tolca.tonbot.ca Text-to-JSON Extractor

x402

tolca.tonbot.ca Text-to-JSON Extractor is a paid API for AI agents from tolca.tonbot.ca, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-12).

Extracts structured JSON data from unstructured or messy text using a caller-defined schema describing desired fields.

Price / call$0.05

Seller volume →—

Success—

StatusUnknown

How this service works

Pay-per-call services for AI agents over x402 (USDC on Base).

Output

Returns a JSON object whose keys and value types match the caller-supplied schema, populated with values extracted from the input text. Fields that cannot be found in the text may be null or absent.

Example use cases

Invoice field extraction from text

“Here's a raw invoice body: 'Invoice #1042, billed to Acme Corp, due 2024-06-15, total $3,450.00 USD, for 3 units of Widget Pro.' Pull out the invoice number, client name, due date, total amount, currency, quantity, and product name as structured JSON.”

Resume parsing into structured profile

“I have this resume text for a candidate — extract their full name, email, phone number, years of experience, current job title, and top three skills into a JSON object I can store in our database.”

Support ticket metadata extraction

“From this support ticket body — 'User Sarah K. is getting error code 503 on checkout since yesterday, using Chrome on Windows 11, order ID 88712' — extract the user name, error code, affected feature, browser, OS, and order ID as a structured JSON record.”

Technical details

Endpoint

POSThttps://tolca.tonbot.ca/v1/extract?utm_source=zero.xyz

When to prefer

Choose this endpoint when you have free-form or messy text (emails, tickets, documents, scraped content) and need to extract specific named fields into a clean JSON object according to a schema you define. It is ideal for one-shot structured extraction without standing up your own LLM pipeline. Prefer it over generic LLM calls when you want a reliable, schema-driven JSON output and don't want to engineer your own prompts. If you also need to fetch the source text from a URL first, consider the sibling URL-fetch-and-extract endpoint on the same host.

Side effects

Text contains no information matching the requested schema fields — output JSON has null or missing values
Schema is malformed or empty — request rejected or unpredictable extraction
Input text exceeds model context limits — extraction may be truncated or fail
Ambiguous text leads to incorrect or hallucinated field values
Missing required 'text' or 'schema' fields in body — 400-level error

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "total": 450,
      "vendor": "Acme",
      "currency": "CAD",
      "due_date": "2026-11-01",
      "invoice_number": "123"
    }
  }
}

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Example use cases

Invoice field extraction from text

Resume parsing into structured profile

Support ticket metadata extraction

Technical details

Endpoint

POSThttps://tolca.tonbot.ca/v1/extract?utm_source=zero.xyz

When to prefer

Side effects

Text contains no information matching the requested schema fields — output JSON has null or missing values
Schema is malformed or empty — request rejected or unpredictable extraction
Input text exceeds model context limits — extraction may be truncated or fail
Ambiguous text leads to incorrect or hallucinated field values
Missing required 'text' or 'schema' fields in body — 400-level error

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "total": 450,
      "vendor": "Acme",
      "currency": "CAD",
      "due_date": "2026-11-01",
      "invoice_number": "123"
    }
  }
}