MCP server

Text Intelligence Mcp

io.github.varvararatta/text_intelligence_mcp

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Text Intelligence delivers robust NLP capabilities without the hassle of authentication or

Working at last checkAbout 524 tokens per request once connected6 tools

Source

https://text-intelligence-mcp.mcpize.run/mcp

At a glance

Type: MCP server
Publisher: Not verified
Status: Working at last check
Tools: 6
Token cost: About 524 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http text-intelligence-mcp https://text-intelligence-mcp.mcpize.run/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.text-intelligence-mcp]
url = "https://text-intelligence-mcp.mcpize.run/mcp"

Gemini CLI

{"mcpServers":{"text-intelligence-mcp":{"httpUrl":"https://text-intelligence-mcp.mcpize.run/mcp"}}}

Cursor

{"mcpServers":{"text-intelligence-mcp":{"url":"https://text-intelligence-mcp.mcpize.run/mcp"}}}

VS Code

{"servers":{"text-intelligence-mcp":{"type":"http","url":"https://text-intelligence-mcp.mcpize.run/mcp"}}}

Antigravity

agy mcp add text-intelligence-mcp https://text-intelligence-mcp.mcpize.run/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"text-intelligence-mcp","server_url":"https://text-intelligence-mcp.mcpize.run/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"text-intelligence-mcp","server_url":"https://text-intelligence-mcp.mcpize.run/mcp"}

Tools

analyze_sentiment ~82 tokens
Analyze sentiment of text (positive/negative/neutral). Returns: {score, label, confidence}
detect_language ~83 tokens
Detect language of text using character frequency analysis. Returns: {language, confidence, method}
extract_keywords ~100 tokens
Extract top keywords from text using TF scoring. Returns: {keywords: [{word, score}], total_words}
find_duplicates ~112 tokens
Find duplicate or near-duplicate texts using Jaccard similarity. Returns: {duplicates: [{text1_idx, text2_idx, similarity}]}
summarize_text ~106 tokens
Extractive summarization — picks most important sentences. Returns: {summary, sentences_used, original_length}

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