# Keyword Proximity Checker MCP for AI Agents MCP

> The Keyword Proximity Checker analyzes written content by calculating the exact word distance between keywords. It helps SEO specialists assess if related terms are clustered naturally in a piece, indicating stronger topical relevance for search engines.

## Overview
- **Category:** seo
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_Yem3wGKJqrGOkrO4CNmGji2IGhxXgS91bKm1h0ln/mcp
- **Tags:** seo, keywords, proximity, text-analysis, tokenization

## Description

Do you struggle to tell if your keyword usage feels forced or natural? This MCP diagnoses semantic relevance within any text. By pinpointing the precise number of words separating key terms, it lets writers and SEO experts validate topical density. You can use it to check if pairs of keywords meet a minimum distance requirement or find dense groups of related terminology that signal authority to search engines. When you connect this tool via Vinkius, your AI client reads the text and instantly reports on how tightly clustered your key phrases are. This means you stop guessing about keyword placement; you start knowing exactly where to edit for maximum impact.

## Tools

### evaluate_proximity_status
Determines if specific pairs of keywords meet a set proximity threshold in your text.

### detect_keyword_clusters
Locates groups where multiple related keywords appear close together within the provided document.

### get_word_distance
Calculates the precise number of words separating two specified keywords in a given text sample.

## Prompt Examples

**Prompt:** 
```
How far apart are 'quantum computing' and 'data encryption' in this article?
```

**Response:** 
```
**Analysis Report: Word Distance**

*   **Keywords:** quantum computing, data encryption*
*   **Text Snippet Analyzed:** "...the shift toward advanced hardware requires robust security measures like data encryption when dealing with quantum computing applications."
*   **Distance Found:** 7 words.

*(Status: Acceptable. The gap is within the recommended threshold of 5-10 words.)*
```

**Prompt:** 
```
Check if 'content strategy' and 'user journey' are close enough in this draft?
```

**Response:** 
```
**Proximity Check Results**

| Keyword Pair | Actual Distance | Status (Max=3) |
| :---: | :---: | :---: |
| content strategy - user journey | 2 words | **Near** |
| keyword gap - SEO best practice | 8 words | Too Far |

The pair 'content strategy' and 'user journey' is perfectly placed, indicating strong thematic cohesion.
```

**Prompt:** 
```
Find any clusters for ['machine learning', 'deep neural network'] in this document.
```

**Response:** 
```
**Cluster Detection: Found 2 Clusters**

1.  **Start Index 4, End Index 7:** Contains ['advanced', 'machine learning', 'models', 'deployment']. This is a high-density cluster.
2.  **Start Index 12, End Index 15:** Contains ['training', 'deep neural network', 'parameters', 'optimization']. This confirms strong related topic grouping.
```

## Capabilities

### Measure Keyword Word Distance
Calculates the precise count of words separating two specific terms in a body of text.

### Validate Proximity Thresholds
Checks if designated keyword pairs maintain a distance within a predefined range, confirming optimal placement.

### Detect Keyword Groups (Clusters)
Identifies sections of text where multiple related keywords appear in close proximity.

## Use Cases

### Optimizing a Product Landing Page
A marketer needs to prove that 'AI software' and 'customer data' appear close together on a new product page. They ask their agent to use the MCP to check for pairs meeting a distance of 2 words, ensuring they hit critical SEO thresholds.

### Reviewing Technical Whitepapers
A technical writer submits a long document and needs confirmation that core concepts like 'API endpoints' and 'authentication protocol' are consistently grouped near each other. They use the tool to identify these key clusters.

### Improving Blog Topic Depth
An SEO editor takes an article draft and runs it through the MCP, asking it to find all keyword clusters for 'sustainability' and 'supply chain.' This instantly reveals sections needing more related terminology.

## Benefits

- Confirm natural keyword grouping. Instead of guessing, use the tool to find true clusters using `detect_keyword_clusters`.
- Guarantee optimal spacing between terms. The MCP lets you set rules and validate pairs instantly with `evaluate_proximity_status`.
- Pinpoint exact word gaps. Need to know if 'widget' and 'software' are too far apart? Use `get_word_distance` for a precise number.
- Write more authority-rich content. By validating proximity, you improve the perceived topical depth of your articles.
- Save manual editing time. You skip copy-pasting text into separate tools; the AI agent handles the analysis flow.

## How It Works

The bottom line is you stop manually counting words to validate if your content naturally groups related keywords together.

1. Provide your AI client with the text you want to analyze and the specific keywords you're checking.
2. The MCP processes the input, calculating word gaps and assessing how many of your defined pairs meet their required proximity rules.
3. You get a detailed report that highlights keyword clusters or flags any terms that are too far apart for optimal SEO.

## Frequently Asked Questions

**How does the Keyword Proximity Checker help me with SEO content?**
It proves that your keywords are grouped naturally. Instead of just mentioning terms, it calculates word gaps to show search engines and readers that your topic is deep, making your page more authoritative.

**What is topical density and why should I care about keyword proximity?**
Topical density means covering a subject from all angles. Keyword proximity checks validate this by ensuring related terms appear near each other, proving to search engines you're an expert on the topic.

**Can I use the Keyword Proximity Checker for non-English text?**
The tool operates based on word counting and spacing. While it works with many languages, ensure your AI client handles tokenization correctly to get accurate results.

**Does this MCP just count words or does it analyze meaning?**
It analyzes the physical placement of specific keywords by counting word gaps. It measures distance, not semantic meaning, but that proximity is what signals strong thematic grouping to search engines.

**If I use the Keyword Proximity Checker, will my content sound robotic?**
No. The goal isn't keyword stuffing; it’s validation. By ensuring natural clustering, you make your writing feel authoritative and naturally structured to a human reader.