# Razi Text Generation

- Registry name: `io.github.razikallayi/razi-text` · version 1.0.0
- Categories (automatic, keyword rules): Databases, Developer tools, Markets & trading, Finance & markets, Email & messaging, Productivity
- Authentication at last check: No auth
- Declared risk: Not declared (tools carry no read/destructive hints)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.razikallayi%2Frazi-text
- Server page: https://mcpstation.net/s/io.github.razikallayi--razi-text.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://www.razi.pro/api/mcp/text`

## Tools (as reported by the server, not verified)

- `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

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html