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Razi Text Generation

io.github.razikallayi/razi-text

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

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Rank
#4417
Score
81
Tool tokens
1,734
Tools
7

Tools

Token cost is the o200k count of the tools list JSON, including schemas, before any tool is called.

  • 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

  • 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 wh

  • 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 th

  • 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

  • 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

  • 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

  • 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 wri

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