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Razi Text Generation MCP Server

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Razi Text Generation is listed as active in the registry but did not answer our last check. It exposes 7 tools.

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

The linked repository no longer exists on GitHub — it was deleted or made private.

Uptime history 14 days of history · worst day 0%
14 days agonow
0.0%
Uptime 24h
0 of 91 checks
7
Tools
read from the server
71 ms
Response time
average over 24h
open, no key
Access
streamable-http

Razi Text Generation does not always answer

Over the last week it answered 0.0% of our checks. We check every 15 minutes, so you hear about the next outage within the hour — not from your users.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 13 min ago.

run in your terminal
claude mcp add razi-text --transport http https://www.razi.pro/api/mcp/text
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "razi-text": {
      "url": "https://www.razi.pro/api/mcp/text"
    }
  }
}
~/.codex/config.toml
[mcp_servers.razi-text]
url = "https://www.razi.pro/api/mcp/text"
.cursor/mcp.json
{
  "mcpServers": {
    "razi-text": {
      "url": "https://www.razi.pro/api/mcp/text"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "razi-text": {
      "url": "https://www.razi.pro/api/mcp/text"
    }
  }
}

Available tools 7

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

generate
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.
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.
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.
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.
compare
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.
draft
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.
humanize
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.

Endpoints

URLTransportStateLatencyChecked
https://www.razi.pro/api/mcp/text streamable-http answering 71 ms 13 min ago

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Razi Text Generation — questions

Answers built from our own checks of this server.

What can Razi Text Generation do?
It exposes 7 tools, read directly from the server on our last check. Among them: compare_text, draft_email, generate_blog_outline, generate_fake_data, generate_sql, generate_text and 1 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is Razi Text Generation working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 0 of 91 checks got a reply (0.0%), average response time 71 ms. The bar chart above shows every period we have measured.
Is Razi Text Generation still maintained?
The linked repository no longer exists on GitHub — it was deleted or made private. We show this because it changes what you can expect: an unmaintained server may keep answering for months and then stop without warning.
The registry lists Razi Text Generation as active — why does it not respond?
The official MCP registry stores what the author submitted; it does not verify that the server still runs. We check the endpoint ourselves, and this one does not answer. Catalogues that copy the registry without checking will show it as working.
How do I connect Razi Text Generation?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Razi Text Generation need an API key?
No. Razi Text Generation completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 7 of them are readable on this page. This is what we observed, not what the docs claim.