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Screen academic papers for AI-generated signals with section-based multi-layer analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-ai-detection" yet — see the docs or source repo.
Analyze this .docx academic paper for AI-generated signals. Extract the full text, split it into abstract, introduction, methods, results, discussion, and references, then use statistical features and LLM judgment to output section-level risk scores, suspicious indicators, and an overall conclusion.
A section-by-section detection report with risk scores, rationale, and an overall assessment.
Read this .tex paper, identify standard sections, and compare AI-detection risk across them. Highlight the most suspicious passages and explain whether statistical language features or semantic judgment drove the higher risk.
A ranked comparison of section risks, highlighting high-risk passages and reasons.
Before submission, help me check whether this paper contains obvious AI-writing signals. Generate a concise report with the overall risk level, the sections most in need of revision, and editing suggestions to reduce false-positive risk.
A concise pre-submission report with risk level and revision suggestions.
Analyze local paper PDFs rigorously and generate editable maps and research insights.
Search papers, parse full-text PDFs, extract details, and manage citations.
Enable AI to extract text, analyze structure, and understand documents deeply.
Search, download, and extract academic papers from many sources in one workflow.
Convert research PDFs to Markdown and search them with grep plus semantics.
Detect AI-generated content, check plagiarism, analyze images, and compare texts.