Pal MCP Toolkit

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Comprehensive Pal MCP toolkit for code analysis, debugging, planning, refactoring, code review, and execution tracing. Provides systematic workflows with expert validation for complex development tasks.

Available today. Use it from your connected AI after setup.

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What this skill tells your AI

The instructions your AI receives, as published by pedronauck/skills in skills/community/pal/SKILL.md and read by ahel’s review.

The Pal MCP toolkit provides specialized tools for comprehensive code analysis and development workflows. Each tool follows a multi-step workflow pattern with expert validation.

Quick Reference: Tool Selection

Task TypeToolReference
Architecture & code analysis before complex tasksmcp__zen__analyzeanalyze.md
Bug investigation & root cause analysismcp__zen__debugdebug.md
Strategic planning & task breakdownmcp__zen__plannerplanner.md
Code smell detection & refactoringmcp__zen__refactorrefactor.md
Code review after completing tasksmcp__zen__codereviewreview.md
Execution flow & dependency tracingmcp__zen__tracertracer.md

Critical Workflow Requirements

Mandatory Completion Rules

  • NEVER stop a workflow before next_step_required: false is returned
  • ALWAYS increment step_number and call the tool again when next_step_required: true
  • TASK INVALIDATION: Incomplete workflows result in immediate task rejection
  • NO EXCEPTIONS: Even if analysis seems complete after step 1, you MUST complete all steps

Model Requirement

  • MANDATORY: Always use model: "anthropic/claude-opus-4.6" for ALL Pal MCP tool calls
  • NEVER use any other model when calling Pal MCP tools

Workflow Validation Checklist

  1. Check next_step_required in every response
  2. If true, call the tool again with incremented step_number
  3. Never proceed with implementation while next_step_required: true
  4. Workflow is complete ONLY when next_step_required: false

Tool Overview

Analyze (mcp__zen__analyze)

When to Use:

  • Before complex tasks to understand existing architecture
  • Architecture review and system design assessment
  • Performance, security, and technical debt analysis

Key Parameters:

  • analysis_type: "architecture" | "performance" | "security" | "quality" | "general"
  • output_format: "summary" | "detailed" | "actionable"

See references/analyze.md for complete documentation.


Debug (mcp__zen__debug)

When to Use:

  • Bug investigation and root cause analysis
  • Performance issues, memory leaks, race conditions
  • Integration failures and service communication problems

Key Parameters:

  • hypothesis: Current theory about the root cause
  • confidence: "exploring" | "low" | "medium" | "high" | "very_high" | "almost_certain" | "certain"

See references/debug.md for complete documentation.


Planner (mcp__zen__planner)

When to Use:

  • Breaking down complex tasks into manageable steps
  • System design and architectural decisions
  • Migration planning and implementation strategies

Key Parameters:

  • is_step_revision: For refining previous steps
  • is_branch_point: For exploring alternative approaches
  • branch_id: Naming alternative approaches

See references/planner.md for complete documentation.


Refactor (mcp__zen__refactor)

When to Use:

  • Addressing code smells and technical debt
  • Decomposing large modules or classes
  • Modernizing legacy patterns

Key Parameters:

  • refactor_type: "codesmells" | "decompose" | "modernize" | "organization"
  • focus_areas: ["performance", "readability", "maintainability", "security"]

See references/refactor.md for complete documentation.


Code Review (mcp__zen__codereview)

When to Use:

  • After completing a task (MANDATORY)
  • Before submitting pull requests
  • Validating implementation against project standards

Key Parameters:

  • review_type: "full" for comprehensive analysis
  • severity_filter: "all" to catch all severity levels
  • focus_on: Specific areas like "performance", "security", "type-safety"

See references/review.md for complete documentation.


Tracer (mcp__zen__tracer)

When to Use:

  • Understanding code execution paths
  • Mapping dependencies before refactoring
  • Debugging complex flows

Key Parameters:

  • trace_mode: "precision" (execution flow) | "dependencies" (structural analysis) | "ask"
  • target_description: Clear description of what to trace and why

See references/tracer.md for complete documentation.

Common Required Parameters

All Pal MCP tools require these base parameters:

{
  "step": "Description of current step",
  "step_number": 1,
  "total_steps": 2,
  "next_step_required": true,
  "findings": "Findings from this step",
  "model": "anthropic/claude-opus-4.6"
}

File Path Requirements

  • ALWAYS use full absolute paths for relevant_files
  • Include files directly involved in the analysis
  • Include related files that provide context
  • Include test files when relevant

Typical Workflow Pattern

  1. Start: Call the tool with step_number: 1 and initial strategy
  2. Iterate: Increment step_number and refine findings based on previous step
  3. Continue: Keep calling until next_step_required: false
  4. Complete: Only proceed with implementation after workflow completes

Violation Examples (Task Rejection)

  • Calling a Pal tool once and proceeding to implementation
  • Skipping steps because analysis seems complete
  • Starting implementation before next_step_required: false
  • Using a model other than anthropic/claude-opus-4.6

Signals

GitHub stars
611
Forks
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Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
pal
Source
github.com/pedronauck/skills