Mastering Error Handling and Bug Catching with Claude Code: A Comprehensive Guide

Introduction

Claude Code is an AI coding assistant from Anthropic that can read your code, run commands, and make changes to your project. It can significantly accelerate development by automating tasks, fixing bugs, and building features—but strong results in error handling and bug catching require a deliberate setup.

This guide walks you through the most effective patterns for making Claude Code reliable in real-world development:

• Building a verifiable feedback loop so Claude can validate its own work

• Using a systematic workflow for bug resolution

• Configuring Hooks, MCP integrations, and a well-structured CLAUDE.md

• Applying advanced options like skills, sub-agents, agent teams, and context management

A key theme throughout is that Claude performs best when it can check itself via tests, linters, and deterministic automation.

The Core Philosophy: Verifiable Work

The single most important principle for a successful Claude Code setup is verifiability. Claude Code is not just a chatbot—it’s an agent acting on your codebase. Its performance improves dramatically when it has a clear mechanism to verify the success or failure of its work.

Claude performs dramatically better when it can verify its own work, like run tests, compare screenshots, and validate outputs. Without clear success criteria, it might produce something that looks right but actually doesn’t work.

In practice, this means building a feedback loop around:

• Unit tests and integration tests

• Linters and formatters

• Build/compile steps

• Simple scripts that validate outputs

When Claude can run these checks itself, it becomes a far more reliable partner.

A Systematic Workflow for Bug Resolution

A structured process prevents Claude from jumping to conclusions and helps ensure a correct fix—especially in unfamiliar codebases.

This separation of exploration, planning, and implementation is especially useful for complex bugs.

Hooks for Error Handling and Automation

Hooks are user-defined shell commands that execute at specific points in Claude Code’s lifecycle. They provide deterministic control—ensuring certain actions (like running tests, formatting code, or blocking unsafe commands) happen automatically.

Hooks are one of the fastest ways to improve bug catching because they:

• Enforce quality gates consistently

• Provide immediate feedback when something fails

• Reduce reliance on “remembering to run tests”

Hook Events That Matter Most

• PreToolUse: Runs before a tool executes. Great for validation and blocking risky operations.

• PostToolUse: Runs after a tool succeeds. Great for running tests or formatting.

• PostToolUseFailure: Runs when a tool fails. Useful for logging and alerting.

• SessionStart: Runs at the start of a session—useful for initialization or reinjecting context.

• Notification: Runs when Claude is waiting for user input.

Practical Hook Examples

1) Run tests after any file write

{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Write",
        "hooks": [
          {
            "type": "command",
            "command": "pytest tests/"
          }
        ]
      }
    ]
  }
}

2) Log failures automatically

{
  "hooks": {
    "PostToolUseFailure": [
      {
        "matcher": ".*",
        "hooks": [
          {
            "type": "command",
            "command": "echo 'Tool failed: $TOOL_INPUT' >> /tmp/claude-error.log"
          }
        ]
      }
    ]
  }
}

3) Block dangerous commands (example script)

#!/bin/bash
# Prevent dangerous commands
if [[ $TOOL_INPUT =~ (rm -rf|sudo) ]]; then
  echo "Dangerous command blocked: $TOOL_INPUT" >&2
  exit 2 # Exit code 2 blocks the action
fi
exit 0

4) Auto-format after writes (Prettier example)

{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Write",
        "hooks": [
          {
            "type": "command",
            "command": "npx prettier --write ."
          }
        ]
      }
    ]
  }
}

Hook Best Practices

• Validate before execution using PreToolUse

• Always run tests/linting after changes

• Log failures so debugging data isn’t lost

• Use matchers to keep expensive checks targeted

• Start simple, then iterate

MCP Integration

MCP (Model Context Protocol) is an open standard that lets Claude connect to external tools and data sources. This is a major upgrade for debugging because it gives Claude real-world context beyond your local codebase.

What MCP Enables

• Query production errors from monitoring tools

• Read and update issues in trackers

• Interact with GitHub/GitLab PRs and CI logs

• Query databases to reproduce state-dependent bugs

• Pull design specs from tools like Figma

Essential MCP Integrations for Debugging

• Error monitoring: Sentry (stack traces, frequency, context)

• Project management: Jira / Linear (requirements and acceptance criteria)

• Version control: GitHub / GitLab (PR discussions, checks, CI failures)

• Databases: PostgreSQL (query state, logs, user journeys)

MCP Best Practices

• Use OAuth or secure auth wherever possible

• Scope access tightly—only what Claude needs

• Test each integration with small prompts first

• Limit tool output size to avoid flooding context

• Combine MCP + Hooks for automated feedback loops

The CLAUDE.md File

CLAUDE.md is a project-level Markdown file that becomes part of Claude’s system prompt when you work in that directory. It’s the best place to define project-specific rules and workflows.

What to Put in CLAUDE.md

• Tech stack and versions

• Project structure and “where things live”

• Testing commands (unit, integration, e2e)

• Error handling conventions (exceptions, logging, retries)

• Coding standards and formatting rules

• Deployment notes and required env vars

• Team conventions (branches, commits, PR expectations)

• Known bugs and reproduction steps

Example Sections

Tech stack

# Tech Stack
- Language: Python 3.10
- Framework: Django 4.2
- Database: PostgreSQL 15
- Env Vars: DATABASE_URL, SECRET_KEY, DEBUG

Testing & validation

# Testing
- Unit tests: `pytest tests/`
- E2E tests: `./run_e2e_tests.sh`
- Run tests before merging to main
- New features must include tests

Error handling conventions

# Error Handling
- APIs return JSON: {"status": "...", "message": "..."}
- Log exceptions with stack traces
- Use custom exceptions:
  - BadRequestError: validation issues
  - DatabaseError: DB failures

CLAUDE.md Best Practices

• Keep it short, clear, and practical

• Use headers and bullet points for scanability

• Split large repos using imports (e.g., @security.md)

• Update it as standards change

• Add short code examples for common patterns

Other Advanced Setup Considerations

Skills and Sub-Agents

Skills extend Claude with specialized workflows (generate tests, security checks, refactoring helpers). Sub-agents are separate Claude instances that can tackle focused subtasks—great for debugging deep modules without cluttering the main thread.

Agent Teams

Agent teams allow parallel investigation—one agent reads logs, another traces backend flow, another inspects frontend state. This can accelerate root-cause discovery in multi-system bugs.

Memory and Context Management

Claude can compact context during long sessions. To avoid losing critical details:

• Keep CLAUDE.md concise and high-signal

• Use SessionStart hooks to re-inject key project conventions when needed

• Maintain a lightweight “registry” document for recurring bugs and fixes

Performance Tuning

• Cap overly large MCP outputs

• Prefer targeted tasks (fix one function) over vague requests (fix everything)

• Ensure the environment can run tests quickly—feedback speed matters

Conclusion

A strong Claude Code setup transforms it from a helpful assistant into a more autonomous partner for maintaining code quality.

The winning formula is an integrated loop:

• CLAUDE.md: teaches Claude how your project works

• Hooks: enforce safety, formatting, and tests deterministically

• MCP: supplies real-world context from monitoring, tickets, CI, and databases

Start small—add a test-running hook and a basic CLAUDE.md—then expand toward MCP and advanced automation. Over time, Claude becomes dramatically better at catching bugs early and fixing them correctly.