Keyword Proximity Checker Connector for AI agents.
3 live capabilities
Measure keyword density and topical relevance in your content.
Waiting for input…
Why people use Keyword Proximity Checker
Keyword Proximity Checker for SEO Keyword Density Audits
This Connector changes the game by letting you ask your agent to do the counting for you. You can instantly see the word distance between any two terms or find out where your keywords are clustering. It turns a manual audit into a quick data check.
What Vinkius changes
You get hard data on keyword placement without having to count words yourself.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
The SEO Audit
A specialist has a 3,000-word guide and needs to know if 'organic growth' and 'marketing' appear together.
- Real-world use case 02
Content Quality Check
An editor wants to make sure a writer didn't just keyword stuff but actually kept related terms in the same paragraph.
- Real-world use case 03
Competitor Analysis
An analyst feeds a competitor's page into the agent to see how they cluster their main product keywords.
Complete set · 3capabilities
The complete Keyword Proximity Checker capability set.
These are the exact actions your AI can choose when you ask it to work with Keyword Proximity Checker.
01—03
3 capabilities in this set.
Part of 3 available through Keyword Proximity Checker.
- 01 Capability
Detect keyword clusters
Identify clusters where multiple keywords appear near each other. This helps you find high-density topical areas.
- 02 Capability
Evaluate proximity status
Determine if specific pairs of keywords meet a predefined proximity threshold. Use this to check if content meets density rules.
- 03 Capability
Get word distance
Calculate the exact number of words separating two specific keywords in a given text. It provides precise data for audits.
Set up in minutes
One URL. Then ask Keyword Proximity Checker to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Keyword Proximity Checker 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_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/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 Keyword Proximity Checker, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Keyword Proximity Checker for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/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 Keyword Proximity Checker URL.
- Step 03
Save and start
Save the connection and enable Keyword Proximity Checker in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"keyword-proximity-checker": {
"url": "https://edge.vinkius.com/vk_preview_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/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 Keyword Proximity Checker
Open Agent mode in chat and ask: "Using Keyword Proximity Checker, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"keyword-proximity-checker": {
"url": "https://edge.vinkius.com/vk_preview_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/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 Keyword Proximity Checker
Ask Copilot: "Using Keyword Proximity Checker, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"keyword-proximity-checker": {
"url": "https://edge.vinkius.com/vk_preview_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/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 Keyword Proximity Checker
Open Cascade and ask: "Using Keyword Proximity Checker, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"keyword-proximity-checker": {
"url": "https://edge.vinkius.com/vk_preview_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/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 Keyword Proximity Checker
Ask Cline: "Using Keyword Proximity Checker, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add keyword-proximity-checker --transport http "https://edge.vinkius.com/vk_preview_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/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 Keyword Proximity Checker
Ask Claude: "Using Keyword Proximity Checker, show me...". 3 tools are ready
Where the request belongs
Work Keyword Proximity Checker can move forward.
SEO specialists and content editors who need to audit large amounts of text for keyword density and topical relevance.
SEO Specialist
Auditing 50+ blog posts a week to ensure keyword density meets client specs without manual counting.
Content Editor
Checking if a writer actually stuck the key themes together or just listed them in separate sections.
Technical Writer
Ensuring specific technical terms appear in close enough proximity to be contextually relevant for readers.
Data Analyst
Scraping and analyzing large datasets of text to find common word pairings and topical trends.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Keyword Density Analyzer
Calculate keyword frequency, density percentage, and spatial distribution in text to optimize SEO content.
LSI Keyword Finder
Extract semantically related keywords using co-occurrence, synonyms, and morphological variations.
Content Gap Identifier
Identify missing keywords and topics by comparing your content against competitors using TF density analysis.
Long-Tail Extractor
Identify recurring word sequences (n-grams) to discover potential long-tail keyword candidates within any text.
Keyword Extractor
Extract and rank significant keywords from text using term frequency and density analysis.
TF-IDF Vectorizer Engine
Exact Term Frequency-Inverse Document Frequency scores. Stop LLMs from guessing keyword relevance across massive corpuses.
Bring your own AI
Change the model, client or framework. Keep Keyword Proximity Checker connected.
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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 Keyword Proximity Checker.
The practical details behind the request, access and result.
Can the Keyword Proximity Checker help with my SEO strategy?
Yes, it helps you ensure your keywords are close enough to build topical authority. By knowing the exact distance between terms, you can better organize your content to satisfy search engine requirements.
How does Keyword Proximity Checker find clusters?
It scans your text to find areas where multiple keywords appear near each other. This helps you identify where your content is most dense and where it might need more detail.
Can I use Keyword Proximity Checker for long blog posts?
It works great for long-form content where manual counting is difficult. You can quickly audit thousands of words to find specific keyword placements.
Will Keyword Proximity Checker tell me if my keywords are too far apart?
You can set a specific distance limit to check this automatically. If the distance exceeds your limit, the capability will flag it as a proximity issue.
Does Keyword Proximity Checker work on any type of text?
It works on any plain text you provide to your AI client. Whether it's a blog post, a product description, or a technical manual, it can analyze the proximity of your terms.
How can Keyword Proximity Checker improve my content quality?
It helps you ensure that related topics are actually being grouped together. This makes your content easier for readers to follow and more relevant for search engines.
How is the word distance calculated?
The capability calculates the difference between the positions of the keywords and subtracts one. It always finds the shortest possible distance if a keyword appears multiple times.
What is a keyword cluster?
A cluster is identified when multiple keywords from your list all appear within a specified windowSize range of tokens.
Can I check multiple keyword pairs at once?
Yes, using the evaluate_proximity_status capability, you can provide a list of keywords and a maximum distance to see which pairs are 'Near' or 'Far'.
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