# Code Block Token Analyzer MCP for AI Agents AI Agent Connect

> Code Block Token Analyzer calculates token density and code-to-text ratios in documents. It uses specific heuristics to estimate token counts for code blocks versus standard text, helping you manage context window limits and optimize how much information your agent processes when dealing with heavy programming files.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_61f6ADAlqbkiYiLN3Wz2ZyPTCqypN7kItzL4JuPb/ai-agent-connect
- **Tags:** tokens, code-analysis, density, llm-optimization, text-processing

## Description

Managing context windows is a constant battle when you're working with large codebases or long technical documents. This MCP gives your AI client a way to look under the hood of a text file to see exactly how much space the code is taking up compared to the surrounding explanation. Instead of guessing how many tokens a file will consume, you get a clear breakdown of density. It uses a specific logic where code tokens are estimated at three characters each, while regular text is estimated at four. This distinction is vital because code is often more dense and can eat through your context window faster than you'd expect. By connecting this to your workflow through the Vinkius catalog, you can quickly check if a file is too code-heavy or if the formatting is broken before you even send it to your agent. It's a simple way to ensure your prompts stay efficient and your context stays clean.

## Tools

### validate_code_block_format
Checks if your text contains valid triple-backtick code blocks. It ensures your agent can actually find and read the code you've provided.

### analyze_text_density
Provides a full breakdown of code blocks, character counts, and token ratios. It's the deep dive for understanding text composition.

### get_token_metrics_summary
Returns a high-level summary of token distribution. Use this for a quick check on whether a file is code-heavy.

## Prompt Examples

**Prompt:** 
```
How much context will this file take up?
```

**Response:** 
```
Based on the text provided, here is your token breakdown:

| Metric | Value |
| :--- | :--- |
| **Total Tokens** | 142 |
| **Code Tokens** | 98 |
| **Text Tokens** | 44 |
| **Code Ratio** | 69% |

This file is considered **code-heavy**. You might want to strip unnecessary comments if you are nearing your limit.
```

**Prompt:** 
```
Is the code in this document formatted correctly for my agent?
```

**Response:** 
```
I checked the formatting for you. 

* **Status:** Valid
* **Code Blocks Found:** 3
* **Format:** Triple-backtick (```) detected.

Your code blocks are properly structured and ready for processing.
```

**Prompt:** 
```
Give me a quick summary of the token usage for this snippet.
```

**Response:** 
```
Here is the summary for your snippet:

* **Total Tokens:** 12
* **Code Ratio:** 1.0 (100% code)
* **Status:** Extremely code-heavy.
```

## Capabilities

### Measure code density
Get a precise breakdown of how much of your text is actual code versus explanatory prose.

### Estimate token usage
Calculate expected token counts using specific character-to-token heuristics for code and text.

### Verify block formatting
Check if your text contains properly formatted triple-backtick code blocks that an agent can read.

### Summarize token metrics
Get a high-level overview of token distribution and code heaviness in a single glance.

## Use Cases

### Managing massive refactoring tasks
A developer needs to refactor a 500-line file and uses the MCP to see if the code-to-text ratio will blow out their context window.

### Validating technical documentation
A writer wants to make sure their Markdown files have valid triple-backtick blocks so their agent doesn't ignore the code snippets.

### Optimizing prompt efficiency
A prompt engineer uses the density summary to decide if they should strip out comments to save tokens before sending a request.

### Debugging agent parsing issues
When an agent fails to recognize code, the user checks the block format to see if the backticks are actually there.

## Benefits

- Stop guessing context limits by getting exact token estimates for code-heavy files.
- Prevent agent errors by using validate_code_block_format to ensure all code is extractable.
- Optimize prompt costs by using get_token_metrics_summary to identify code-heavy files.
- Get a granular view of text composition with analyze_text_density for better prompt engineering.
- Ensure your technical documentation is properly formatted for seamless agent processing.

## How It Works

The bottom line is you get a mathematical breakdown of how much context your code-heavy files will actually consume.

1. Connect the MCP to your preferred AI client via Vinkius.
2. Provide the text or document you want to analyze to your agent.
3. Receive a detailed breakdown of token counts, ratios, and formatting status.

## Frequently Asked Questions

**How does the Code Block Token Analyzer calculate tokens?**
It uses specific heuristics where code tokens are estimated at 3 characters each and non-code text is estimated at 4 characters each. This provides a more accurate representation of how code consumes context compared to standard text.

**Can I use Code Block Token Analyzer to fix broken code blocks?**
It won't rewrite your code, but it will tell you if your triple-backtick blocks are missing or malformed so you can fix them before your agent tries to read them.

**Will the Code Block Token Analyzer help me save money on API costs?**
Yes. By identifying code-heavy files and providing exact token counts, you can more effectively prune unnecessary text and stay within efficient usage limits.

**Is the Code Block Token Analyzer compatible with Cursor?**
Yes, this MCP works with any MCP-compatible client, including Cursor, Claude, and Windsurf.

**Does the Code Block Token Analyzer work for plain text files?**
It works for any text, but its primary value is in analyzing files that contain code blocks. For plain text, it will simply provide the standard text token estimates.