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KunihiroS

claude-code-mcp

test_code

Automatically generates test cases for provided code, supporting custom test frameworks to ensure robust functionality and quality assurance.

Instructions

Generates tests for the given code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesCode to test
test_frameworkNoTest framework to use

Implementation Reference

  • Handler implementation for the 'test_code' tool. It destructures the input arguments, encodes the code to base64, constructs a prompt for generating tests using Claude CLI, executes it, and returns the generated test code as text content.
    case 'test_code': {
      const { code, test_framework } = args;
      logger.debug(`Processing test_code request, code length: ${code.length}`);
      const encodedCode = encodeText(truncateIfNeeded(code));
      logger.debug(`Code encoded to base64, length: ${encodedCode.length}`);
      const framework = test_framework || 'default';
      const prompt = `You are super professional engineer. Please generate tests for the following Base64 encoded code.\n\nCode:\n${encodedCode}\n\nTest framework (if specified):\n${framework || 'No specific framework provided. Please use a suitable default framework.'}`;
      logger.debug('Calling Claude CLI with prompt');
      const output = await runClaudeCommand(['--print'], prompt);
      logger.debug(`Received response from Claude, length: ${output.length}`);
      return { content: [{ type: 'text', text: output }] };
    }
  • Registration of the 'test_code' tool in the ListTools response, including its name, description, and input schema definition.
    {
      name: 'test_code',
      description: 'Generates tests for the given code.',
      inputSchema: {
        type: 'object',
        properties: {
          code: { type: 'string', description: 'Code to test' },
          test_framework: { type: 'string', description: 'Test framework to use', default: '' }
        },
        required: ['code']
      }
    },
  • Input schema definition for the 'test_code' tool, specifying the expected parameters: code (required string) and optional test_framework.
      inputSchema: {
        type: 'object',
        properties: {
          code: { type: 'string', description: 'Code to test' },
          test_framework: { type: 'string', description: 'Test framework to use', default: '' }
        },
        required: ['code']
      }
    },
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does ('generates tests') but doesn't explain how it behaves—e.g., whether it overwrites existing tests, requires specific permissions, handles errors, or produces structured output. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of generating tests (which could involve language-specific frameworks or output formats), the lack of annotations and output schema means the description is incomplete. It doesn't address behavioral aspects, return values, or error handling, leaving gaps for the agent to navigate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters ('code' and 'test_framework') adequately. The description doesn't add any meaning beyond what the schema provides, such as examples or constraints, but the high schema coverage justifies the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('generates') and resource ('tests for the given code'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'review_code' or 'fix_code', which might also involve testing-related functionality, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'review_code' or 'fix_code'. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based solely on the tool name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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