natural Connector for AI agents.
1 live capability
Get exact phrase counts and linguistic data from massive documents.
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Why people use natural
N-Gram Frequency Engine for Precise Linguistic Analysis
The N-Gram Frequency Engine replaces that guesswork with native V8 JavaScript. It scans the text, counts the phrases, and hands your agent a perfect list of results. You get actual data instead of a vibe.
What Vinkius changes
You get perfect math instead of AI approximations.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
SEO Keyword Mapping
A marketing lead has a 50-page whitepaper and needs to know the top 10 bigrams.
- Real-world use case 02
Linguistic Research
A student wants to find recurring 3-word phrases in a classic novel to identify themes.
- Real-world use case 03
Customer Review Analysis
A product manager wants to see the most common 2-word complaints from 1,000 reviews.
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
Extract ngram frequencies
This capability finds the most frequent N-Grams from a block of text. It returns exact counts for bigrams, trigrams, or any custom length you specify.
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_9T1etKMahiW8NnGArHZFRlQgidDXjNxOPOUMepVx/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_9T1etKMahiW8NnGArHZFRlQgidDXjNxOPOUMepVx/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": {
"n-gram-frequency-engine": {
"url": "https://edge.vinkius.com/vk_preview_9T1etKMahiW8NnGArHZFRlQgidDXjNxOPOUMepVx/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": {
"n-gram-frequency-engine": {
"url": "https://edge.vinkius.com/vk_preview_9T1etKMahiW8NnGArHZFRlQgidDXjNxOPOUMepVx/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": {
"n-gram-frequency-engine": {
"url": "https://edge.vinkius.com/vk_preview_9T1etKMahiW8NnGArHZFRlQgidDXjNxOPOUMepVx/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": {
"n-gram-frequency-engine": {
"url": "https://edge.vinkius.com/vk_preview_9T1etKMahiW8NnGArHZFRlQgidDXjNxOPOUMepVx/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 n-gram-frequency-engine --transport http "https://edge.vinkius.com/vk_preview_9T1etKMahiW8NnGArHZFRlQgidDXjNxOPOUMepVx/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 scientists and SEO specialists who need to know exactly how often specific phrases appear in massive bodies of text without manual counting.
SEO Analyst
Mapping keyword density across long-form whitepapers and blog posts.
Linguistic Researcher
Identifying recurring motifs and patterns in large literary datasets.
Data Scientist
Preprocessing massive text dumps for NLP model training.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsString Metrics Analyzer
Bypass LLM tokenization blindness. Get exact character counts, word counts, and specific string occurrences deterministically.
TF-IDF Vectorizer Engine
Exact Term Frequency-Inverse Document Frequency scores. Stop LLMs from guessing keyword relevance across massive corpuses.
Deterministic 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.
Text Readability Scorer
Calculate mathematically accurate readability metrics (Flesch-Kincaid, Gunning Fog, SMOG) for any text. Stop relying on AI 'feelings'. get exact US grade levels for SEO and compliance.
Long-Tail Extractor
Identify recurring word sequences (n-grams) to discover potential long-tail keyword candidates within any text.
Context Redundancy Deduplicator
Identify and quantify exact N-gram overlaps across RAG documents to optimize context window usage.
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.
Can the N-Gram Frequency Engine count phrases in very long books?
Yes, it can. It processes the text directly in native JavaScript, so it doesn't get bogged down by the length of the document like a standard AI chat might.
How accurate are the counts provided by this Connector?
They are 100% accurate. The capability performs deterministic counting, meaning it provides the exact mathematical count every time you run it.
Does it work for bigrams and trigrams?
Yes, it handles bigrams, trigrams, and even custom N-Grams of any length you specify during your analysis.
Why should I use this instead of just asking my AI?
Standard AI models often 'guess' frequencies based on their training data or what they remember from the context window. This Connector gives you the hard numbers you need for real research.
Can I use the N-Gram Frequency Engine for SEO research?
Absolutely. It's a perfect capability for mapping keyword density and identifying common phrase patterns in long-form content or large datasets.
Is the N-Gram Frequency Engine fast enough for large files?
Yes, it's designed for speed. It can process and count phrases in massive documents in just milliseconds.
What are Bigrams and Trigrams?
A bigram is a sequence of two adjacent words (e.g., 'machine learning'). A trigram is three (e.g., 'natural language processing').
Does it lowercase the text automatically?
Yes, all text is automatically lowercased and tokenized natively to ensure accurate aggregation of phrases.
Is this faster than asking Claude?
Significantly faster and 100% accurate. LLMs cannot count occurrences across thousands of tokens reliably.
One connection away
Give your agent a direct line to natural.
Connect natural once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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