natural Connector for AI agents.
1 live capability
Get mathematically precise keyword relevance for large-scale text analysis.
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Why people use natural
TF-IDF Vectorizer Engine for Accurate Keyword Extraction
With this Connector, you stop guessing. You can feed your agent a massive list of documents and have it calculate the exact mathematical weight of your keywords instantly. You get a sorted list of results based on actual data, not just what the AI thinks sounds good.
What Vinkius changes
You get mathematically perfect keyword relevance scores instead of AI-generated guesses.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Ranking Support Tickets
A lead has 5,000 tickets and needs to find the ones specifically about latency.
- Real-world use case 02
SEO Content Audit
A specialist wants to know which of 100 articles truly focus on sustainable farming.
- Real-world use case 03
Research Paper Sorting
A researcher has a folder of 1,000 PDFs and needs to find the most relevant ones for quantum cryptography.
Complete set · 1capability
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 Capability
Calculate tf idf
Calculates the exact TF-IDF scores for an array of terms across an array of documents. This provides an objective way to rank content based on true mathematical relevance.
Set up in minutes
One URL. Then ask natural to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use natural from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it natural, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable natural for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the natural URL.
- Step 03
Save and start
Save the connection and enable natural in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"tf-idf-vectorizer-engine": {
"url": "https://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using natural
Open Agent mode in chat and ask: "Using natural, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"tf-idf-vectorizer-engine": {
"url": "https://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using natural
Ask Copilot: "Using natural, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"tf-idf-vectorizer-engine": {
"url": "https://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using natural
Open Cascade and ask: "Using natural, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"tf-idf-vectorizer-engine": {
"url": "https://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using natural
Ask Cline: "Using natural, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add tf-idf-vectorizer-engine --transport http "https://edge.vinkius.com/vk_preview_CzteX5B7baA5kKcgQISGvHapYHeKBksKMIECPMUI/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using natural
Ask Claude: "Using natural, show me...". 1 tools are ready
Where the request belongs
Work natural can move forward.
This is for data professionals who need to sort through massive amounts of text where accuracy is a requirement. It's for anyone tired of AI 'hallucinating' which documents are most important.
Data Scientist
Sorting through large-scale text corpora to find research papers with specific technical overlap.
NLP Engineer
Building search components or retrieval systems that require exact scoring instead of fuzzy matching.
SEO Analyst
Auditing hundreds of blog posts to find which ones truly rank for specific long-tail keywords.
Content Strategist
Filtering thousands of customer reviews to find high-signal feedback about specific product features.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsDeterministic Text Summarizer & Extractor
Equip your AI with pure Term Frequency (TF) text analysis. Deterministically extract keywords, bigrams, and generate extractive summaries without external API calls.
MonkeyLearn
Automate text analysis via MonkeyLearn. classify sentiment, extract keywords, and run custom NLP pipelines directly from any AI agent.
MeaningCloud
Advanced text analytics for sentiment analysis, topic extraction, language detection, and automatic summarization.
Keyword Extractor
Extract and rank significant keywords from text using term frequency and density analysis.
LSI Keyword Finder
Extract semantically related keywords using co-occurrence, synonyms, and morphological variations.
Long-Tail Extractor
Identify recurring word sequences (n-grams) to discover potential long-tail keyword candidates within any text.
Bring your own AI
Change the model, client or framework. Keep natural connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about natural.
The practical details behind the request, access and result.
How does the TF-IDF Vectorizer Engine help with large datasets?
It provides a way to mathematically rank thousands of documents at once. Instead of the AI guessing, it uses a deterministic formula to find the most relevant content for you.
Can I use the TF-IDF Vectorizer Engine for SEO analysis?
Yes, it's great for identifying which pieces of content actually focus on your target keywords. It gives you an objective score for every page in your site.
Why use this instead of just asking my AI client to find keywords?
Standard AI clients can hallucinate or ignore common words. This Connector uses exact math to ensure the results are consistent and based on true frequency data.
What kind of documents can the TF-IDF Vectorizer Engine process?
It can process any text-based data, including support tickets, research papers, customer reviews, and blog posts, as long as they are provided as an array of strings.
Is the TF-IDF Vectorizer Engine accurate for research?
Yes, it is highly accurate because it uses a deterministic mathematical model. It's designed specifically for situations where you need objective, reproducible results.
How does the TF-IDF Vectorizer Engine handle multiple keywords?
You can provide a list of terms, and the engine will calculate the scores for all of them across your documents, helping you find the most multi-faceted matches.
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.
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.
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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