Razi Text Generation MCP server

• compare_textFree

  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.

• draft_emailwrite actionFree

  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.

• generate_blog_outlineFree

  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.

• generate_fake_dataFree

  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.

• generate_sqlFree

  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.

• generate_textFree

  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.

• humanize_textFree

  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.

• –Code scanremote-only server, no package to scann/a
• Live reliabilityremote reachable in 289ms20/20
• Tool poisoning7 tool descriptions checked15/15
• Auth qualityAPI key sent as a header8/15
• Maintenancerepository not readable: repo not found3/15
• Maintainer identityno repository or website to verify2/10

Safety score

64/100

higher than 14% of 802 Databases servers

Tools

7

the typical Databases server has 8

Tools that change things

14%

typical: 10%. Fewer write tools means less an agent can break

Uptime

100%

of our recent checks; the typical remote Databases server answers 100%

Response time

158 ms

faster than 67% of 292 Databases servers

Razi Text Generation: common questions

Is Razi Text Generation MCP server safe?

With care: it is graded C, so read the findings first (64/100). Read the Razi Text Generation safety report

How do I install Razi Text Generation?

It runs remotely at www.razi.pro. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.

Does Razi Text Generation need an API key?

Yes. The registry entry asks for Authorization.

Is Razi Text Generation maintained?

The latest release is v1.0.0.

Is Razi Text Generation up?

100% of our last 28 checks got an answer. We check remote servers about four times a day.

What can I use instead of Razi Text Generation?

Servers from other publishers that do the same job: StackQL MCP Server, Magic Cloud MCP server and IBM Db2 for i MCP server.

How does Razi Text Generation compare with other Databases MCP servers?

safety score 64/100 (higher than 14% of 802 Databases servers); tools 7 (the typical Databases server has 8); tools that change things 14% (typical: 10%. Fewer write tools means less an agent can break). Full comparison.