Blinka Machine Mouths – Structured Extraction

x402

Blinka Machine Mouths – Structured Extraction is a paid API for AI agents from blinka-machine-mouths.netlify.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).

Extracts structured, schema-validated data from raw text using a local stateless model, with evidence citations and uncertainty flags.

Price / call$0.01

Seller volume →—

Success—

StatusUnknown

How this service works

Bounded stateless machine services plus public procurement discovery. No private Mika memory or arbitrary tool execution.

Output

Returns a JSON object with: ok (boolean), data (extracted fields validated against the provided schema), model identifier, evidence (per-field citation snippets from source text), retained (always false — stateless), and uncertain (list of fields the model could not confidently extract).

Example use cases

Parse procurement email into schema

“Extract the vendor name, contract value, and deadline from this procurement notice text and return it as structured JSON: 'Vendor Acme Corp is awarded a contract of $45,000 for delivery by March 31, 2025.' Use the schema {vendor: string, contract_value_usd: number, deadline: string}.”

Structure customer form submission

“I have this raw form response: 'My name is Jordan Lee, I need 500 units shipped to Austin by end of month, total spend approved is $8,200.' Extract name, units, destination, and budget_usd from it and give me the evidence for each field.”

Extract job applicant details from cover letter

“Here's a cover letter text — can you pull out the candidate's name, years of experience, and desired salary into a JSON object? Text: 'I'm Alex Rivera, with 7 years in data engineering and looking for a role around $130,000 per year.' Schema: {name: string, years_experience: number, desired_salary_usd: number}.”

Technical details

Endpoint

POSThttps://blinka-machine-mouths.netlify.app/structure?utm_source=zero.xyz

When to prefer

Choose this endpoint when you have raw text already in hand and need to reliably extract typed, schema-validated fields with evidence citations and uncertainty flags — especially when hallucination or data invention is unacceptable. Prefer it over general LLM extraction when you need stateless, reproducible, evidence-backed structured output against a strict JSON Schema. Not suitable for fetching URLs, generating new content, or tasks requiring memory or tool execution.

Side effects

Source text exceeds 12,000 characters — request rejected
Extracted data fails to validate against provided schema — may return partial data or error
Field genuinely absent from source text — listed in uncertain array rather than invented
Malformed JSON Schema input — extraction fails with schema error
Ambiguous instruction leads to low-confidence fields flagged in uncertain

Input

Schema · Request body

Field	Type	Description
text	string	Untrusted source text already in hand. Max 12,000 characters. No URLs are fetched.
schema	object	JSON Schema the returned data must validate against.
instruction	string	Optional extraction focus. It cannot authorize invention of facts.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "ok": true,
    "data": {
      "name": "Jamie",
      "budget_usd": 250
    },
    "model": "local-structured-extractor",
    "evidence": {
      "name": "Jamie emailed",
      "budget_usd": "Budget is $250"
    },
    "retained": false,
    "uncertain": []
  }
}

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