Content Score
by SHTechUpdated May 4, 2026
Computes numerical scores for input text using ML models, evaluating metrics like readability, SEO suitability, sentiment, and toxicity. Developers integrate it via MCP to assess user-generated content in apps; content teams apply it for pre-publish quality checks in blogs and social media.
content-scoring
text-analysis
ml-evaluation
|Overview
The Content Score MCP server (content-score-mcp) exposes machine learning-based content evaluation through the Model Context Protocol. It processes text inputs and returns quantitative scores on key quality dimensions, enabling automated analysis without custom model training.
Key Capabilities
- Content scoring algorithms: Analyzes text for readability (Flesch-Kincaid), SEO factors (keyword density, structure), sentiment polarity, and toxicity probability, outputting normalized scores (0-1 or 0-100 scales).
- Batch processing support: Handles multiple texts in single requests for efficiency in high-volume scenarios.
- Configurable thresholds: Allows custom weighting of metrics for domain-specific evaluations.
Use Cases
- User-generated content moderation: Feed forum posts to the scoring engine; reject items below 0.6 toxicity threshold.
- SEO content optimization: Score blog drafts for keyword effectiveness and readability before publishing.
- Marketing copy review: Evaluate ad texts for sentiment alignment and engagement potential in campaigns.
- App feedback analysis: Score customer reviews to prioritize high-quality inputs for training data.
Who This Is For
- Developers building AI apps needing inline text quality gates.
- Content creators and editors automating manuscript reviews.
- Data analysts processing large text corpora for insights.
- Platform owners implementing scalable moderation pipelines.