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JanNafta

PropellerAds MCP Server

by JanNafta

find_underperforming_zones

Identify ad zones with high spending but low conversions to optimize campaign performance and create blacklist candidates for PropellerAds campaigns.

Instructions

Find zones that are spending money but not converting. Useful for blacklist candidates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
campaign_idYesCampaign ID
min_spendNoMinimum spend threshold (default: $10)
max_conversionsNoMaximum conversions (default: 0)
date_fromNoStart date
date_toNoEnd date

Implementation Reference

  • The handler for the 'find_underperforming_zones' tool, which filters zone statistics based on spend and conversion thresholds.
    elif name == "find_underperforming_zones":
        zones = client.get_zone_statistics(
            campaign_id=args["campaign_id"],
            date_from=args.get("date_from"),
            date_to=args.get("date_to"),
        )
    
        min_spend = args.get("min_spend", 10)
        max_conv = args.get("max_conversions", 0)
    
        underperforming = []
        for z in zones:
            spend = z.get("spend", z.get("cost", 0)) or 0
            conv = z.get("conversions", 0) or 0
            if spend >= min_spend and conv <= max_conv:
                underperforming.append(z)
    
        if not underperforming:
            return f"No underperforming zones found (min spend: ${min_spend}, max conversions: {max_conv})."
    
        underperforming.sort(key=lambda x: x.get("spend", x.get("cost", 0)) or 0, reverse=True)
    
        lines = [f"# Underperforming Zones (Campaign {args['campaign_id']})\n\n"]
        lines.append(f"Criteria: Spend >= ${min_spend}, Conversions <= {max_conv}\n\n")
        lines.append("| Zone ID | Spend | Conversions | Clicks |\n")
        lines.append("|---------|-------|-------------|--------|\n")
    
        total_waste = 0
        for z in underperforming:
            spend = z.get("spend", z.get("cost", 0)) or 0
            total_waste += spend
            lines.append(
                f"| {z.get('zone_id')} | "
                f"{format_currency(spend)} | "
                f"{z.get('conversions', 0)} | "
                f"{z.get('clicks', 0)} |\n"
            )
    
        lines.append(f"\n**Total wasted spend:** {format_currency(total_waste)}\n")
        lines.append(f"**Zones to blacklist:** {len(underperforming)}\n")
    
        zone_ids = [z.get("zone_id") for z in underperforming if z.get("zone_id")]
        lines.append(f"\nZone IDs: `{zone_ids}`")
    
        return "".join(lines)
  • Tool registration and input schema definition for 'find_underperforming_zones'.
    Tool(
        name="find_underperforming_zones",
        description="Find zones that are spending money but not converting. Useful for blacklist candidates.",
        inputSchema={
            "type": "object",
            "properties": {
                "campaign_id": {"type": "integer", "description": "Campaign ID"},
                "min_spend": {
                    "type": "number",
                    "description": "Minimum spend threshold (default: $10)",
                },
                "max_conversions": {
                    "type": "integer",
                    "description": "Maximum conversions (default: 0)",
                },
                "date_from": {"type": "string", "description": "Start date"},
                "date_to": {"type": "string", "description": "End date"},
            },
            "required": ["campaign_id"],
        },
    ),
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool's output purpose ('blacklist candidates') but lacks details on permissions needed, rate limits, whether it's a read-only operation, what the return format looks like, or any side effects. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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

Conciseness5/5

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

The description is extremely concise with two short sentences that are front-loaded and waste no words. The first sentence states the core purpose, and the second adds practical context, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and a usage hint but lacks details on behavior, output format, or error handling. With no annotations to fill gaps, the description should do more to be complete for an agent to use it effectively.

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

Parameters3/5

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

The input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds minimal value beyond the schema by implying the tool uses spend and conversion metrics but doesn't explain parameter interactions or provide additional context like how 'min_spend' and 'max_conversions' combine to define 'underperforming.' Baseline 3 is appropriate given high schema coverage.

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: 'Find zones that are spending money but not converting.' It specifies the verb ('find') and resource ('zones') with a clear criterion (spending without conversion). However, it doesn't explicitly differentiate from siblings like 'find_top_zones' or 'find_scaling_opportunities' beyond the implied focus on underperformance.

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

Usage Guidelines3/5

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

The description provides implied usage context: 'Useful for blacklist candidates.' This suggests when to use the tool (to identify zones for potential blacklisting) but doesn't explicitly state when not to use it or name alternatives like 'auto_blacklist_zones' or 'get_zone_performance' for comparison. The guidance is helpful but not comprehensive.

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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