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debuggingapproach

Apply systematic debugging methods like binary search and cause elimination to identify and resolve technical issues through structured problem-solving approaches.

Instructions

A tool for applying systematic debugging approaches to solve technical issues. Supports various debugging methods including:

  • Binary Search

  • Reverse Engineering

  • Divide and Conquer

  • Backtracking

  • Cause Elimination

  • Program Slicing

Each approach provides a structured method for identifying and resolving issues.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
approachNameYes
issueYes
stepsNo
findingsNo
resolutionNo

Implementation Reference

  • The `processApproach` method in `DebuggingApproachServer` class handles the execution logic for the 'debuggingapproach' tool, including input validation and result formatting.
    public processApproach(input: unknown): { content: Array<{ type: string; text: string }>; isError?: boolean } {
      try {
        const validatedInput = this.validateApproachData(input);
        const formattedOutput = this.formatApproachOutput(validatedInput);
        console.error(formattedOutput);
    
        return {
          content: [{
            type: "text",
            text: JSON.stringify({
              approachName: validatedInput.approachName,
              status: 'success',
              hasSteps: validatedInput.steps.length > 0,
              hasResolution: !!validatedInput.resolution
            }, null, 2)
          }]
        };
      } catch (error) {
        return {
          content: [{
            type: "text",
            text: JSON.stringify({
              error: error instanceof Error ? error.message : String(error),
              status: 'failed'
            }, null, 2)
          }],
          isError: true
        };
      }
    }
  • The `DebuggingApproachData` interface defines the input structure and types for the debugging approach tool.
    export interface DebuggingApproachData {
        approachName: string;
        issue: string;
        steps: string[];
        findings: string;
        resolution: string;
    }
    
    // Collaborative Reasoning
  • src/index.ts:1083-1095 (registration)
    The 'debuggingapproach' tool handler is registered within the main switch statement of the tool execution logic in `src/index.ts`, delegating to `debuggingServer.processApproach`.
    case "debuggingapproach": {
        const result = debuggingServer.processApproach(
            request.params.arguments
        );
        return {
            content: [
                {
                    type: "text",
                    text: JSON.stringify(result, null, 2),
                },
            ],
        };
    }
Behavior2/5

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

With no annotations provided, the description carries full disclosure burden but reveals nothing about actual tool behavior—whether it returns a diagnostic plan, validates provided steps, requires iterative calls, or produces side effects. The phrase 'structured method for identifying and resolving issues' is vague and doesn't explain the output format.

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

Conciseness3/5

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

Three-sentence structure is appropriately sized, though the final sentence ('Each approach provides...') is generic and adds minimal value beyond restating the purpose. The bulleted enumeration of methods is efficiently presented.

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 5 parameters with 0% schema coverage and no output schema, the description is insufficient. It doesn't explain the interaction between input parameters (e.g., whether 'steps' are inputs for validation or outputs for planning) or what constitutes a complete invocation sequence.

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

Parameters2/5

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

Schema coverage is 0%, requiring heavy description compensation. While it maps enum values to readable method names (helpful for 'approachName') and implies 'issue' describes the technical problem, it completely omits explanation for 'steps', 'findings', and 'resolution'—critical context given these suggest a progressive debugging workflow state.

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?

Clearly identifies the resource (debugging approaches) and action (applying) with specific enumeration of supported methods (Binary Search, Reverse Engineering, etc.). However, it fails to explicitly differentiate from siblings like 'scientificmethod' or 'sequentialthinking' that could also be used for problem-solving.

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?

Provides no guidance on when to select this tool versus alternatives like 'scientificmethod' or 'decisionframework', nor does it indicate prerequisites such as having a reproducible issue or specific technical context.

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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