Use natural with your AI.
Connect your account once and let the AI you already use work with it, without building another integration. Exact Term Frequency-Inverse Document Frequency scores. Stop LLMs from guessing keyword relevance across massive corpuses.
Developed, maintained, and hosted by Vinkius.
MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED
Waiting for input…
Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.
Complete set · 1 capability
The complete natural capability set.
These are the exact actions your AI can choose when you ask it to work with natural.
01
1 capability in this set.
Part of 1 available through natural.
- 01
Calculate tf idf
Calculates the exact TF-IDF scores for an array of terms across an array of documents
Observed, not estimated
777ms average. Fast in production.
natural is checked daily against the live service.
- Fastest day
- 662ms
- Slowest day
- 956ms
- 14-day trend
- Slowing+10%
Connect your client
One URL. Every client.
Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.
Preview access · not provider authentication
The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of natural, so you can see the experience inside your AI.
It does not authenticate your account with natural. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
natural Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcpClaude Desktop
Follow the steps below to connect in seconds.
- 1In Claude Desktop, open Settings → Connectors.
- 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
- 3Click Add and start a new chat — natural capabilities are ready to use.
{
"mcpServers": {
"tf-idf-vectorizer-engine-mcp": {
"url": "https://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp"
}
}
}
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Step-by-step instructions for each client are in the guide. How to connect
FAQ
Questions natural owners ask.
- 01
Why is TF-IDF better than simple word counting?
Word counting overvalues common words like 'the' or 'and'. TF-IDF lowers the weight of words that appear in many documents, highlighting terms that are uniquely relevant to a specific text.
- 02
Can it process JSON document arrays?
Yes, just provide a stringified JSON array of text documents and a target array of terms. The engine handles the corpus building and tokenization.
- 03
Does it work in languages other than English?
Yes, TF-IDF relies on token frequency, making it highly effective for multi-language corpuses without needing specific translation logic.
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