Provides a unified interface to Arize Phoenix's capabilities for managing prompts, exploring datasets, and running experiments across different LLM providers

A visualization mcp contains 25+ visual charts using @antvis. Using for chart generation and data analysis.

Provides a JSON manipulation interface using JSONPath syntax for querying, transforming, and analyzing structured data across diverse datasets.

Enables Excel file operations and data analysis with tools for statistical analysis, data filtering, pivot table creation, and visualization through charts and plots.

Provides automated hyperparameter optimization and analysis using Optuna framework with support for multiple samplers, multi-objective optimization, parameter importance analysis, and interactive visualizations including optimization history and Pareto fronts.

Integrates with Kaggle's API to enable competition participation, dataset management, kernel operations, and model submissions for data scientists and machine learning practitioners.

Analyzes project directories to extract code structure and symbols using Tree-sitter parsers, providing tools for generating directory trees and performing deep code analysis of JavaScript, TypeScript, and Python files.

Provides natural language access to single-cell RNA sequencing analysis through Scanpy, enabling bioinformatics workflows like clustering, dimensionality reduction, and cell type annotation without writing code.

MCP server for Constraint Solving and Optimization

Interfaces with Cube.dev's REST API to enable querying and describing data from Cube deployments, simplifying access to complex data structures for automated analysis and natural language exploration of business metrics.

Integrates with Spring Boot to process CSV and Excel files, enabling data extraction and manipulation for analysis, reporting, or system integration tasks.

Integrates Spring AI components with AI models to enable natural language processing, machine learning, and data analysis within Spring-based applications.

Enables advanced econometric analysis by providing R-based statistical modeling capabilities for researchers and data scientists, supporting complex regression techniques, panel data modeling, and diagnostic testing across diverse research domains.

Enables AI to query and analyze data through DuckDB, an in-process analytical database optimized for OLAP workloads with direct support for CSV, Parquet, and JSON files including remote sources like S3.

Enables Excel file interaction with capabilities for retrieving cell values, searching ranges, examining formatting and formulas, and listing worksheet information for data analysis workflows.

Provides a natural language interface to MLflow tracking servers for querying and managing machine learning experiments and models through standardized tools for listing models, exploring experiments, and retrieving detailed information.

Integrates with the Hugging Face Dataset Viewer API to enable exploration, analysis, and content retrieval of datasets hosted on the Hugging Face Hub.

Integrates with WolframAlpha to enable computational knowledge queries, scientific calculations, and data analysis across diverse disciplines.

Execute SQL queries and analyze data in DuckDB databases.

Provides a Bayesian Monte Carlo Tree Search engine for systematic exploration of different angles and interpretations, balancing exploration versus exploitation through probabilistic approaches that evolve through multiple iterations.

Enables document processing with OCR, vector embeddings, and semantic search capabilities for efficient retrieval-augmented generation across multiple file formats including PDF, DOCX, PPTX, Excel, CSV, and images.

Provides a PyTorch CI/CD analytics API for investigating build failures, analyzing test flakiness, and monitoring performance trends across the PyTorch infrastructure.

Integrates with Biomart to enable advanced biological data queries, including gene information retrieval, ID translation, and dataset exploration using the pybiomart package.

Indexes and retrieves code snippets using embedding models and vector databases, enabling efficient code search and analysis for software development tasks.

Enable Similarity-Distance-Magnitude statistical verification for your search, software, and data science workflows.

Train linear regression models.