# Tool Output Entropy Sanitizer MCP for AI Agents MCP

> Tool Output Entropy Sanitizer prevents sensitive data like API keys, passwords, and encoded payloads from leaking into your AI's context window. It identifies high-entropy strings using character frequency analysis and replaces them with structured redaction patterns to keep your agent secure.

## Overview
- **Category:** security
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/undefined/mcp
- **Tags:** entropy, redaction, security, sanitization, privacy

## Description

Running an agent that pulls data from external APIs is a massive convenience, but it's also a security nightmare if those outputs contain secrets. If a tool returns a log file or a configuration string containing a private key, that secret is now part of your AI client's context window and potentially its history. This MCP stops that leak before it happens. It works by scanning text for the specific patterns of randomness that characterize sensitive credentials. Instead of just deleting the data, it replaces high-entropy segments with clear redaction markers, so your agent still understands the structure of the information without seeing the actual secret. You can find this part of the Vinkius catalog to add a layer of automated privacy to any workflow involving external data retrieval. It handles the heavy lifting of pattern detection so you don't have to manually scrub every single tool response for potential vulnerabilities.

## Tools

### evaluate_segment_entropy
Audits the randomness of a specific piece of text. This helps you identify if a string looks like a secret.

### sanitize_text_output
Scans text and replaces high-entropy segments with redaction markers. It keeps your data safe and structured.

### verify_window_bounds
Checks that your analysis window stays between 16 and 64 characters. This ensures the detection remains accurate.

## Prompt Examples

**Prompt:** 
```
Can you check this string for any sensitive information?
```

**Response:** 
```
I've analyzed the text provided. I found one high-entropy segment that looks like a credential:

* **Original:** `...auth_token=abc123xyz789!...` 
* **Status:** Redacted
* **Result:** `...auth_token=[REDACTED_HIGH_ENTROPY:15]...`
```

**Prompt:** 
```
Is this piece of text safe to send to my agent?
```

**Response:** 
```
The entropy score for the segment is 3.8, which is well below the threshold for a secret. It appears to be standard alphanumeric text and does not trigger any redaction rules.
```

**Prompt:** 
```
I want to set a scanning window of 10 characters. Is that okay?
```

**Response:** 
```
That size is too small for reliable detection. The configuration must be between **16 and 64 characters** to properly identify high-entropy patterns like API keys.
```

## Capabilities

### Scrub API keys from text
Automatically identifies and replaces high-entropy strings like tokens or passwords with redaction labels.

### Audit string randomness
Check specific segments of text to see if their character distribution suggests a hidden secret.

### Validate analysis parameters
Ensure your scanning window stays within the required 16 to 64 character range for accurate detection.

### Preserve data structure
Keep the context of your data intact by using structured placeholders instead of simple deletions.

## Use Cases

### Preventing API key exposure in logs
An agent pulls a system log containing an expired token. The MCP detects the high entropy and redacts it before the agent reads it.

### Securing environment variable parsing
You ask your agent to check a .env file. The tool identifies the sensitive values and replaces them with placeholders.

### Safe processing of encoded payloads
When an agent processes Base64 strings, the MCP flags segments that look like encoded secrets to prevent leakage.

### Automated privacy auditing
A developer uses the tool to scan large datasets for potential password patterns before feeding them into a model.

## Benefits

- Stop credential leaks by automatically redacting high-entropy strings before they reach your agent.
- Maintain context integrity using structured redaction patterns that don't break your data structure.
- Automate security audits with character frequency analysis that detects passwords and tokens.
- Reduce manual scrubbing tasks by letting the MCP handle all text sanitization in the background.
- Ensure accurate detection by validating that all scanning windows stay within operational bounds.

## How It Works

The bottom line is that your sensitive credentials stay out of your AI's memory while the context remains useful.

1. Connect your agent to the MCP and point it toward your incoming tool outputs.
2. The system scans the text segments for character frequency patterns that exceed a specific entropy threshold.
3. High-entropy strings are replaced with structured redaction markers, leaving you with safe, usable data.

## Frequently Asked Questions

**How does Tool Output Entropy Sanitizer prevent data leaks?**
It scans incoming text for high randomness, which is a hallmark of API keys and passwords. When it finds these patterns, it replaces them with a safe placeholder before your agent ever sees them.

**Will this MCP break the structure of my JSON data?**
No. Instead of deleting text, it uses structured redaction markers. This allows your agent to still understand the context and format of the data without seeing the actual secret.

**Can I use Tool Output Entropy Sanitizer with Claude or Cursor?**
Yes. It works with any MCP-compatible client, including Claude, Cursor, and Windsurf, providing a consistent security layer across your entire AI workflow.

**What kind of sensitive information does Tool Output Entropy Sanitizer detect?**
It is specifically tuned to find high-entropy strings like API keys, authentication tokens, passwords, and encoded payloads that are difficult to catch with simple keyword filters.

**Does Tool Output Entropy Sanitizer require manual setup for every tool?**
Once connected, it can be configured to automatically process the outputs of any tool your agent uses, providing hands-off protection for all incoming data streams.

**How does the sanitizer identify secrets?**
The server uses Shannon entropy calculation. If a window of text has an entropy score above 4.5, it is flagged as high-risk and redacted.

**What happens to the redacted text?**
The high-entropy segment is replaced with a redaction pattern in the format `[REDACTED_HIGH_ENTROPY:length]`, where length is the original character count.

**Can I check a specific string without redacting it?**
Yes, you can use the `evaluate_segment_entropy` tool to get the entropy score and high-entropy flag for a specific text segment without modifying the input.