# Twitter Mention Spam and Cluster Checker MCP for AI Agents MCP

> Twitter Mention Spam and Cluster Checker detects @-mention clustering and structural spam patterns to prevent shadowbans. It helps you keep your account in good standing by auditing tweet content for violations like excessive leading mentions or improper hashtag spacing before you hit post.

## Overview
- **Category:** security
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/undefined/mcp
- **Tags:** twitter, spam-detection, shadowban, analytics, social-media, safety

## Description

This MCP identifies @-mention clustering and structural spam patterns to keep your account in good standing. If you're running a high-volume account or a brand presence, a single tweet with too many mentions can trigger a shadowban or a total block. This tool gives your agent the eyes to see these risks before they happen. Instead of guessing if your content looks spammy to an algorithm, you can have your agent audit every draft. You can check if your mentions are too dense, see if you're starting tweets with a wall of tags, or verify that your hashtags aren't bumping into your mentions. It's about making sure your content actually reaches your audience instead of getting buried by a filter. By connecting this to your workflow via the Vinkius catalog, you get a safety net for your social media strategy. It handles the tedious math of ratio checking and pattern recognition so you don't have to worry about losing your reach overnight.

## Prompt Examples

**Prompt:** 
```
Is this tweet safe: '@user1 @user2 @user3 @user4 check this out!'
```

**Response:** 
```
The tweet is flagged. It starts with four consecutive mentions, which is a common pattern for spam. You should move the mentions to the middle or end of the post.
```

**Prompt:** 
```
Check the risk of this post: '#tech @startup @news #innovation'
```

**Response:** 
```
The mention-to-text ratio is safe, but the hashtag and mention spacing is tight. The distance ratio is 1. I recommend adding a space between '#innovation' and '@startup'.
```

**Prompt:** 
```
Are these mentions too clustered? '@a @b @c @d'
```

**Response:** 
```
Yes, this is a high-density cluster. You have 4 mentions in a very small character window. Consider spreading them out to reduce the risk of a shadowban.
```

## Capabilities

### Identify pattern violations
Detects structural issues like excessive leading mentions that trigger spam filters.

### Detect mention clusters
Locates high-density groups of mentions within a small character window.

### Assess shadowban risk
Provides a risk score based on your mention-to-text ratios.

### Verify hashtag spacing
Checks the distance between hashtags and mentions to ensure proper separation.

## Use Cases

### Campaign Risk Audit
A social media manager wants to know if a new campaign tweet is too heavy on mentions. They ask the agent to check the risk score.

### Shadowban Recovery Check
A brand owner is worried about a shadowban after a series of mentions. They have the agent audit their last 10 posts for clustering.

### Tagging Strategy Validation
A growth marketer wants to ensure their hashtag strategy isn't clashing with their mention strategy. They validate the spacing.

### Bot-Like Pattern Detection
A community manager checks if a series of quick responses starts with too many mentions, which could trigger a spam filter.

## Benefits

- Avoid shadowbans by catching spam-like patterns before you post.
- Stay under the radar by ensuring your mentions aren't too dense.
- Get clear risk metrics to balance your mention-to-text ratios.
- Maintain a clean look by keeping tags and mentions distinct.
- Protect your brand's reach by ensuring your content follows platform safety guidelines automatically.

## How It Works

The bottom line is it acts as a pre-flight check to keep your social media account safe from automated spam filters.

1. Provide your agent with a draft tweet or a list of planned posts.
2. The MCP analyzes the structure, density, and spacing of the content.
3. You get a risk score and specific feedback on which parts of the tweet might trigger spam filters.

## Frequently Asked Questions

**What is the Twitter Mention Spam and Cluster Checker?**
It's a tool that helps you check if your tweets look like spam to the platform. It looks for things like too many mentions at once or bad spacing that could get your account flagged.

**How does this help prevent shadowbans?**
It identifies specific patterns that the platform's filters look for, like excessive leading mentions. By catching these before you post, you stay in the platform's good graces.

**Can I use this for my brand account?**
Yes, it's perfect for brand accounts that need to maintain a high volume of mentions without getting blocked. It helps you stay safe while staying active.

**Does it check my hashtags too?**
Yes, it specifically looks at the spacing between your hashtags and your mentions. This ensures your post looks clean and follows the platform's best practices.

**Is this for every social media platform?**
This MCP is specifically tuned for Twitter (X) mention and hashtag structures. It's designed to handle the specific ways that platform flags spam.

**How do I know if my tweet is safe?**
The MCP provides a risk score based on your mention-to-text ratio. This gives you a concrete number to help you decide if a post is ready to go live.

**How can I check if my tweet is at risk of being flagged?**
You can use the `computeShadowbanScore` tool to analyze your text and see the calculated risk level. Tools available: `your_tool_name`.

**What defines a mention cluster?**
The `calculateClusteringRisk` tool flags any instance where three or more @-mentions appear within a 20-character window.

**Does it detect spam patterns at the start of a tweet?**
Yes, `evaluateTweetStructure` specifically checks if a post begins with four or more consecutive mentions.