env |
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.
- 10d ago First seen · 72 lines · 699 tokens per session scan A 33ecc32073d3
whisper-transcribe-mcp AGENTS.md is an instructions file published in the GitHub repository ZahiriNatZuke/whisper-transcribe-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 699 tokens to every session, about $0.0035 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 2026-08-31.
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