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emmron
by emmron

mcp__gemini__debug_analysis

Analyze and debug errors in code by providing error messages and code snippets. Supports multiple programming languages for accurate problem resolution.

Instructions

AI-powered debugging assistance

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoCode where error occurs
errorYesError message
languageNoProgramming languagejavascript

Implementation Reference

  • The main execution logic for the mcp__gemini__debug_analysis tool. It destructures arguments, validates the error message, constructs a detailed prompt for the AI, calls aiClient with 'debug' model, and returns a formatted response.
        handler: async (args) => {
          const { error, code = '', language = 'javascript' } = args;
          validateString(error, 'error message');
          
          const prompt = `Debug this ${language} error:
    
    **Error:** ${error}
    
    ${code ? `**Code:**\n\`\`\`${language}\n${code}\n\`\`\`` : ''}
    
    Provide:
    1. Root cause analysis
    2. Step-by-step debugging approach
    3. Potential fixes with code examples
    4. Prevention strategies
    5. Common pitfalls to avoid`;
    
          const result = await aiClient.call(prompt, 'debug');
          return `🐛 **Debug Analysis**\n\n${result}`;
        }
  • Input parameter schema defining required 'error' string, optional 'code' and 'language' (defaults to 'javascript'). Used for validation in the MCP tool registry.
    parameters: {
      error: { type: 'string', description: 'Error message', required: true },
      code: { type: 'string', description: 'Code where error occurs' },
      language: { type: 'string', description: 'Programming language', default: 'javascript' }
    },
  • Registration block in the main ToolRegistry that imports and registers all tools from analysisTools module, including mcp__gemini__debug_analysis, making it available for MCP execution.
    this.registerToolsFromModule(codeTools);
    this.registerToolsFromModule(analysisTools);
    this.registerToolsFromModule(enhancedTools);
    this.registerToolsFromModule(businessTools);
    this.registerToolsFromModule(licenseTools);
Behavior2/5

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

No annotations are provided, so the description carries full burden. 'AI-powered debugging assistance' implies analysis and suggestions but doesn't disclose behavioral traits like whether it modifies code, requires specific permissions, has rate limits, or what kind of output to expect. This leaves significant gaps for a debugging tool.

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 phrase with zero wasted words. It's appropriately sized and front-loaded, though the brevity contributes to the vagueness in other dimensions.

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 no annotations and no output schema, the description is inadequate for a debugging tool with 3 parameters. It doesn't explain what the tool returns, how it behaves, or when to use it, leaving the agent with insufficient context to invoke it correctly.

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%, providing clear documentation for all parameters. The description adds no additional meaning beyond the schema, which already defines 'code', 'error', and 'language' with descriptions. Baseline 3 is appropriate when schema does the heavy lifting.

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 'AI-powered debugging assistance' states the general purpose (debugging) but is vague about the specific action and resource. It doesn't distinguish from sibling tools like 'debug_master' or 'analyze_codebase', leaving ambiguity about what makes this tool unique.

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. With multiple sibling tools including 'debug_master' and various analysis tools, the description offers no context about appropriate use cases, prerequisites, or exclusions.

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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