The Complete Guide to ChatGPT App Discoverability

You built a great ChatGPT app. It works perfectly. Users love it when they find it.

But here's the brutal truth: if users can't find your app, it may as well not exist.

Why Discoverability is the Bottleneck

ChatGPT has +850 million weekly active users. The App Store is live and growing. But unlike the early days of the iPhone App Store—where getting in early meant automatic visibility—ChatGPT apps don't get discovered by accident. There's no "new releases" section that guarantees eyeballs.

Discoverability is the bottleneck for growth. You can build the most useful app in the ecosystem, but if ChatGPT doesn't know when to suggest it—or if users can't find it when they search—you'll get zero installs.

The developers who understand this early will dominate the rankings as the ecosystem matures. Just like mobile ASO (App Store Optimization) became a discipline unto itself, ChatGPT app discoverability will become essential knowledge for anyone building in this space.

How ChatGPT Discovery is Different

The ChatGPT App Store works like traditional app stores—users can browse categories and search for apps. But there's a second discovery channel that's unique to ChatGPT: apps can also appear organically during conversations when the LLM decides they're relevant to what the user is trying to do.

This means your metadata does double duty. It helps you rank in search results, AND it tells ChatGPT when to suggest your app mid-conversation.

This guide covers both channels and what actually matters for each.

ChatGPT app discoverability happens in two distinct places:

Channel 1: App Store Search (Static)

When users search for apps in the ChatGPT App Store, your app needs to rank for relevant keywords. This is traditional SEO—keywords in your name, description, and metadata determine where you appear in search results.

Channel 2: Prompt Optimization (Dynamic)

When users have conversations in ChatGPT, the LLM decides which apps to suggest based on conversation context. Your tool descriptions tell ChatGPT when to invoke your app.

Why both matter: Users discover apps two ways:

1. Active discovery: "I need an app for X" → searches/browses App Store → finds your app
2. Passive discovery: "Help me do X" → ChatGPT suggests your app mid-conversation → user connects

Most developers only optimize for #1. The winners optimize for both.

Appearance vs. Volume: The Two Questions

For each channel, there are two distinct questions to answer:

Most developers only focus on the first question—getting their app to show up. But appearing for a keyword nobody searches, or a prompt nobody asks, doesn't drive installs.

The goal is to appear consistently for high-volume keywords and high-frequency prompts. Optimize for both appearance AND relevance.

Establish Your Baseline: The Golden Prompt Set

Before tuning any metadata, assemble a labeled dataset. This comes directly from OpenAI's official guidance.

Why start here? You can't optimize what you can't measure. The golden prompt set becomes your testing framework—the benchmark you'll use to evaluate every metadata change you make. Without it, you're guessing.

When do you create this? Build your golden prompt set during development, before you submit your app. You'll use it for:

• Pre-submission testing – Validate your metadata works before you submit
• Submission test cases – OpenAI requires 5+ test cases; your golden set becomes the foundation
• Regression testing – Every time you update metadata, rerun the same prompts to track improvements
• Production monitoring – Periodically replay your prompts to catch metadata drift

The three categories:

1. Direct prompts – Users explicitly name your product or data source.

• "Use Notion to create a task called X"
• "ChatGig, create a gig for this contract"

2. Indirect prompts – Users describe the outcome they want without naming your tool.

• "I need to organize my notes"
• "Help me find a freelancer to finish this contract"

3. Negative prompts – Cases where built-in tools or other connectors should handle the request.

• "Tell me about the best project management tools"
• "What's the history of the gig economy"

Document the expected behavior for each prompt (should your tool trigger, stay silent, or defer to another tool?). This set becomes the backbone of your entire optimization process.

How App Store Search Works

The ChatGPT App Store uses traditional search mechanics. Keywords in your metadata determine where you appear when users search.

What to Optimize

App Name

Include your most important keyword. This is prime real estate.

Description

Write naturally for users, but include relevant keywords. Your description appears in search results.

Starter Prompts

These show up in search results. Use them to showcase your best use cases with relevant keywords.

• ✅ "Create a presentation from my notes"
• ❌ "Use my app"

Screenshots

Must be exactly 706 pixels wide (per submission requirements). Show:

• Your best use cases
• The actual prompts users should try
• Clear visual examples of output

Our observation: Every screenshot should include the prompt that triggers it. Users see "Create a gradient tweet" → screenshot → understand exactly what your app does.

Test Cases for Submission

OpenAI requires at least five test cases for submission, showing:

• Scenario
• Prompt
• Tool that will be triggered
• Expected output

They also test negative cases—prompts where your app should NOT trigger.

Our observation: Use your golden prompt set as the foundation for test cases. You've already tested these—just format them for submission.

This is where the official OpenAI documentation provides the most guidance. Everything in this section is grounded in their Optimize Metadata guide.

How Organic Recommendations Work

ChatGPT decides when to call your app based on the metadata you provide. Well-crafted names, descriptions, and parameter docs increase recall on relevant prompts and reduce accidental activations.

"Treat metadata like product copy—it needs iteration, testing, and analytics."
— OpenAI Apps SDK

Our observation: Organic discoverability was briefly enabled, then disabled after users thought the suggestions were ads. When it returns, apps with optimized metadata will have a significant advantage.

What Affects Prompt Discoverability

Beyond your metadata, several contextual factors influence when and how consistently your app appears:

Prior usage creates preference. If a user has used your app before, ChatGPT is more likely to suggest it again for related prompts. Your first impression matters—get users to try your app once, and you've increased your future discoverability with that user.

Memory and context matter. ChatGPT's memory about user preferences and past activities can influence which apps surface. A user who frequently works on presentations may see design apps suggested more often.

Conversation depth affects suggestions. Apps may surface differently at turn 1 of a conversation versus turn 10. The accumulated context changes what ChatGPT considers relevant.

Consistency matters as much as appearance. It's not just whether your app appears—it's how reliably. An app that appears 80% of the time for a relevant prompt has more dependable discoverability than one that appears 40% of the time. When testing, track variance across multiple runs, not just single appearances.

These factors mean discoverability isn't fully within your control—but your metadata is. The sections below focus on what you can directly optimize.

Writing Your Tool Metadata

1. Name Structure: domain.action

Official guidance: "Pair the domain with the action."

The format tells the LLM semantically what your tool does.

2. Description Clarity: "Use when" + "Do not use"

Official guidance: "Start with 'Use this when…' and call out disallowed cases."

Bad:

"Queries the database"

Good (Airtable-style):

"Use this when the user wants to update existing records in an Airtable table. To get baseId and tableId, you must first use the search_bases and list_tables_for_base tools. Do not use for creating new records—use create_records instead."

The description does two jobs: tell ChatGPT when to call you, and when NOT to call you.

3. Parameter Documentation with Examples

Official guidance: "Describe each argument, include examples, and use enums for constrained values."

{
  "name": "search_query",
  "type": "string",
  "description": "Company identifier to search for. Use ticker symbol (preferred) or company name. Examples: 'AAPL', 'MSFT', 'Tesla Inc', 'Amazon.com'"
}

The more specific you are, the better ChatGPT understands how to fill parameters from natural language.

4. Tool Hints

Official guidance: Use these annotations to help ChatGPT handle your tools appropriately:

• readOnlyHint: true – For tools that never mutate state (streamlines confirmation)
• destructiveHint: false – For tools that don't delete or overwrite user data
• openWorldHint: false – For tools that don't publish content or reach outside the user's account

5. Going Beyond Basic Descriptions

The best ChatGPT apps treat tool descriptions as runtime instructions, not API documentation. The LLM reads them at execution time, so write them like you're instructing a junior developer who needs to know exactly what to do.

Here are patterns that significantly improve how ChatGPT handles your tools:

Enforce prerequisites
If your tool needs data from another tool first, say so explicitly:

"Before calling this tool, you MUST first use the query tool to get the record ID."

Show negative examples
Telling ChatGPT what NOT to do is surprisingly effective. Most descriptions only show happy paths:

"NOT: discover_companies('Apple Inc.') — use ticker symbol like 'AAPL' instead"

Define fallback strategies
Don't assume ChatGPT will retry intelligently. Tell it what to try when the first approach fails:

"1. PRIMARY: Search by ticker symbol. 2. SECONDARY: Search by company name (only if ticker fails). 3. FALLBACK: Try alternative name forms"

Encode pause points for user confirmation
When you need user input before proceeding:

"Present the available options to the user and ask which one they want before proceeding."

Real example from Salesforce's Agentforce:

The summarize_conversation_transcript tool description is ~500 words and includes:

"CRITICAL WORKFLOW: Before calling this tool, you MUST follow these steps: 1) If call ID is not known, use the soql_query tool to query BOTH VoiceCall AND VideoCall entities in SEPARATE queries..."

It goes on to specify output formatting rules, PII guardrails, and instructions for handling edge cases. This isn't documentation—it's a system prompt hiding in a tool description.

Setup

You need ChatGPT Plus ($20/mo) for Developer Mode:

• Settings → Apps & Connectors → Advanced Settings → Enable Developer Mode

Use ngrok for local testing or deploy your MCP server publicly.

Process (from Official Docs)

1. Link your connector
Add custom connector, paste MCP URL, complete OAuth if needed.

2. Run your golden prompt set
For each prompt, track:

• Precision: Did the right tool run?
• Recall: Did the tool run when it should?

3. Change ONE thing at a time

"Change one metadata field at a time so you can attribute improvements."

4. Keep a log with timestamps

"Keep a log of revisions with timestamps and test results. Share diffs with reviewers to catch ambiguous copy before you deploy it."

After each revision, repeat the evaluation. Aim for high precision on negative prompts before chasing marginal recall improvements.

Production Monitoring (from Official Docs)

Once your connector is live:

• Review tool-call analytics weekly. Spikes in "wrong tool" confirmations usually indicate metadata drift.
• Capture user feedback and update descriptions to cover common misconceptions.
• Schedule periodic prompt replays, especially after adding new tools or changing structured fields.

"Treat metadata as a living asset. The more intentional you are with wording and evaluation, the easier discovery and invocation become."

What Works Well

Fixed templates with clear outputs
Diagrams, charts, forms. Anything where the format is predictable and the output is clear.

Workflow handoffs to external services
Natural transition points from "working in ChatGPT" to "need an external service." The key is preserving context across that handoff.

Eliminating context switching
Users are already in ChatGPT. Anything that keeps them there instead of forcing them to open another tab is valuable.

What to Avoid

Long-form or static content
If users need to scroll through pages of content, that's not a chat experience.

Complex multi-step workflows
If your workflow requires 10 steps with conditional branching, the chat interface probably isn't optimal.

Heavy visual browsing
Shopping, design exploration, anything where users need to see 50 options at once. The window is too limited.

Duplicating ChatGPT's system functions
Don't rebuild text analysis or summarization. Find the gaps where ChatGPT needs external data or specialized functionality.

Our Observations & Testing

The following are based on our testing and experience, not official documentation:

• App Store current state observations
• Organic discoverability status observations
• Screenshot dimension requirements (706px wide)
• Specific submission process details
• Use case recommendations (what works/what to avoid)
• Analysis of how major B2B apps structure their tool descriptions

Success isn't just about building a great product. It's about understanding the metadata game and how the discovery algorithm works. You can build the best app in the world, but if ChatGPT doesn't know when to suggest it, you'll get zero users.

Never depend entirely on this platform. Build your own distribution, capture emails, have an exit plan. Platforms always consolidate control eventually.

Optimize for both channels. Most developers focus on App Store search. The winners optimize for both search rankings AND prompt optimization.

Start tracking now. Document your metadata changes. Track which prompts trigger your app. Build your own historical baseline before the ecosystem matures.

Metadata is a living asset. The more intentional you are with wording and evaluation, the easier discovery and invocation become.

Last updated: January 2026