An MCP server implementing the ROBINS-I V2 framework for risk-of-bias assessment in non-randomized studies, with deterministic algorithms and full provenance. It enables users to parse study documents, specify target trial results, answer signalling questions with evidence-bound quotes, and compute or override domain judgements.
Enables scientific due diligence by grading claims against public literature, clinical trials, and filings, with explicit citations and optional attestation.
Enables multi-tier AI-detection screening on academic papers by extracting text from .tex and .docx files, splitting into standard sections, and running a pipeline of statistical and LLM-based analysis.
An MCP server that evaluates whether retrieval methods and AI outputs are grounded in long narrative manuscripts by retrieving evidence and scoring coverage deterministically, without external model APIs. It provides tools for chunking, indexing, retrieval, and evaluation.
