jsontoschemaStop wasting tokens on raw JSON
LLMs don't need your data to understand your data's structure. A simplified schema gives them the same context at a fraction of the cost.
The problem with raw JSON in prompts
When you include a JSON payload in an LLM prompt, the model tokenizes every key, every value, every bracket. A typical API response with 50 user objects means the same field names repeated 50 times. The model sees "email" fifty times but only needs to see it once to understand the structure.
48,000+
tokens for a typical API response
<500
tokens for the same data as a schema
What the LLM actually needs
For most LLM tasks (generating code against an API, writing documentation, building queries) the model needs the shape of the data, not the data itself. Field names, types, nesting, which fields are optional. That's it.
A simplified schema captures all of that in a format that's both human-readable and token-efficient.
{
"users": [
{ "id": 1, "name": "Alice",
"email": "alice@co.com",
"role": "admin" },
{ "id": 2, "name": "Bob",
"email": "bob@co.com",
"role": "user" },
... 200 more items
]
}{
users: [{
id: number,
name: string,
email: string,
role: string
}]
}Where this matters most
System prompts
When your system prompt includes API response shapes for function calling or tool use, a schema keeps your prompt lean and leaves room for actual conversation.
RAG pipelines
If you're injecting retrieved JSON documents into context, converting them to schemas first means you can fit more documents in the same context window.
Code generation
Ask an LLM to write a parser, a form, or a migration based on a JSON response. The schema gives it everything it needs without the noise.
Cost control
API pricing is per-token. Cutting 48,000 tokens to 500 per request adds up fast when you're processing thousands of calls per day.
How it works
Paste your JSON into the tool. The converter analyzes the entire structure: it replaces values with their types, collapses arrays to a single representative item, marks optional fields with ?, and surfaces mixed types like string | null.
The output is valid enough to drop directly into a prompt. No extra formatting or explanation needed. LLMs parse it naturally because it looks like the JSON they were trained on.
Common questions
Does the LLM lose accuracy without seeing real values?
For structural tasks (code generation, API integration, data modeling), no. The model needs types and field names, not sample data. For tasks that depend on content (analysis, summarization), you still need the actual data.
Can I include a few example values alongside types?
Yes. Toggle "Include examples" in the tool to get output like "admin" | "user" | string. This gives the LLM both the structure and a hint about what the values look like.
See the token savings
Paste any JSON payload and watch the token count drop. The tool shows the exact reduction.