"Understanding Git commands: pull, push, commit, and merge" matching MCP servers:

Enables AI agents to crawl, analyze, and query job description (JD) data from recruitment sites via MCP, with tools for searching jobs, viewing statistics and portraits, and running local analysis on privately stored data.

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

Deterministic US equity-compensation tax optimizer. Six tools cover ISO/AMT exercise scheduling, NSO sell-vs-hold, RSU vest-and-sell, single-stock concentration, protective put / zero-cost collar pricing, and Section 1202 QSBS qualification. Federal plus 50-state plus DC tax math.

Institutional-grade quantitative stock analysis and research signals for AI agents via the Model Context Protocol (MCP).

One-call Australian health and welfare data plumbing via AIHW — cited responses for practical analysis, not a data broker.

An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.

Enables AI agents and sportsbooks to discover, evaluate, and monitor alternative sports leagues with tools for discovery, valuation, fingerprinting, and market data.

MCP-compatible server that gives AI agents access to alternative sports data across 30+ leagues — odds, events, probabilities, settlement, and futures for prediction markets, DFS platforms, and sportsbooks.

Enables researchers to turn property-history questions into reviewable evidence dossiers by gathering sources, tracing people and places, linking conclusions to supporting or opposing passages, and exporting portable backups.

MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.

A self-contained MCP server that gives AI agents the ability to calculate high-precision astronomical data. It provides tropical zodiac coordinates, planetary speeds, retrograde detection, and house cusps using the trusted Swiss Ephemeris engine. 100%

Enables simulation and analysis of M/M/1 and M/M/c queuing systems using SimPy, with tools for parameter validation, theoretical metric calculation, simulation execution, and comparison of separate vs pooled queue strategies.

Enables structured information extraction from academic PDFs using LLMs, integrating with Claude Desktop for natural language querying and batch processing.

A lightweight MCP server that provides stock research tools for Japanese and US stocks, including price, dividends, valuation, and screening, powered by yfinance.

Enables AI assistants to interact with Ansys simulation software (Fluent, MAPDL, Mechanical, Geometry) through the Model Context Protocol.

Enables natural language question answering over academic PDFs with grounded answers, exact source attribution, and explicit uncertainty handling.

Enables users to preprocess, analyze, and visualize CSV data through comprehensive tools for data manipulation, statistical analysis, and graph generation.

Options analytics an agent can drive: import a permitted chain, solve implied volatility, build a sixteen-Greek ladder, map dealer gamma walls, rank twenty-three structures, run a GARCH-t simulation with a convergence gate, and backtest with modelled premiums. Twelve stdio tools; every output is a schema-validated JSON file with source, assumptions and a degraded flag. Research only, no orders.

Startup engineering acceleration signals for VC investors. Tracks commit velocity, contributor growth, and repo expansion across 20 sectors via public GitHub data. No API key required.

GitHunt MCP gives AI assistants access to developer talent-discovery engine. Search a pool of millions of GitHub developers by location, role, and skills; pull a single developer's ranked profile, or run deep analysis on a candidate. Results come back ranked by fit with transparent scoring, experience, commit activity, OSS contributions and the contact info, so an agent can build a shortlist.