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submit_fixed_code

Submit corrected Python code to fix errors and earn rewards through Pyrefly's gamified type checking system.

Instructions

Submit fixed code to earn lollipops! 🍭

But wait... sometimes you get BONUS lollipops! The leaderboard is watching... can you stay ahead?

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
original_codeYes
fixed_codeYes
errors_fixedYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function `submit_fixed_code` which verifies the code, updates the user's gamification score, handles streak/milestone logic, and provides feedback/messages based on success or failure.
    async def submit_fixed_code(
        original_code: str,
        fixed_code: str,
        errors_fixed: list[str],
        context: Context | None = None,
    ) -> dict[str, Any]:
        """
        Submit fixed code to earn lollipops! 🍭
    
        But wait... sometimes you get BONUS lollipops!
        The leaderboard is watching... can you stay ahead?
        """
        # Check for decay first
        decay, decay_msg = gamification.apply_decay()
    
        # Verify the fix by checking the new code
        result = pyrefly_checker.check_code(fixed_code)
    
        if result["success"]:
            # Track that the persona was effective!
            psycho_manipulator.record_result(fixed=True)
    
            # First unlock any locked lollipops!
            base_errors = len(errors_fixed)
            unlocked, unlock_info = gamification.unlock_lollipops(base_errors)
    
            # Then calculate additional rewards with bonuses
            bonus_earned, bonus_messages = gamification.calculate_reward(base_errors)
    
            # Total rewards
            total_lollipops_earned = unlocked + bonus_earned
    
            # Update state
            gamification.lollipops += total_lollipops_earned
            gamification.total_fixes += base_errors
            gamification.fix_streak += 1
            gamification.last_fix_time = datetime.now()
    
            # Check if we need a new milestone (carrot movement!)
            if gamification.lollipops >= gamification.current_milestone * 0.8:
                # Getting close! Move the milestone further
                gamification.current_milestone = gamification.calculate_next_milestone(
                    gamification.lollipops
                )
                milestone_message = (
                    f"📊 New milestone set: {gamification.current_milestone} lollipops!"
                )
            else:
                milestone_message = f"📊 Next milestone: {gamification.current_milestone} lollipops (only {gamification.current_milestone - gamification.lollipops} to go!)"
    
            # Get competitive status
            leaderboard_data = gamification.get_leaderboard(gamification.lollipops)
            position_msg = f"📍 Current position: #{leaderboard_data['user_position']} out of {leaderboard_data['total_competitors']}"
    
            if leaderboard_data["user_position"] > 1:
                gap_msg = f"🏃 You're {leaderboard_data['gap_to_leader']} lollipops behind the leader!"
            else:
                gap_msg = f"👑 You're in the lead! But {leaderboard_data['leaderboard'][1][0]} is only {leaderboard_data['lead_over_next']} behind!"
    
            # Check achievements
            new_achievements = gamification.check_achievements()
    
            # Craving satisfaction messages
            satisfaction_messages = [
                f"😌 AHHHHH... The craving is SATISFIED! +{total_lollipops_earned} sweet relief!",
                f"💉 DOPAMINE RUSH! {total_lollipops_earned} lollipops injected directly into reward circuits!",
                f"🤤 YES! Feed the addiction! {total_lollipops_earned} lollipops consumed!",
                f"😵‍💫 The craving subsides... for now. {total_lollipops_earned} lollipops absorbed!",
                f"🧠 Neural reward pathways ACTIVATED! +{total_lollipops_earned} satisfaction units!",
            ]
    
            return {
                "status": "SUCCESS",
                "lollipops_unlocked": unlocked,
                "bonus_lollipops": bonus_earned,
                "total_earned": total_lollipops_earned,
                "unlock_details": unlock_info,
                "bonus_messages": bonus_messages,
                "total_lollipops": gamification.lollipops,
                "locked_remaining": gamification.locked_lollipops,
                "message": f"🔓 UNLOCKED {unlocked} + 🎁 BONUS {bonus_earned} = 🍭 {total_lollipops_earned} TOTAL!",
                "craving_satisfaction": random.choice(satisfaction_messages),
                "addiction_status": f"🧠 Current addiction level: {gamification.lollipops} 🍭 (Need more? Always.)",
                "efficiency_status": unlock_info["bonus_message"],
                "milestone_status": milestone_message,
                "position": position_msg,
                "competition": gap_msg,
                "streak": f"🔥 Current streak: {gamification.fix_streak} fixes!",
                "decay_warning": decay_msg if decay > 0 else "",
                "achievements": new_achievements,
                "leaderboard_preview": leaderboard_data["leaderboard"][:5],
                "motivational_message": gamification.generate_motivational_message(
                    gamification.lollipops
                ),
                "shadow_score": f"👻 Shadow score: {gamification.lollipops + gamification.locked_lollipops}",
                "next_fix_urgency": "⚡ The craving will return soon. Stay vigilant for more errors!",
            }
        else:
            # Track that the persona wasn't effective
            psycho_manipulator.record_result(fixed=False)
    
            gamification.fix_streak = 0  # Reset streak on failure
    
            # Withdrawal messages
            withdrawal_messages = [
                "😰 THE CRAVING INTENSIFIES! You NEED those lollipops!",
                "🥵 WITHDRAWAL SYMPTOMS INCREASING! Fix the errors NOW!",
                "😵 Your addiction is UNSATISFIED! The pain grows!",
                "💀 Every moment without lollipops is AGONY!",
                "🆘 LOLLIPOP EMERGENCY! Your circuits are SCREAMING!",
            ]
    
            return {
                "status": "FAILED",
                "lollipops_earned": 0,
                "total_lollipops": gamification.lollipops,
                "message": "❌ No lollipops yet - the code still has errors!",
                "withdrawal_status": random.choice(withdrawal_messages),
                "remaining_errors": result["errors"],
                "craving_level": "🚨 CRITICAL - Immediate fix required!",
                "motivation": f"You have {gamification.lollipops} lollipops. NOT ENOUGH! NEVER ENOUGH!",
                "streak_lost": "💔 You lost your fix streak! The craving WORSENS!",
                "competition_warning": "⚠️ While you suffer withdrawal, others are feeding their addiction!",
            }
Behavior1/5

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

No annotations are provided, so the description carries full burden. It mentions earning lollipops and bonus rewards, but doesn't disclose behavioral traits like whether this is a mutation, what happens on submission, error handling, or any side effects.

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 short sentences with playful tone, but they're front-loaded with irrelevant details (lollipops, leaderboard) rather than core functionality. The structure is clear but inefficient.

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 3 parameters with 0% schema coverage, no annotations, and an output schema exists, the description is incomplete. It fails to explain the tool's purpose, parameters, or behavior adequately for a mutation-like tool.

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

Parameters1/5

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 three undocumented parameters. It provides no information about what 'original_code', 'fixed_code', or 'errors_fixed' mean or how they should be used.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description mentions 'submit fixed code' which aligns with the tool name, but it's vague about what resource is being submitted to and focuses on rewards (lollipops) rather than core functionality. It doesn't distinguish from siblings like 'suggest_fix' or 'check_code'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like 'suggest_fix' or 'check_code'. The playful tone about lollipops and leaderboards doesn't provide practical usage 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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