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Qwen3-Coder MCP Server

qwen3_code_generate

Generate code from natural language descriptions in specified programming languages using the Qwen3-Coder model.

Instructions

Generate code using Qwen3-Coder

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of what code to generate
languageNoTarget programming language

Implementation Reference

  • Registration of the 'qwen3_code_generate' tool, including name, description, and input schema definition.
    {
      name: "qwen3_code_generate",
      description: "Generate code using Qwen3-Coder",
      inputSchema: {
        type: "object",
        properties: {
          prompt: {
            type: "string",
            description: "Description of what code to generate"
          },
          language: {
            type: "string",
            description: "Target programming language"
          }
        },
        required: ["prompt"]
      }
    },
  • Handler for 'qwen3_code_generate': constructs a specific prompt based on input arguments and calls the shared Qwen3-Coder function.
          case "qwen3_code_generate":
            prompt = `Generate ${args.language || 'code'} for the following requirement:
    
    ${args.prompt}
    
    ${args.language ? `Please write the code in ${args.language}.` : ''}
    
    Provide clean, well-documented code with explanations.`;
            result = await callQwen3Coder(prompt);
            break;
  • Shared helper function that spawns Ollama process to run Qwen3-Coder model with the given prompt and returns the output.
    async function callQwen3Coder(prompt, options = {}) {
      return new Promise((resolve, reject) => {
        const ollamaProcess = spawn('ollama', ['run', 'qwen3-coder:30b', prompt], {
          stdio: ['pipe', 'pipe', 'pipe']
        });
    
        let output = '';
        let error = '';
    
        ollamaProcess.stdout.on('data', (data) => {
          output += data.toString();
        });
    
        ollamaProcess.stderr.on('data', (data) => {
          error += data.toString();
        });
    
        ollamaProcess.on('close', (code) => {
          if (code === 0) {
            resolve(output.trim());
          } else {
            reject(new Error(`Ollama process exited with code ${code}: ${error}`));
          }
        });
    
        // Set timeout for long-running requests
        setTimeout(() => {
          ollamaProcess.kill();
          reject(new Error('Request timeout'));
        }, options.timeout || 120000); // 2 minutes default timeout
      });
    }
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 the tool generates code but does not explain how it works, what limitations exist (e.g., code quality, length constraints), or what the output format looks like. For a tool with no annotations and no output schema, this is a significant gap in transparency about its behavior and results.

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: 'Generate code using Qwen3-Coder'. It is front-loaded and appropriately sized for the tool's purpose, making it easy to parse without unnecessary details. Every word earns its place, contributing directly to understanding the tool's function.

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 a code generation tool, the lack of annotations, and no output schema, the description is incomplete. It does not address key aspects like what the generated code includes, how errors are handled, or any usage limits. For a tool with two parameters and no structured behavioral hints, more context is needed to ensure the agent can use it effectively.

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, with clear documentation for 'prompt' and 'language' parameters. The description does not add any additional meaning beyond what the schema provides, such as examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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 as 'Generate code using Qwen3-Coder', which specifies the verb ('Generate') and resource ('code') with the method ('using Qwen3-Coder'). It distinguishes from siblings like 'explain', 'fix', 'optimize', and 'review' by focusing on creation rather than analysis or modification. However, it doesn't explicitly mention what Qwen3-Coder is or its capabilities, keeping it from a perfect 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 its siblings. It lacks explicit instructions on scenarios for code generation compared to alternatives like code explanation or fixing, and does not mention prerequisites or exclusions. This leaves the agent to infer usage based on tool names alone, which is insufficient for clear decision-making.

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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