Razi Text Generation

io.github.razikallayi由 GitHub 帳號 razikallayi 發布v1.0.0更新於 Oct 24, 2026

Text generation over MCP: prose, emails, blog outlines, SQL, humanizing, text diffs, fake data.

已驗證Streamable HTTP可網頁執行效率與工作流開發者工具AI 與機器學習

概覽

AI 產生的概覽

一個遠端 MCP 伺服器,用來產生文字:填充文案、郵件草稿、部落格大綱、SQL、人性化改寫、逐行文字比對和測試假資料。

可以做什麼
依簡短說明或參數產生填充文案、商務郵件內文和 Markdown 部落格大綱。改寫既有文字,讓它讀起來不像模型輸出,並把自然語言請求轉成 SQL 語句。逐行比對兩段文字,回報新增、刪除或修改的行。產生確定性的佔位人員記錄,供測試資料和 fixture 使用。
適用情境
適合需要快速佔位文案、郵件草稿、部落格骨架、初版 SQL 查詢、逐行文字比對或測試假記錄,又不想離開助理時使用。不適合必須逐字保留的內容,也不適合需要對照真實資料庫驗證的查詢。
執行需求
透過 streamable HTTP 連線至指定主機的遠端端點,不需本機套件或執行環境。可選的 Authorization 標頭攜帶 bearer 權杖,可提高速率限制並歸屬用量。
安裝前請注意
部分工具是付費模型呼叫,尤其是 generate_text。產生的 SQL 不會被執行或驗證,也不了解你的資料表結構。人性化改寫會改變措辭,不要用於引文、法律文字或程式碼。假資料來自固定詞表,並非真實個人資料。可選的 bearer 權杖屬於機密。郵件工具不會送出任何內容。若提供權杖,它會被傳送給服務提供者。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Razi Text Generation,將其新增到工作區。
  2. 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.razikallayi%2Frazi-text/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": {
    "razi-text-generation": {
      "type": "http",
      "url": "https://www.razi.pro/api/mcp/text"
    }
  }
}

README

該儲存庫沒有 README。

工具

7

humanize_text

Rewrite text you already have so it reads less like model output — fewer stock phrases, more contractions, varied sentence rhythm. Returns JSON { humanizedText }. Meaning is meant to be preserved but wording is not: never use it on text that must stay verbatim, such as quotes, legal copy or code. Use generate_text to produce new prose from a prompt and draft_email for a whole email; this one only transforms text it is given. Requires a signed-in razi.pro account — an anonymous call is rejected with 401. Paid model call; input capped at 10,000 characters and output at roughly 2,000 tokens, so long passages come back truncated. 30 calls per hour per account, and identical inputs may return a cached result.

輸入參數text必填level

draft_email

Write a business email body from a short brief. Returns JSON { email, cached } containing the body only — no subject line, no recipient, and nothing is sent anywhere. Choose this over generate_text when the output should be a whole email; use humanize_text to rewrite an email you already drafted. Paid model call. Anonymous callers get 3 per hour per IP and are then refused with 401; signed-in callers get 15 per minute per IP. Length is capped at roughly 400 tokens. Identical briefs may return a cached draft.

輸入參數toneprompt必填

generate_blog_outline

Produce a markdown heading structure for a blog post — title, introduction, numbered sections with subsections, conclusion and an FAQ block. Returns JSON { outline } holding the markdown. It writes the skeleton only, not the article: use generate_text for body prose and humanize_text to rework text that already exists. Paid model call, capped at roughly 1,000 tokens, so a large section count yields thinner sections. 10 calls per minute per IP; identical requests may return a cached outline.

輸入參數topic必填sections

generate_sql

Translate a natural-language request into a SQL statement. Returns JSON { sql } containing the query text and nothing else — no validation report, no complexity score, and the query is never executed or checked against a real database. It has no knowledge of your schema beyond what the request states, so table and column names are guesses unless you supply them. Review before running, especially anything that writes. Paid model call. Anonymous callers get 3 per hour per IP and are then refused with 401; signed-in callers get 15 per minute per IP. Capped at roughly 500 tokens. Answers are cached, so the same request returns the same query.

輸入參數dialectdescription必填

compare_text

Compare two blocks of text line by line. Returns JSON { identical, linesCompared, changeCount, changes[] }, where each change carries a 1-based line number, a change of 'added' | 'removed' | 'modified', and the before/after text. Lines are matched by POSITION, not by content: this is not an LCS diff, so inserting one line near the top reports every following line as modified. There is no character-level or word-level detail, and no unified-diff patch output. Whitespace and case are significant; \r\n and \n line endings are treated the same. Runs locally and costs nothing.

輸入參數text1必填text2必填

generate_fake_data

Produce placeholder person records for testing and fixtures. Returns JSON { type, count, records } where records is an array of strings, or of objects when type is 'user'. The values are drawn from a fixed word list by index, so they are DETERMINISTIC: the same arguments always return the same records, and asking twice does not give you fresh data. Emails all use example.com and phone numbers all use the +1-555 reserved range. It fabricates people only — for lorem-style prose use generate_text.

輸入參數type必填count

generate_text

Generate filler prose — lorem ipsum, random copy or sentences — for mockups and placeholder content. Returns JSON { text, provider, cached } with the blocks separated by newlines. A language model writes it, so it is a paid call and the output is approximate: type, length and count are phrased into the prompt rather than enforced, and the result will not match a requested character count exactly. For placeholder people (names, emails, addresses) use generate_fake_data, which is exact, free and deterministic. For a real email use draft_email, and to rework existing text use humanize_text. 15 calls per minute per IP; capped at roughly 1,500 tokens; identical requests may return a cached result.

輸入參數type必填countlength

版本歷史

1
  1. v1.0.0最新Sep 16, 2026

評分

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  • 圖示10 分
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    請宣告包含 README 的 GitHub 儲存庫,或在 OAuth 受保護資源後設資料中提供 resource_documentation。

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