MCP server providing 29 A-share analysis skills including real-time data, capital flow, limit-up tracking, technical/fundamental analysis, backtesting, risk control, and Xueqiu portfolio tracking, enabling AI agents to execute market research and strategy tasks.
Provides comprehensive A-share (Chinese stock market) data including stock information, historical prices, financial reports, macroeconomic indicators, technical analysis, and valuation metrics through the free Baostock data source.
MCP server adapter that exposes A-share stock data tools, prompts, and resources via FastMCP, enabling querying of stocks, K-lines, financials, sectors, and market hot spots through natural language.
Provides 42+ specialized tools for nutrition analysis integrating Canada's Food Guide recipes with Health Canada's official databases (CNF, DRI, EER) for recipe discovery, macro calculations, energy requirements, and dietary adequacy assessments.
Enables sentiment analysis of text blocks using the Api Ninjas API, returning sentiment scores and overall sentiment classification for up to 2000 characters of text.
Enables genomic sequence analysis through the Evo 2 model, supporting DNA sequence scoring, embedding, generation, and variant effect prediction with multiple model checkpoints (7B, 40B, 1B parameters).
Enables LLMs to build, inspect, run, and analyze CFAST fire models step by step via tools for compartments, materials, vents, fires, devices, and surface connections, with simulation and result summaries.
Enables interactive access to JAXA's satellite observation data (precipitation, land surface temperature, NDVI, elevation, soil moisture) via Claude, providing tools for point time series, dataset information, and area image generation.
Provides a tool for dynamic and reflective problem-solving by breaking complex problems into manageable steps with support for revision, branching, and hypothesis generation.
Minimalistic MCP server that lets AI assistants inspect, quality-check, and clean CSV datasets through tools, resources, and prompts, without needing local file access.
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.
Enables to analyze Korean pharma/biotech stocks using clinical trial data from ClinicalTrials.gov and market data from Naver Finance, providing 100-point scoring, decision labels, and technical indicators.
MCP server for symbolic computation that enables AI agents to perform step-by-step derivations, transform formulas, and verify results with full provenance, combining natural language with formal mathematical operations.