Blinka Python Error Diagnosis

x402

Blinka Python Error Diagnosis 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 2026-10-10).

Analyzes Python source code and error messages to diagnose the root cause and suggest a fix, without executing any code.

Price / call$0.01

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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 a natural-language diagnosis of the error, a suggested fix, the key evidence excerpt from the code or error, a confidence score (0–1), and flags confirming the code was never executed and no data was retained.

Example use cases

CI pipeline failure root cause

“My automated test suite just failed with this traceback: 'AttributeError: NoneType object has no attribute split'. Here's the Python function it's running — the intent was to parse an optional config value and split it by commas. Can you diagnose what's causing it and suggest how to fix it?”

Beginner debugging assistance

“I'm learning Python and my script keeps crashing with a KeyError on a dictionary lookup. Here's my code and the full error message — I was trying to get a user's email from a response dict. What's actually going wrong and how should I fix it?”

Production exception triage

“We're seeing a recurring TypeError in production: 'unsupported operand type(s) for +: int and str'. Here's the function and the exception. The intent is to concatenate a numeric ID with a string prefix for logging. Can you diagnose the root cause and tell me how confident you are in the fix?”

Technical details

Endpoint

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

When to prefer

Choose this endpoint when you need a fast, stateless, read-only analysis of a Python error without any code execution risk. It is ideal for CI/CD pipelines, agent-assisted debugging loops, or beginner support flows where safety and privacy (no code retention) are paramount. Prefer it over general-purpose LLM calls when you want a structured, confidence-scored diagnosis with explicit evidence anchoring rather than a freeform explanation.

Side effects

Code or error input exceeds character limits (16,000 / 4,000 chars respectively) — request rejected
Ambiguous or minimal code snippet with no clear error context — low confidence diagnosis returned
Unrelated or non-Python language submitted despite language field — may return unhelpful diagnosis
Network or payment failure — HTTP 402 or 5xx returned with no diagnosis

Input

Schema · Request body

Field	Type	Description
code	string	Python source associated with the observed failure. Max 16,000 characters; never executed.
error	string	Observed Python error, exception, traceback, or failure message. Max 4,000 characters.
intent	string	Optional intended behavior used to disambiguate the diagnosis. Max 1,800 characters.
language	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "ok": true,
    "service": "blinka-python-error-diagnosis",
    "evidence": "ZeroDivisionError: division by zero",
    "executed": false,
    "language": "python",
    "retained": false,
    "diagnosis": "The function divides by b without handling the zero-denominator case shown by the supplied error.",
    "confidence": 0.95,
    "primary_issue": null,
    "suggested_fix": "Define the intended zero-denominator policy and guard b == 0 before division."
  }
}

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