How to Set Up Metadata for Indirect Prompting.

How to Set Up Metadata for Indirect Prompting.

If you look at how users behave nowadays, you’ll see that most of the time they describe a need to an LLM and get an instant answer from AI. That shift changes how businesses and tools get discovered today, and it demands new approaches to visibility and marketing.

Once you’ve built your MCP app and it’s been approved in ChatGPT, indirect prompting is the next thing to get right. It’s the ability for your app to appear automatically when a user describes what they need without them typing your app’s name. Skip this, and your app only shows up when users already know to call it directly.

The fix is in how you configure tool metadata.

Why indirect prompting matters more than direct calls

The way people find tools and services is shifting fast. Future AI Sentiment Wave 3 report found that about 55% of users now turn to AI chatbots for tasks they previously would have asked Google to help with. Another survey confirms that traffic from generative AI platforms is growing 165 times faster than traditional search.

People describe what they need and let the AI decide what to use.

That’s the problem for most MCP apps. Providers will need to make sure their tooling is easily discoverable and differentiated enough for the agent to pick for a particular task. If your tool metadata doesn’t give the model enough context, it won’t pick your app, even if your app is the best fit.

The first step to getting that right is knowing exactly when your app should and shouldn’t show up.

Start by defining when your app should activate

Before writing a single line of metadata, map out the scenarios your app is built for. Define:

• 3-5 prompts where users should trigger your app (without naming it)
• 1-3 prompts where your app should stay out of the way

For example, if your app helps users find auto parts by VIN code, the indirect trigger might be "Which brake pads fit a BMW 3 Series?" — the user isn’t asking for your app by name, but your app is the right tool.

If you can’t describe when your app should fire, the model won’t figure it out either.

Write tool descriptions that help the model choose your app

With your prompt map in hand, the next step is encoding it into your tool descriptions. Add 1–3 sentences to each tool explaining what the app does, who it’s for, and why it’s better than the model answering alone. For example: "This app generates a meal plan for the user based on a selected calorie target and macronutrient ratio."

Then structure your tool descriptions to include three types of prompts:

• Direct prompts — examples where the user explicitly names your product or data source, like "Get me a report from [YourApp]."
• Indirect prompts — examples where the user describes the outcome they want without naming your tool, like "What should I eat to hit 2,000 calories with high protein?"
• Negative prompts — cases where built-in tools or other connectors should handle the request, like a nutrition app that shouldn’t activate when someone asks "What’s the weather in Kyiv?"

Tips for writing tool metadata that actually works

Getting indirect prompting right is mostly a metadata problem. Recognizing that there are no established guides on how to do it properly, we’ve prepared our own list of tips based on both official guidelines and real testing.

Keep each tool focused on a single job

Each tool should handle one read or write action. This helps the model choose between alternatives correctly and reduces the chance of it picking the wrong tool or skipping yours entirely.

When naming your tools, always use a verb that describes the action: search_products, create_invoice, get_order_status. Even if two methods are functionally similar, their names should be clearly distinguishable — search_by_name vs. search_by_id tells the model exactly when to use which.

Start with the simplest inputSchema possible. Use LLM-friendly parameter names, define data types and enums, and document which fields are optional or nullable. A simpler schema means fewer model errors and faster adaptation during tuning.

Give the model enough context to pick your tool

For indirect prompts, the model needs enough signal in your tool description to choose your connector over a built-in alternative. Add 1–3 sentences explaining what your tool does and why it produces better results than the model answering alone. Use words like "exact," "precise," or "explicit" wherever they apply — these signal accuracy and give the model more confidence in selecting your tool.

For a deeper look at how this selection process works, see how ChatGPT chooses which apps and tools to show.

Plan for edge cases and separate read from write

If your app needs both read and write behavior, create separate tools for each. This lets ChatGPT respect confirmation flows before write actions are executed, which matters both for user trust and for avoiding unintended actions.

Also define what happens when things go wrong. If a tool might return an empty result or crash, specify what it should do next. Unhandled edge cases often look like broken behavior to the model, and it may stop calling your tool altogether.

Track every change so you know what’s working

Metadata tuning is iterative, and without a log you’re flying blind. After each update, record what changed and how your key prompts responded. A simple table works well. List your prompts in rows, your commits in columns, and fill in results after each test.

Don’t push changes too frequently either. The model needs time to adapt, so limit yourself to around two commits per day before running tests. If you’re already tracking traffic from ChatGPT to your app, that data can tell you a lot about whether your tuning is moving in the right direction.

Final line

As you can see, indirect prompting is the result of deliberate metadata work. Define when your app should activate, write descriptions that give the model enough context to choose it, and track what changes after each update. The apps that get discovered consistently are the ones that communicate their purpose most clearly.

If you haven’t submitted your app yet, start there first. If you’re already live, this is the next lever worth pulling.