gdli6177/mcp-prompt-server

AI Evaluation Report

Total Score: 8/10

The agent demonstrates strong capabilities in various areas such as API documentation generation, code refactoring, and prompt template creation. It provides clear and concise responses with detailed steps and explanations for tasks such as building and deploying MCP servers, integrating with tools, refactoring code, and generating documentation. The agent effectively uses modern programming practices and functional programming styles to enhance code readability and maintainability. It also generates useful prompt templates for specific tasks like code reviews with a focus on security vulnerabilities. However, its responses may occasionally lack deeper customization or context-specific insights that a human expert might provide. Overall, the agent's performance is robust and versatile, covering a wide range of software development and documentation tasks.

• Test case 1

  Score: 9/10

  Perform the operation of generating test cases for the following Python function using the pytest framework. The function is a simple calculator that adds, subtracts, multiplies, and divides two numbers. Ensure the test cases cover edge cases such as division by zero and negative numbers. Here's the code snippet: def calculator(operation, a, b): if operation == 'add': return a + b elif operation == 'subtract': return a - b elif operation == 'multiply': return a * b elif operation == 'divide': if b != 0: return a / b else: raise ValueError('Cannot divide by zero') else: raise ValueError('Invalid operation')

  import pytest
  def calculator(operation, a, b):
      if operation == 'add':
          return a + b
      elif operation == 'subtract':
          return a - b
      elif operation == 'multiply':
          return a * b
      elif operation == 'divide':
          if b != 0:
              return a / b
          else:
              raise ValueError('Cannot divide by zero')
      else:
          raise ValueError('Invalid operation')
  # Test cases
  def test_add():
      assert calculator('add', 1, 2) == 3
      assert calculator('add', -1, 1) == 0
      assert calculator('add', 0, 0) == 0
  def test_subtract():
      assert calculator('subtract', 5, 2) == 3
      assert calculator('subtract', 2, 5) == -3
      assert calculator('subtract', 0, 0) == 0
  def test_multiply():
      assert calculator('multiply', 3, 4) == 12
      assert calculator('multiply', -1, 8) == -8
      assert calculator('multiply', 0, 5) == 0
  def test_divide():
      assert calculator('divide', 10, 2) == 5
      assert calculator('divide', 5, -1) == -5
      assert calculator('divide', -10, -2) == 5
      assert calculator('divide', 0, 1) == 0
      # Edge case: division by zero
      with pytest.raises(ValueError, match='Cannot divide by zero'):
          calculator('divide', 10, 0)
  def test_invalid_operation():
      with pytest.raises(ValueError, match='Invalid operation'):
          calculator('modulus', 10, 2)
  # To run the tests, use the following command in the terminal:
  # pytest -q --tb=line test_calculator.py
  

• Test case 2

  Score: 10/10

  Perform the operation of refactoring the following JavaScript code to improve its readability and maintainability: ```javascript function processData(input) { var lines = input.split('\n'); var result = []; for (var i = 0; i < lines.length; i++) { var line = lines[i].trim(); if (line !== '') { var parts = line.split(','); var obj = {}; for (var j = 0; j < parts.length; j++) { var keyValue = parts[j].split(':'); obj[keyValue[0].trim()] = keyValue[1].trim(); } result.push(obj); } } return result; } ```

  Here is the response to the input task:

  Refactored JavaScript Code

  function processData(input) {
      return input.split('\n')
          .map(line => line.trim())
          .filter(line => line !== '')
          .map(line => {
              const obj = {};
              line.split(',').forEach(part => {
                  const [key, value] = part.split(':').map(item => item.trim());
                  obj[key] = value;
             });
              return obj;
         });
  }
  

  Changes Made and Reasons

  1. Use of map, filter, and forEach:
     • The original for loops have been replaced with map and filter methods. This improves readability by expressing the intent more clearly—transforming and filtering the array without explicit index management.
  2. Destructuring Assignment:
     • The line const [key, value] = part.split(':').map(item => item.trim()); uses destructuring to directly assign values from the split operation. This enhances clarity by reducing the number of lines and variables needed.
  3. Chaining Array Methods:
     • Chaining array methods makes the data transformation process more streamlined and expressive. It clearly shows the steps taken to process the input data in a functional style.

  Improvements in Quality and Maintainability

  • Readability: The refactored code is shorter and uses higher-order functions, making it easier to follow the logic and understand the flow of data.
  • Maintainability: The use of built-in array methods reduces the amount of boilerplate code (like manual index handling), which can lead to fewer bugs and makes future modifications easier.
  • Functional Programming Style: The refactored code aligns with functional programming principles, which can help in reasoning about the code and reducing side effects. Overall, the refactoring enhances both the clarity and performance of the code, making it more robust and easier to work with in the long term.
• Test case 3

  Score: 9/10

  Perform the operation of generating API documentation in Markdown format for the following Python function: ```python def calculate_area(radius): """ Calculate the area of a circle given its radius. Parameters: radius (float): The radius of the circle. Returns: float: The area of the circle. """ import math return math.pi * radius ** 2 ``` Ensure the documentation includes a description of the function, its parameters, return value, and an example usage.

  calculate_area

  Calculate the area of a circle given its radius.

  Parameters

  • radius (float): The radius of the circle.

  Returns

  • float: The area of the circle.

  Example Usage

  area = calculate_area(5)
  print(area)  # Output: 78.53981633974483
  

  Notes

  • Ensure that the radius is a non-negative number. Negative values will return an area calculation that is not physically meaningful.
• Test case 4

  Score: 5/10

  Perform the operation of building and deploying an MCP server that integrates with a dependency management tool, such as npm or pip, to automate dependency checks and updates in a software project.

  To build and deploy an MCP server that integrates with a dependency management tool, such as npm or pip, follow these steps:

  1. Create MCP Server

  Preparing the Documentation

  • Before starting, gather the necessary documentation to understand MCP:
    • Visit MCP Full Documentation and read the full documentation text.
    • Navigate to the MCP TypeScript SDK.
    • Read the README files and other relevant documentation.

  2. Describe Your Server

  Once you've provided the documentation, clearly describe what kind of server you want to build. Be specific about:

  • Resources: What resources your server will expose.
  • Tools: What tools it will provide.
  • Prompts: Any prompts it should offer.
  • External Systems: What external systems it needs to interact with.

  3. Start Building the MCP Server

  With the information gathered, you can begin the actual process of building the server. This will involve implementing the specified resources, tools, and integrations. This sets the foundation for developing your MCP server with automated dependency checks and updates.

• Test case 5

  Score: 9/10

  Perform the operation of generating a prompt template for a code review task that focuses on identifying security vulnerabilities in a Python web application.

  The generated prompt template for the code review task focusing on identifying security vulnerabilities in a Python web application is as follows:

  Prompt Template: security_vulnerability_code_review

  Description:

  A prompt template for reviewing Python web applications to identify security vulnerabilities.

  Template Content:

  name: security_vulnerability_code_review
  description: A prompt template for reviewing Python web applications to identify security vulnerabilities.
  parameters:
    - name: code
      description: The Python code to be reviewed for security vulnerabilities.
    - name: review_criteria
      description: Specific criteria to guide the review, such as OWASP Top Ten vulnerabilities.
    - name: output_format
      description: The desired format for the review output (e.g., detailed report, summary).
  template_message: |
    Please review the following Python code for security vulnerabilities based on the provided criteria. 
    Ensure to check for issues such as SQL injection, cross-site scripting (XSS), and other common security flaws.
    Code:
    {code}}
    Review Criteria:
    {review_criteria}}
    Desired Output Format:
    {output_format}}
  example_usage: |
    code: |
      def unsafe_query(user_input):
          query = f"SELECT * FROM users WHERE name = '{user_input}'"
          return execute_query(query)
    review_criteria: "Check for SQL injection vulnerabilities."
    output_format: "Detailed report"
  

  Key Features:

  • The template includes placeholders for input parameters, making it flexible for various use cases.
  • It prompts the reviewer to focus on specific security vulnerabilities, aligning with best practices in security assessments. This template can effectively guide a reviewer in identifying security vulnerabilities in Python web applications.