Skip to main content
Glama
chirag127
by chirag127

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),
                },
            ],
        };
    }

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.2

TDQS

C2.7/5.0
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 mentions that approaches provide 'structured methods for identifying and resolving issues,' which hints at a process-oriented tool, but lacks critical details: it doesn't specify if this is a read-only analysis tool or if it modifies data, what the output format looks like, any rate limits, or authentication needs. For a tool with 5 parameters and no annotation coverage, this is insufficient.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, starting with the core purpose in the first sentence. The bulleted list of methods is efficient for enumeration, and the concluding sentence reinforces the tool's value. There's no redundant information, though it could be slightly more streamlined by integrating the list into the flow.

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 (5 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns, how parameters interact, or the behavioral implications of using different debugging approaches. For a tool that likely involves multi-step reasoning processes, more context on execution and results is needed to be fully helpful.

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 description coverage is 0%, so the description must compensate for undocumented parameters. It lists debugging method names (e.g., 'Binary Search') which partially explains the 'approachName' enum, but doesn't clarify the semantics of other parameters like 'issue', 'steps', 'findings', or 'resolution'. The description adds minimal value beyond what the enum suggests, failing to adequately cover the parameter meanings.

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: 'applying systematic debugging approaches to solve technical issues.' It specifies the verb ('applying') and resource ('debugging approaches'), making it distinct from sibling tools like 'collaborativereasoning' or 'designpattern' which focus on different reasoning methods. However, it doesn't explicitly differentiate itself from all siblings (e.g., 'sequentialthinking' might overlap), preventing 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 alternatives. It lists supported debugging methods but doesn't specify scenarios where this tool is appropriate compared to sibling tools like 'scientificmethod' or 'structuredargumentation'. There's no mention of prerequisites, exclusions, or comparative contexts, leaving usage ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.