Frenchie

io.github.Lab94由 GitHub 帳號 Lab94 發布v0.5.0更新於 Oct 24, 2026

OCR, transcription, file extraction, and image generation for AI agents via MCP.

已驗證Streamable HTTP可網頁執行檔案與儲存媒體與設計AI 與機器學習

概覽

AI 產生的概覽

Frenchie 透過付費的點數制雲端服務，把 PDF、圖片、音訊影片和 Office 檔案轉成 Markdown，並能依文字提示生成圖片。

可以做什麼
用 OCR 把 PDF 和圖片轉成 Markdown 文字，可指定語言提示以提升準確度，本機模式下結果會自動儲存。把音訊和影片檔轉寫成 Markdown 文稿，可指定 ISO 語言代碼或自動辨識。把 DOCX、XLSX、CSV、TSV 和 PPTX 文件擷取為 Markdown，儲存到本機資料夾或直接內嵌回傳。依文字提示生成單張圖片，可設定風格、尺寸、品質、格式和背景。在託管 HTTP 模式下上傳檔案取得預簽章網址，並取得結果檔案的暫時下載連結。查詢非同步處理任務的最新結果。
適用情境
適合需要讓助理把掃描版 PDF、圖片、會議錄音或 Office 文件讀成 Markdown，或依提示快速生成圖片的場合。特別適合已能處理檔案、又不想自行建置 OCR 或轉寫流程的代理程式。
執行需求
可作為託管端點 mcp.getfrenchie.dev 使用，也可作為本機 npm 套件透過 npx 啟動，後者需要 Node.js。需要 Frenchie 帳號和 API 金鑰，透過 FRENCHIE_API_KEY 或 Authorization bearer 標頭提供。可透過 FRENCHIE_DEFAULT_LANGUAGE 或 X-Frenchie-Default-Language 標頭設定預設語言。所有處理都需要網路存取。
安裝前請注意
處理會消耗付費點數：OCR 每頁 1 點，轉寫每分鐘 2 點，擷取每頁或每張投影片 0.5 至 1 點，生成圖片每張 20 點，首次註冊贈送 100 點。檔案會上傳到第三方服務；結果在首次交付後約 30 分鐘過期，需及時儲存 Markdown。API 金鑰屬於機密，安裝程式會將其寫入代理程式設定檔。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Frenchie，將其新增到工作區。
  2. 連接 Authorization token 憑證。它們會被私密儲存，絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.Lab94%2Ffrenchie-skill/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "frenchie": {
      "type": "http",
      "url": "https://mcp.getfrenchie.dev/",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}

README

[Frenchie — your agent's best friend.]

[smithery badge]

@lab94/frenchie

Frenchie — your agent's best friend.

Install Frenchie in your coding agent with one command. Frenchie Kit gives agents file and multimodal capabilities: read PDFs and images, transcribe audio and video, extract Office/CSV files to Markdown, and generate images from text prompts. Frenchie Method gives teams a structured workflow from requirement to release.

Upgrading from 0.1.x or 0.2.x? See MIGRATION.md for the breaking changes in 0.3.0 (stdio metadata-only responses, absolute-path MCP configs, new mcp --help / --selftest flags).

This package ships:

  • Frenchie Kit (/ocr, /transcribe, /extract, /generate-image, /frenchie-status commands + HTTP/stdio MCP guidance)
  • Frenchie Method (/frenchie-method, /requirement, /qa-analysis, /implement, /run-test, /pr-review, /release-check, and related workflow commands)
  • The Frenchie stdio MCP server (lab94-frenchie mcp) bundled for npx
  • An installer that wires skills and MCP config into your agent

Quick start

1. Install the skills

From the root of your project:

bash
npx @lab94/frenchie install

This installs Frenchie Kit and Frenchie Method skills. Method is text-only and works without an API key.

Optional Method config:

bash
npx @lab94/frenchie install --init-method

This creates .frenchie/method.config.json and .frenchie/.gitignore; it does not edit your root .gitignore.

2. Log in for Kit MCP tools when you need file or image capabilities

Create an account at getfrenchie.dev. You get 100 free credits on your first signup, once per email. No card required.

Then connect your local agent:

bash
npx @lab94/frenchie login

The login command opens a browser device-code flow, then writes FRENCHIE_API_KEY into the MCP config the installer created. The full key is never printed.

Fallback if you already have a key from the dashboard:

bash
npx @lab94/frenchie install --api-key fr_your_key_here

To target a specific agent:

bash
npx @lab94/frenchie login --agent claudenpx @lab94/frenchie login --agent cursornpx @lab94/frenchie login --agent codexnpx @lab94/frenchie login --agent vscodenpx @lab94/frenchie login --agent gemini

User-level installs (Antigravity, Windsurf, Zed, Claude Desktop) need the --global flag — auto-detect never writes under $HOME:

bash
npx @lab94/frenchie login --agent antigravity   --globalnpx @lab94/frenchie login --agent windsurf       --globalnpx @lab94/frenchie login --agent zed            --globalnpx @lab94/frenchie login --agent claude-desktop --global

Fallback with an existing key:

bash
npx @lab94/frenchie install --agent claude        --api-key fr_…npx @lab94/frenchie install --agent codex         --api-key fr_…npx @lab94/frenchie install --agent antigravity   --global --api-key fr_…npx @lab94/frenchie install --agent claude-desktop --global --api-key fr_…

After login, your agent can call ocr_to_markdown, transcribe_to_markdown, extract_to_markdown, or generate_image. OCR/transcription/extraction results are saved to .frenchie/<name>/result.md automatically; generated images are saved to .frenchie/<slug>/generated.<ext>.

Upgrading later

When a new Frenchie version ships, the pinned package spec inside each MCP config (@lab94/[email protected]) needs to point at the new version so npx stops serving a cached older bundle. Two upgrade commands cover everything:

bash
# Project-scoped configs in this directory (.mcp.json, .cursor/mcp.json, .codex/config.toml, etc.)npx @lab94/frenchie@latest install
# Every $HOME config that already has a frenchie entry — Antigravity, Claude Desktop, Windsurf, Zed,# and any global Claude Code / Cursor / Codex / Gemini configs you set up. cwd doesn't matter.npx @lab94/frenchie@latest install --global

install --global is upgrade-mode: it only rewrites frenchie entries that already exist. It will not surprise-install anything new under $HOME. First-time global installs still go through install --agent <name> --global.

3. Restart your agent

The installer prints the restart hint for your agent. After that, ask:

OCR ./report.pdf with Frenchie

…and Frenchie takes it from there.

For workflow orchestration, ask:

txt
/frenchie-method

/frenchie remains the Kit entrypoint in agents that use a single Frenchie command. It is not an alias for Method.

Hosted agents (Lovable, Manus, Claude.ai, ChatGPT.com, Le Chat)

These agents can't run local npm binaries. Use the hosted MCP endpoint instead:

URL:    https://mcp.getfrenchie.devHeader: Authorization: Bearer fr_your_key_here

The same @lab94/frenchie skill files work in HTTP mode — install them once with install --agent <name> and the included SKILL.md will tell the agent to upload files via upload_file before calling OCR/transcription/extraction. Image generation does not need an upload step in HTTP mode; it returns a short-lived imageUrl that the agent should download for the user.

What you get

Frenchie Kit

CommandWhat it does
/ocr <file>Parse a PDF or image into Markdown
/transcribe <file>Parse audio or video into a Markdown transcript
/extract <file>Parse DOCX, XLSX, CSV, TSV, or PPTX into Markdown
/generate-image <prompt>Generate a single image from a text prompt
/frenchie-statusCheck credits and recent jobs

Under the hood, Frenchie exposes these MCP tools:

  • ocr_to_markdown
  • transcribe_to_markdown
  • extract_to_markdown
  • generate_image
  • get_job_result
  • upload_file (HTTP mode only)
  • fetch_result_file (HTTP mode only)

Frenchie Method

Slash notation is the canonical Method command name. Claude Code reads command files directly. Codex Desktop gets the same entries through wrapper skills such as requirement and qa-analysis; restart Codex after install before trying /requirement. Hosts without slash support can use natural language such as Frenchie Method requirement ....

CommandWhat it does
/frenchie-methodRoute a task through Method from requirement to release
/method-checkValidate Method config, artifacts, gates, and next action
/requirementTurn a request into scoped requirements
/qa-analysis / /test-caseDesign proof before implementation planning
/sa-analysis / /implementation-planAnalyze architecture and implementation approach
/implementExecute from Method artifacts using the current agent
/run-testRun repo-declared verification and write a test report
/pr-review / /review-prReview the result, tests, security, and artifact drift
/release-checkRead-only release readiness gate

Method stores optional workflow artifacts under .frenchie/docs/feature/. install --init-method also writes a visible skillTriggers skeleton in .frenchie/method.config.json; command keys are empty slots teams can fill with installed agent skills, for example "requirement": ["brainstorming"]. Teams decide whether to commit or ignore Method config and artifacts.

Invocation — how to call Frenchie in each agent

Every agent handles MCP a little differently. /ocr is a Claude Code-only slash command; Method slash commands are host-dependent and use Codex wrapper skills outside Claude Code. Other agents use natural language, @-mention, or a server-name slash command. All facts below are dogfood-verified.

AgentInvokeFull guide
Claude Code/ocr TOR.pdfdocs
CursorUse Frenchie to OCR TOR.pdfdocs
Codex (Desktop / CLI / IDE)Kit: /frenchie TOR.pdf · @frenchie ocr TOR.pdf; Method: /requirement after restart, or Frenchie Method requirement ...docs
Antigravity/frenchie TOR.pdf (invokes by server name)docs
VS Code Copilot/frenchie TOR.pdfdocs
Claude DesktopUse Frenchie to OCR TOR.pdfdocs
WindsurfOCR TOR.pdf via Frenchiedocs
Gemini CLIOCR TOR.pdf with Frenchiedocs
ZedOCR TOR.pdf via Frenchiedocs

Something not working? See the symptom-first troubleshooting guide — every error we've hit in dogfood has a canonical entry.

Pricing

Simple numbers. No subscriptions.

File extraction pricing: DOCX 0.5 credits per page, XLSX 0.5 credits per sheet, CSV/TSV 0.5 credits per file, PPTX 1 credit per slide.

ActionCost
OCR1 credit per page
Transcription2 credits per minute
DOCX extraction0.5 credits per page
XLSX extraction0.5 credits per sheet
CSV/TSV extraction0.5 credits per file
PPTX extraction1 credit per slide
Image generation20 credits per image

$1 = 100 credits. Credits don't expire.

Privacy

Files are processed and deleted. Results expire about 30 minutes after first delivery. If you need a durable copy, save the Markdown when it comes back.

Supported formats

OCR: PDF, PNG, JPG, JPEG, WebP

Transcription: MP3, M4A, WAV, MP4, MOV, WebM

File extraction: DOCX, XLSX, CSV, TSV, PPTX

Image generation: PNG, JPEG, WebP output from text prompts

Need help?

來源：README.md，提交 91ba927

工具

7

ocr_to_markdown

OCR to Markdown

Convert PDF/image files into Markdown through Frenchie

輸入參數api_keyfile_pathuploaded_file_reference

transcribe_to_markdown

Transcribe to Markdown

Convert audio/video files into Markdown transcripts through Frenchie. Set language (ISO 639-1 code, e.g. 'th', 'en', 'ja') for better accuracy; omit for auto-detection.

輸入參數api_keylanguagefile_pathuploaded_file_reference

generate_image

Generate Image

Generate a single image from a text prompt through Frenchie. Required: prompt. Optional: style (free-text style direction), size, quality, format, background. stdio mode auto-saves the image to .frenchie/<slug>/generated.<ext>; HTTP mode returns a presigned imageUrl that the agent should download for the user.

輸入參數sizestyleformatprompt必填api_keyqualitybackgroundoutput_dir

extract_to_markdown

Extract to Markdown

Extract structured documents (.docx, .xlsx, .csv, .tsv, .pptx) into Markdown through Frenchie. stdio mode auto-saves the result to .frenchie/<name>/result.md; HTTP mode returns inline Markdown.

輸入參數api_keyfile_pathuploaded_file_reference

get_job_result

Get Job Result

Fetch the latest async Frenchie job result

輸入參數job_id必填api_key

upload_file

Upload File (HTTP)

Get a presigned upload URL for use with ocr_to_markdown, transcribe_to_markdown, or extract_to_markdown in HTTP mode. After calling this tool, PUT the file to upload_url (with the correct Content-Type header), then pass object_key as uploaded_file_reference to the processing tool.

輸入參數api_keyfilename必填file_size必填mime_type必填

fetch_result_file

Fetch Result File (HTTP)

Get a temporary download URL for a result file from OCR/transcription output. Use this to download images referenced as frenchie-result: in the result markdown.

輸入參數api_keyobject_key必填

版本歷史

1
  1. v0.5.0最新Sep 16, 2026

評分

已滿足適用檢查項的 100%

該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 圖示10 分
  • 文件15 分
  • 授權條款10 分