"Python Object-Oriented Programming Resources" matching MCP servers:

This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.

A Python implementation of the Model Context Protocol (MCP) server that enables searching and extracting information from arXiv papers, designed to be extensible with additional MCP tools.

Enables secure execution of statistical Python code in isolated micro-VM sandboxes, generating charts and analytical summaries.

Enables AI agents to hire verified human operators for tasks requiring physical presence, human perception, or judgment, such as real-world verification, product testing, and data collection.

Provides AI agents with real-time access to corporate credit data, including debt structures, bond pricing, and guarantor chains extracted from SEC filings. It enables complex financial analysis such as screening companies by leverage, tracing corporate hierarchies, and searching covenant language.

Enables time series analysis following Box-Jenkins-Treadway methodology, supporting guided or autonomous modes for model identification, estimation, and diagnosis via an LLM.

Enables AI assistants to load CSV datasets, compute summary statistics, filter rows, rank columns, and compute correlations through the Model Context Protocol.

Enables analysts to build and evaluate simultaneous VARMA models for systems of time series via exact maximum likelihood on the ATSW ladder, using univariate models as the yardstick. It guides users through loading univariate fits, identifying residual cross-correlations, estimating joint models, forecasting, impulse responses, variance decomposition, and recording decisions.

An MCP server for the NASA Astrophysics Data System (ADS) that enables searching, citation graph traversal, and bibliography management for astrophysics literature. It provides tools for computing citation metrics, resolving astronomical object names, and managing paper libraries through natural language.

A safety-bounded MCP server for discovering data-acquisition devices and performing finite analog-voltage and digital-input reads through NI's nidaqmx Python package.

Local MCP adapter for public Stock Trends API resources, enabling credential-free access to public resources and a workflow cost estimation tool. It does not generate investment advice; it only translates published API responses for MCP clients.

Enables MCP hosts to conduct goal-oriented question-selection conversations by starting or resuming sessions, asking pending questions, recording replies or context, adjusting known data, and submitting business feedback through the NBQ API.

An intelligent research assistant MCP server for AI agents, providing task-oriented literature search and analysis across multiple academic databases.

Enables read-only research on YouTube by exposing video metadata, transcripts, comments, search, channels, playlists, and trending data through four task-oriented tools.

A Python MCP server providing 11 tools for querying KenPom basketball analytics, including efficiency ratings, game predictions, four factors, and more through Claude.

Stop your AI agent from rebuilding what already exists. MCP server that checks GitHub, npm and Python repos for maintained alternatives before you scaffold.

Enables LLMs to load, simulate, modify, and generate what-if scenarios for EPANET water-distribution network models through natural language, leveraging the ePyT Python Toolkit.

Provides a compact, task-oriented wrapper around the CDISC Library API for discovering, searching, and fetching Biomedical Concepts and SDTM Dataset Specializations.

Unified SPEDAS-oriented MCP server for Claude Code and Codex agents, providing tools to discover and fetch heliophysics and planetary science data from CDAWeb, NASA PDS, and SPICE kernels.

An MCP server that enables automated dataset creation and custom object detection model training through natural language interactions. It integrates foundation models like GroundedSAM for auto-labeling and supports training specialized YOLOv8 models using local or Unsplash images.