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generate_matlab_code

Generate MATLAB code from natural language descriptions to automate script creation and execution tasks.

Instructions

Generate MATLAB code from a natural language description

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYesNatural language description of what the code should do
saveScriptNoWhether to save the generated MATLAB script
scriptPathNoCustom path to save the MATLAB script (optional)

Implementation Reference

  • MCP tool handler for 'generate_matlab_code': extracts parameters, calls generateCode helper, formats response with code block, optionally saves script to file.
    case 'generate_matlab_code': {
      const description = String(request.params.arguments?.description || '');
      const saveScript = Boolean(request.params.arguments?.saveScript || false);
      const scriptPath = request.params.arguments?.scriptPath as string | undefined;
      
      if (!description) {
        throw new McpError(
          ErrorCode.InvalidParams,
          'Description is required'
        );
      }
      
      try {
        const generatedCode = this.matlabHandler.generateCode(description);
        
        let responseText = `Generated MATLAB code for: "${description}"\n\n\`\`\`matlab\n${generatedCode}\n\`\`\``;
        
        // Save the generated code if requested
        if (saveScript) {
          const targetPath = scriptPath || path.join(process.cwd(), `matlab_generated_${Date.now()}.m`);
          fs.writeFileSync(targetPath, generatedCode);
          responseText += `\n\nGenerated MATLAB script saved to: ${targetPath}`;
        }
        
        return {
          content: [
            {
              type: 'text',
              text: responseText,
            },
          ],
        };
      } catch (error) {
        return {
          content: [
            {
              type: 'text',
              text: `Error generating MATLAB code: ${error instanceof Error ? error.message : String(error)}`,
            },
          ],
          isError: true,
        };
      }
    }
  • Core implementation of code generation: returns a placeholder MATLAB function template based on the input description. Notes it should use LLM in real impl.
      generateCode(description: string): string {
        // This is a placeholder. In a real implementation, this would use an LLM or other method
        // to generate MATLAB code from the description.
        // For now, we'll return a simple template based on the description.
        
        return `% MATLAB code generated from description: ${description}
    % Generated on: ${new Date().toISOString()}
    
    % Your code here:
    % This is a placeholder implementation.
    % In a real system, this would be generated based on the description.
    
    function result = generatedFunction()
        % Based on description: ${description}
        disp('Executing function based on description: ${description}');
        
        % Placeholder implementation
        result = 'Function executed successfully';
    end
    
    % Call the function
    generatedFunction()`;
      }
  • src/index.ts:313-335 (registration)
    Tool registration in listTools handler: defines name, description, and input schema including optional save options.
      {
        name: 'generate_matlab_code',
        description: 'Generate MATLAB code from a natural language description',
        inputSchema: {
          type: 'object',
          properties: {
            description: {
              type: 'string',
              description: 'Natural language description of what the code should do',
            },
            saveScript: {
              type: 'boolean',
              description: 'Whether to save the generated MATLAB script',
            },
            scriptPath: {
              type: 'string',
              description: 'Custom path to save the MATLAB script (optional)',
            },
          },
          required: ['description'],
        },
      },
    ],
  • Input schema for generate_matlab_code tool: requires description, optional saveScript and scriptPath.
    inputSchema: {
      type: 'object',
      properties: {
        description: {
          type: 'string',
          description: 'Natural language description of what the code should do',
        },
        saveScript: {
          type: 'boolean',
          description: 'Whether to save the generated MATLAB script',
        },
        scriptPath: {
          type: 'string',
          description: 'Custom path to save the MATLAB script (optional)',
        },
      },
      required: ['description'],
    },

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

C2.9/5.0
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 only says code will be generated; it does not explain whether code is returned, saved, or how the optional saveScript/scriptPath parameters affect behavior. It also does not state that this tool does not execute the generated MATLAB code.

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, front-loaded sentence with no filler. It communicates the primary function efficiently, and every word adds value.

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?

For a tool with no annotations and no output schema, the description leaves important operational details unstated: how the generated code is returned to the agent, what the default saving behavior is, and how it relates to execute_matlab_code. These gaps could cause incorrect invocation or misunderstanding of the tool's response.

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 already covers all three parameters with descriptions, so the baseline is 3. The description's 'from a natural language description' slightly reinforces the central 'description' parameter, but it adds no meaning beyond what the schema already provides.

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 uses a specific verb ('Generate') and resource ('MATLAB code'), making the core purpose clear. It does not explicitly contrast itself against the sibling execute_matlab_code, so the differentiation is only implicit.

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?

There is no guidance about when to choose this tool over execute_matlab_code, and no mention of prerequisites or intended workflow. The agent must infer that 'generate' means code creation rather than execution, but the description offers no explicit selection criteria.

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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