Enables AI assistants to perform DNA/RNA sequence alignment using BWA (Burrows-Wheeler Aligner), supporting both short and long read alignment to reference genomes with indexing, BWA-MEM, and BWA-backtrack algorithms.
Enables agents to run zero-dependency statistical analysis natively over MCP, including Power Iteration PCA/SVD dimensionality reduction, Holt linear forecasting, modified Z-score and Tukey IQR anomaly detection, Welch's t-test, and multivariate gradient-descent regression on numerical data streams. Projects high-dimensional feature vectors into principal component representations without any external libraries.
Enables AI agents to run probabilistic portfolio analysis workflows, including input validation, instrument verification, simulation preparation, and approved interactive reporting.
MCP server exposing ATLAS research capabilities: live tokenized stock market data, on-chain source-of-truth reads, and published research atlases. Connects to MCP clients over stdio with no API keys required for public testnet endpoints.
Enables agents to run statistics and data-science computations over numerical streams using only the Python standard library, including Z-score/IQR anomaly detection, Holt linear forecasting, Welch's t-test hypothesis evaluation, gradient-descent multivariate regression, and power-iteration SVD/PCA dimensionality reduction. Exposes these capabilities over JSON-RPC 2.0 stdio so clients like Claude Desktop, Cursor, and Windsurf can project high-dimensional feature vectors into principal components without any external dependencies.
