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Razi Text Generation

io.github.razikallayi/razi-text · v1.0.0

Measured by MCP Station

Description written by the publisher, not verified

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

Connect

claude mcp add --transport http razi-text https://www.razi.pro/api/mcp/text

{
  "mcpServers": {
    "razi-text": {
      "url": "https://www.razi.pro/api/mcp/text"
    }
  }
}

Tools as reported by the server

• 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. Us
• 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. Pa
• 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
• 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 state
• 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
• 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 recor
• 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

Repository · Website · JSON · Text for agents · Registry updated 2026-09-08