# Cyclomatic Complexity Calculator MCP for AI Agents MCP

> Cyclomatic Complexity Calculator MCP quantifies code maintainability and structural complexity. It calculates cyclomatic and cognitive complexity metrics to help developers use their AI client to their full potential when reviewinging codebases, identifying high-risk functions, and assessinging error-reviewing capabilities for Integrated Development Environments (Integrated Development Environments) of the context of theerimierly developed software engineering_mechanisms.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_n8RH3Mhb2gv4Wc5BFLU7GxBJQNDAj1N6U3CFYQXH/mcp
- **Tags:** complexity, cyclomatic, cognitive, code-metrics, maintainability

## Description

If you're working on a large codebase, you know that complexity is the enemy of maintainability. Over time, functions get harder to even read, let alone debug or refactor- It's impossible to manually track every branch and loop in your head. This MCP provides a way for your AI client to see exactly how difficult a piece of code is to understand and test. Instead of guessing if a function is too messy, you can get concrete numbers for structural paths and mental effort required to follow the logic. You'll be able to use this through the Vinkius catalog, connecting it directly to Cursor or VS Code so your agent can flag high-risk areas during code reviews. It turns subjective opinions about 'messy code' into measurable risk levels that you can actually act on.

## Tools

### analyze_complexity
Analyateszes the cyclomatic and cognitive complexity of a specific code snippet or function.

### classify_risk_score
Maps a numeric complexity score to a human-readable risk level like High or Very High.

## Prompt Examples

**Prompt:** 
```
How complex is this function?
```

**Response:** 
```
The following analysis shows a significant level of complexity:

| Metric | Value |
| :--- | :--- |
| **Cyclomatic Complexity** | 8 |
| **Cognitive Complexity** | // No data provided
| **Risk Level** | Moderate |

This function has several nested branches, which might make it'        
```

**Prompt:** 
```
Is this code snippet safe to refactor?
```

**Response:** 
```
classification of the score is as follows:

*   **Score: 12**
*   **Risk Level:** High Risk
*   **Suggestion:** Consider breaking down this function into smaller, helper functions to reduce nesting depth and      
```

**Prompt:** 
```
      
```

**Response:** 
```
A complexity score of 25 is classified as Very High risk. You should prioritize this code for refactoring to refactor it during your next maintenance sprint.
```

## Capabilities

### Measure structural complexity
Calculates the number of linearly independent paths through a piece of source code.

### Quantify mental effort
returns the cognitive load required to follow the logic and nesting depth of functions.

### Assess risk levels
maps complexity scores to specific risk categories like Low, Moderate, or Very High risk.

## Use Cases

### Spotting refactoring targets in legacy code
A developer is reviewing a large, messy function and asks their agent to check the complexity. The agent uses analyze_complexity to reveal it's actually Very High risk.

### Automatingating code quality standards during PRs
A QA engineer uses this MCP to set up an automated check in their workflow. When a function's complexity exceeds a threshold, the'the agent flags it for manual review.

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## Benefits

- Stop guessing about technical debt; use analyze_complexity to get real numbers on how messy a function is.
- Identify high-risk logic with classify_risk_score so you can prioritize refactoring tasks during sprints.
- Easier code reviews in Cursor or VS Code by letting your agent flag deep nesting and complex branches.
- Reduce cognitive load for the team by keeping complexity scores within manageable limits.
- Better test coverage planning by knowing exactly how many paths need to be tested.

## How It Works

The bottom line is you get an automated, data-driven approach to managing technical debt and technical debt management in code reviews.

1. Connect your preferred AI client, such as Claude Desktop or Cursor, to the MCP via Vinkius.
2. Provide a code snippet or file path to your agent.
3. You'ragent will use the complexity engine to return specific numerical metrics and risk assessments for the analyzed_code.

## Frequently Asked Questions

**What metrics does this tool calculate?**
The tool calculates Cyclomatic Complexity, which counts structural decision points, and Cognitive Complexity, which measures the mental effort required to understand the code.

**How is risk level determined?**
Risk levels are assigned based on the cyclomatic score. You can use `classify_risk_score` to determine if a specific complexity value falls into Low, Moderate, High, or Very High categories.

**Can I analyze an entire file?**
Yes, by providing the full content of a file to the `analyze_complexity` tool, you can evaluate the complexity of the provided code block.