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KunihiroS

claude-code-mcp

simulate_command

Simulate the execution of a command to test its behavior and output without running it on a live system, ensuring safe and controlled analysis.

Instructions

Simulates the execution of a given command.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYesCommand to execute
inputNoInput data

Implementation Reference

  • Registration of the 'simulate_command' tool in the ListTools handler, defining its name, description, and input schema.
    {
      name: 'simulate_command',
      description: 'Simulates the execution of a given command.',
      inputSchema: {
        type: 'object',
        properties: {
          command: { type: 'string', description: 'Command to execute' },
          input: { type: 'string', description: 'Input data', default: '' }
        },
        required: ['command']
      }
    },
  • Input schema for the simulate_command tool: object with required 'command' string and optional 'input' string.
    inputSchema: {
      type: 'object',
      properties: {
        command: { type: 'string', description: 'Command to execute' },
        input: { type: 'string', description: 'Input data', default: '' }
      },
      required: ['command']
    }
  • Handler implementation for 'simulate_command': destructures command and input, builds a simulation prompt for Claude CLI via runClaudeCommand helper, returns the AI-generated simulation output as text content.
    case 'simulate_command': {
      const { command, input } = args;
      logger.debug(`Processing simulate_command request, command: ${command}`);
      const prompt = `You are super professional engineer. Simulate the execution of the following command:\n\nCommand: ${command}\n\nInput: ${input || 'No input provided.'}\n\nDescribe the expected behavior and output, without actually executing the command.`;
      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 }] };
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool simulates execution but doesn't clarify what 'simulates' entails—e.g., whether it's a dry run, sandboxed execution, or mock output—nor does it address permissions, side effects, or rate limits.

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 with zero waste, making it appropriately sized and front-loaded. Every word contributes directly to stating the tool's purpose.

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 lack of annotations and output schema, the description is incomplete for a tool that simulates commands. It doesn't explain what the simulation returns, potential errors, or how it differs from actual execution, leaving significant gaps for agent understanding.

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?

The input schema has 100% description coverage, clearly documenting both parameters. The description adds no additional meaning beyond the schema, such as examples or constraints, so it meets the baseline for high schema coverage without compensating further.

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

Purpose3/5

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

The description states the tool 'simulates the execution of a given command,' which provides a basic verb+action but lacks specificity about what type of command or simulation is involved. It doesn't differentiate from sibling tools like 'test_code' or 'your_own_query,' making it vague in context.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention any context, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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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