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Fine-Tuning Quality Scorer MCP
x402
Fine-Tuning Quality Scorer MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.22/call, status unknown (last checked 2026-10-12).
Scores the quality of training data samples to determine their suitability for fine-tuning language models
Price / call$0.22
Success—
StatusUnknown
How this service works
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Output
Returns a quality score (likely a numeric value or rating) indicating how suitable the submitted text data is for use as a fine-tuning training sample, potentially accompanied by qualitative feedback or confidence indicators.
Example use cases
Curating a fine-tuning dataset
“I have a batch of instruction-response pairs I want to fine-tune a model on — can you score the quality of this one: 'User: Summarize this article. Assistant: Sure! The article discusses...' and tell me if it's worth keeping?”
Filtering low-quality training examples
“Before I kick off my fine-tuning run, can you evaluate this training example and give me a quality score so I can decide whether to include it or throw it out?”
Benchmarking data quality across samples
“I'm building a high-quality SFT dataset and need to assess this sample: 'User: Write a Python function to reverse a string. Assistant: Here is a simple function...' — score it for fine-tuning quality so I can compare it against my other examples.”
Technical details
Endpoint
POSThttps://api.m2mcent.com/fine-tuning-quality-scorer-mcp/api/process?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need an automated, cost-efficient signal for ranking or filtering training data samples prior to an LLM fine-tuning job. It is especially useful when curating large instruction-following or RLHF datasets and you want to remove noisy or low-quality examples programmatically without manual review.
Side effects
- Empty or null data field returns an error or zero score
- Extremely long input strings may be truncated or rejected
- Malformed JSON input returns a 400-level error
- Payment not completed results in 402 response blocking access
- Ambiguous or very short samples may yield low-confidence scores
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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