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JanNafta

PropellerAds MCP Server

by JanNafta

auto_blacklist_zones

Automatically identify and block underperforming ad zones in a campaign to optimize advertising spend and improve campaign performance.

Instructions

Automatically find and blacklist underperforming zones for a campaign.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
campaign_idYesCampaign ID
min_spendNoMinimum spend to consider (default: $10)
max_conversionsNoMaximum conversions (default: 0)
date_fromNoStart date
date_toNoEnd date
dry_runNoIf true, show zones but don't blacklist (default: true)

Implementation Reference

  • The implementation of the 'auto_blacklist_zones' tool handler, which identifies underperforming zones and optionally blacklists them.
    elif name == "auto_blacklist_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)
        dry_run = args.get("dry_run", True)
    
        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 "No underperforming zones found."
    
        zone_ids = [z.get("zone_id") for z in underperforming if z.get("zone_id")]
        total_spend = sum(z.get("spend", z.get("cost", 0)) or 0 for z in underperforming)
    
        if dry_run:
            return (
                f"# Dry Run - Zones to Blacklist\n\n"
                f"Found {len(zone_ids)} underperforming zones.\n"
                f"Total wasted spend: {format_currency(total_spend)}\n\n"
                f"Zone IDs: `{zone_ids}`\n\n"
                f"Run with `dry_run: false` to actually blacklist these zones."
            )
        else:
            result = client.add_zones_to_blacklist(args["campaign_id"], zone_ids)
  • The schema definition for the 'auto_blacklist_zones' tool, including input arguments like campaign_id, min_spend, and max_conversions.
    Tool(
        name="auto_blacklist_zones",
        description="Automatically find and blacklist underperforming zones for a campaign.",
        inputSchema={
            "type": "object",
            "properties": {
                "campaign_id": {"type": "integer", "description": "Campaign ID"},
                "min_spend": {
                    "type": "number",
                    "description": "Minimum spend to consider (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"},
                "dry_run": {
                    "type": "boolean",
                    "description": "If true, show zones but don't blacklist (default: true)",
                },
            },
            "required": ["campaign_id"],
        },
    ),
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 the tool will 'find and blacklist,' implying a potentially destructive write operation, but doesn't clarify permissions needed, whether changes are reversible, rate limits, or what happens during execution. The 'dry_run' parameter in the schema suggests a safety feature, but the description doesn't highlight this behavioral trait.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action ('find and blacklist') and resource, making it easy to parse quickly. Every word earns its place in conveying the essential function.

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 of an automated blacklisting tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'underperforming' means, how zones are selected, what the output looks like, or potential side effects. For a tool that likely modifies campaign settings, more context is needed to use it safely and 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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as explaining how 'min_spend' and 'max_conversions' interact to define 'underperforming.' However, with high schema coverage, a baseline score of 3 is appropriate as the description doesn't need to compensate.

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 with a specific verb ('find and blacklist') and resource ('underperforming zones for a campaign'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'find_underperforming_zones' or 'add_to_blacklist', which could cause confusion about when to use each.

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. There are multiple sibling tools related to zones, blacklisting, and performance analysis (e.g., 'find_underperforming_zones', 'add_to_blacklist', 'find_top_zones'), but the description doesn't mention any of them or specify contexts where this automated approach is preferred over manual alternatives.

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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