"author:Alpha-Park" matching MCP servers:

Enables statistical A/B testing by computing Welch's two-sample t-test, Satterthwaite degrees of freedom, and two-tailed p-values, alongside time-series forecasting, anomaly detection, regression, and PCA through MCP tools.

Enables autonomous agents to cross-reference factual claims against multiple source documents, detect ungrounded or hallucinated statements, and return normalized confidence scores via the verify_claim tool.

Enables users to calibrate multimodal chart coordinates and recover tabular datasets from visual bar, line, and scatter charts as structured numbers.

Enables users to model indoor CO2 mass balance, estimate cognitive impairment from exposure, and schedule ventilation purges through an MCP interface.

Enables AI agents and MCP clients to heuristically parse multi-column document dumps, aligned spacing, and CSV/TSV input, converting them into clean GitHub Flavored Markdown tables and typed JSON. Runs fully offline on the Python standard library with zero external dependencies, returning deterministic results without LLM round-trips.

This MCP server enables users to analyze personal reading and media streams by computing Shannon entropy, detecting Jaccard redundancy and echo chambers, and ranking content sources by decibel SNR to produce pruning recommendations and high-signal curation.

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 analyze and link citations across multiple research sources, building cross-citation graphs and resolving consensus from conflicting findings. It exposes this capability as a deterministic, zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP-compliant clients.

Enables AI agents to perform deep research across multiple sources, arbitrating consensus while evaluating source authority and detecting contradictions. It integrates via MCP with clients like Claude Desktop and Cursor to deliver deterministic, structured research dossiers.

Enables users to audit academic citation contexts, calculate scientific consensus ratios, and generate credibility badge indexes for verified citations through MCP-compliant tools.

Serves as an MCP server that resolves academic DOIs to canonical identifiers and generates BibTeX records via CrossRef.

Enables decomposition of complex, multi-faceted research prompts into executable query plans and synthesis DAGs, with support for scientific evidence analysis and consensus calculation.

Enables multi-provider waterfall data enrichment for B2B prospects, firmographics, and verified decision-maker corporate emails via MCP.

Enables autonomous multi-study scientific paper analysis, consensus ratio calculation, citation credibility verification, and multi-hop research queries through the Model Context Protocol.

Enables auditing scientific papers for methodological biases such as selection bias and p-hacking, and assessing citation credibility and research consensus.

Enables running multi-perspective synthetic consumer focus groups that simulate persona interviews to gauge market sentiment. Delivers deterministic, JSON-schema-validated results over the Model Context Protocol for use in autonomous research pipelines and multi-agent workflows.

Enables aspect-level sentiment mining of customer reviews, producing satisfaction radar breakdowns and structured sentiment analysis through MCP-compatible clients.