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plan_chatgpt_ads_readiness

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Prepare intent clusters, ad copy, proof links, UTM measurement, and launch gates for testing ChatGPT Ads Manager campaigns.

Instructions

Prepare ThumbGate intent clusters, ad copy, proof links, UTM measurement, and launch gates for ChatGPT Ads Manager tests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offerNoOffer to advertise, such as Pro or Workflow Hardening Sprint.
audienceNoAudience segment to target.
budgetNoInitial test budget.
keywordsNoHigh-intent conversational queries.
proofLinksNoProof URLs required by ad claims.

Implementation Reference

  • Main handler function that builds the ChatGPT Ads Readiness Pack. Normalizes options, builds ad groups, creative content, measurement plan, and returns a structured readiness report with strategy, launch checklist, and marketing angle.
    function buildChatgptAdsReadinessPack(rawOptions = {}) {
      const options = normalizeOptions(rawOptions);
      const adGroups = buildAdGroups(options);
      return {
        name: 'thumbgate-chatgpt-ads-readiness-pack',
        source: SOURCE,
        status: 'ready_for_interest_signup',
        offer: options.offer,
        audience: options.audience,
        strategy: {
          channelThesis: 'If ChatGPT Ads Manager becomes self-serve, high-intent conversational queries can capture developers at the moment they are asking how to trust or govern AI agents.',
          trustBoundary: 'OpenAI states ads are separate from answers and clearly labeled, so the campaign must win with proof-backed landing pages rather than implying answer influence.',
          firstMove: 'Submit advertiser interest, prepare exact-match intent clusters, and route traffic to guide or workflow sprint pages with proof links.',
        },
        adGroups,
        creative: buildCreative(options),
        measurement: buildMeasurementPlan(options),
        launchChecklist: [
          'Submit interest at openai.com/advertisers.',
          'Create a ChatGPT ads UTM namespace before first spend.',
          'Use guide landing page for self-serve developer intent.',
          'Use workflow sprint intake for high-risk team workflow pain.',
          'Block unsupported claims in ad copy and landing pages.',
          'Compare paid AI traffic against organic AI-search visibility before scaling budget.',
        ],
        marketingAngle: {
          headline: 'ChatGPT ads are a paid surface for the exact moment developers ask how to trust agents.',
          subhead: 'ThumbGate should be ready with proof-backed copy, intent clusters, and conversion routes before self-serve inventory gets crowded.',
          replyDraft: 'If ChatGPT Ads Manager becomes self-serve, ThumbGate should test it early but stay evidence-first: bid on agent-governance pain, route to the setup guide or workflow sprint, and never imply ads influence ChatGPT answers. The wedge is proof-backed trust at the moment someone asks how to make agents safer.',
        },
      };
    }
  • Options normalization function that defines default values for offer, audience, budget, keywords, and proofLinks — effectively serving as the input schema.
    function normalizeOptions(raw = {}) {
      return {
        offer: normalizeText(raw.offer) || 'ThumbGate Pro and Workflow Hardening Sprint',
        audience: normalizeText(raw.audience) || 'AI coding teams and developers comparing agent governance tools',
        budget: parseNumber(raw.budget || raw['test-budget'], 500),
        keywords: splitList(raw.keywords || raw.queries).length
          ? splitList(raw.keywords || raw.queries)
          : [
            'AI coding agent keeps repeating mistakes',
            'Claude Code pre tool use hook',
            'Cursor agent guardrails',
            'AI agent verification before PR',
            'agent governance for coding teams',
          ],
        proofLinks: splitList(raw.proofLinks || raw['proof-links']).length
          ? splitList(raw.proofLinks || raw['proof-links'])
          : [
            'https://github.com/IgorGanapolsky/ThumbGate/blob/main/docs/VERIFICATION_EVIDENCE.md',
            'https://github.com/IgorGanapolsky/ThumbGate/blob/main/docs/COMMERCIAL_TRUTH.md',
          ],
      };
    }
  • Builds two ad groups (agent-governance-intent and workflow-hardening-intent) with keywords, landing pages, and CTAs.
    function buildAdGroups(options) {
      return [
        {
          id: 'agent-governance-intent',
          theme: 'developers asking how to make AI coding agents safer',
          keywords: options.keywords.filter((keyword) => /agent|guardrail|governance|verification/i.test(keyword)).slice(0, 8),
          landingPage: 'https://thumbgate.ai/guide?utm_source=chatgpt_ads&utm_medium=paid_ai&utm_campaign=agent_governance_intent',
          primaryCta: 'Install the proof-backed guide',
        },
        {
          id: 'workflow-hardening-intent',
          theme: 'teams describing repeated autonomous-agent workflow failures',
          keywords: [
            'AI agent made same mistake again',
            'coding agent opened bad PR',
            'agent changed production without approval',
            'AI workflow hardening sprint',
          ],
          landingPage: 'https://thumbgate.ai/?utm_source=chatgpt_ads&utm_medium=paid_ai&utm_campaign=workflow_hardening_intent#workflow-sprint-intake',
          primaryCta: 'Book one workflow hardening sprint',
        },
      ];
    }
  • MCP server case statement that routes the 'plan_chatgpt_ads_readiness' tool to the buildChatgptAdsReadinessPack handler and wraps the result as text.
    case 'plan_chatgpt_ads_readiness':
      return toTextResult(buildChatgptAdsReadinessPack(args));
  • CLI schema registration mapping the 'chatgpt-ads-readiness-pack' CLI command to the 'plan_chatgpt_ads_readiness' MCP tool, with flag definitions for offer, audience, budget, keywords, and proof-links.
    discoveryCommand({
      name: 'chatgpt-ads-readiness-pack',
      aliases: ['chatgpt-ads-plan', 'ai-ads-plan'],
      description: 'Prepare ThumbGate copy, intent clusters, proof gates, and measurement for ChatGPT Ads Manager tests',
      mcpTool: 'plan_chatgpt_ads_readiness',
      flags: [
        jsonFlag(),
        { name: 'offer', type: 'string', description: 'Offer to advertise, such as Pro or Workflow Hardening Sprint' },
        { name: 'audience', type: 'string', description: 'Audience segment to target' },
        { name: 'budget', type: 'number', description: 'Initial test budget' },
        { name: 'keywords', type: 'string', description: 'Comma-separated high-intent conversational queries' },
        { name: 'proof-links', type: 'string', description: 'Comma-separated proof URLs required by ad claims' },
      ],
    }),
Behavior1/5

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

The description states the tool 'prepares' components, implying write/modify operations, but annotations declare readOnlyHint=true. This direct contradiction severely misinforms an agent about 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.

Conciseness4/5

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

A single sentence that front-loads the key action and lists deliverables. It is concise, though slightly dense.

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?

No output schema is present, and the description does not clarify what the tool returns or any prerequisites. With an annotation contradiction, the completeness is insufficient for safe invocation.

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?

Schema coverage is 100%, so the baseline is 3. The description mentions intents, ad copy, and proof links but does not add meaningful detail beyond what the schema descriptions already provide.

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

Purpose5/5

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

The description uses the specific verb 'Prepare' and identifies multiple resources (ThumbGate intent clusters, ad copy, proof links, etc.) tied to ChatGPT Ads Manager tests, clearly distinguishing it from sibling 'plan_' tools which target different domains.

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?

No guidance is provided on when to use this tool vs alternatives such as plan_intent or plan_agent_design_governance. The agent is left to infer context from the resource names alone.

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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