Financial data MCP servers and CLIs for AI agents — stock quotes, personal finance, accounting, trading APIs, market analysis.
Finance MCP Servers tools are AI-powered software designed to help developers and teams tackle finance mcp servers-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 30 quality-scored finance mcp servers tools across languages including Python, TypeScript.
In 2026, the AI agent ecosystem is maturing rapidly. Finance MCP Servers tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — maverick-mcp, mcp-trader, stratevo — have earned an average of 897 GitHub stars, reflecting strong community validation. 25 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a finance mcp servers tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is Python; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with maverick-mcp — it ranks highest in both star count and quality score.
A Model Context Protocol (MCP) server for stock traders
Genetic algorithms auto-evolve trading strategies from 484 factors. Crypto + A-shares + US stocks. Walk-forward validated. No API keys needed.
TradingAgents-MCPmode 是一个创新的多智能体交易分析系统，集成了 Model Context Protocol (MCP) 工具，实现了智能化的股票分析和交易决策流程。系统通过多个专业化智能体的协作，提供全面的市场分析、投资建议和风险管理。
Korea Investment & Securities Open API Github
An MCP server and Next.js web app for querying S&P 500 company data from Supabase, with tools for company info, news, officers, and SEC filings, plus embedded MCP App UI resources, Elicitation, and Sampling support.
Official Python MCP server for local interactions with the QuantConnect API
Backtrader-powered backtesting framework for algorithmic trading, featuring 20+ strategies, multi-market support, CLI tools, and an integrated MCP server for professional traders.
Model Context Protocol (MCP) to enable AI LLMs to trade using MetaTrader platform
Composer's MCP server lets MCP-enabled LLMs like Claude backtest trading ideas and automatically invest in them for you
This is a Model Context Protocol (MCP) server that provides comprehensive financial data from Yahoo Finance. It allows you to retrieve detailed information about stocks, including historical prices, company information, financial statements, options data, and market news.
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MCP server that exposes live insider trading data to any LLM.
ValueCell is a community-driven, multi-agent platform for financial applications.
An MCP server for interacting with the Financial Datasets stock market API.
Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build strategies in plain English directly from your favorite LLM tools and IDEs
🤖 AI-Powered MCP Server for Polymarket - Enable Claude to trade prediction markets with 45 tools, real-time monitoring, and enterprise-grade safety features
An MCP server for Massive.com Financial Market Data
OKX trading MCP server — connect AI agents to spot, swap, futures, options & grid bots via the Model Context Protocol.
A Model Context Protocol (MCP) implementation for Financial Modeling Prep, enabling AI assistants to access and analyze financial data, stock information, company fundamentals, and market insights.
Curated list of LLM-driven trading agents, MCP servers, and agent skills for market research, strategy, and execution.
Open-source tax skills for AI — 371 skills across 134 countries. Upload to any LLM or connect via MCP. Quality-tiered Q1–Q5.
A 股多智能体智能投研系统 — 基于 TradingAgents 架构，15 名 AI Agent 模拟机构协作与实时辩论对抗，全流程可视化，支持 OpenClaw / Claude Code 集成，Docker 一键部署。
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| maverick-mcp | ★ 543 | Python | MIT | 51 |
| mcp-trader | ★ 260 | — | — | 32 |
| stratevo | ★ 51 | Python | AGPL-3.0 | 36 |
| monarch-mcp-server | ★ 209 | Python | MIT | 48 |
| TradingAgents-MCPmode | ★ 238 | Python | — | 33 |
| Vibe-Trading | ★ 7.3k | Python | MIT | 48 |
| open-trading-api | ★ 1.1k | Python | — | 39 |
| sp500-mcp-server | ★ 94 | TypeScript | AGPL-3.0 | 44 |
| mcp-server | ★ 74 | Python | Apache-2.0 | 48 |
| ai-trader | ★ 530 | Python | GPL-3.0 | 40 |
| mcp-aktools | ★ 371 | Python | MIT | 47 |
| metatrader-mcp-server | ★ 183 | Python | MIT | 46 |
| composer-trade-mcp | ★ 220 | Python | MIT | 37 |
| yfinance-mcp | ★ 135 | Python | MIT | 49 |
| yahoo-finance-mcp | ★ 246 | Python | MIT | 40 |
| best-of-algorithmic-trading | ★ 149 | TypeScript | CC-BY-SA-4.0 | 48 |
| OpenInsider-MCP | ★ 68 | TypeScript | MIT | 50 |
| valuecell | ★ 9.5k | Python | Apache-2.0 | 42 |
| mcp-server | ★ 1.5k | Python | MIT | 37 |
| LangAlpha | ★ 1.1k | Python | Apache-2.0 | 39 |
| alpaca-mcp-server | ★ 651 | Python | MIT | 51 |
| prism-insight | ★ 583 | Python | AGPL-3.0 | 47 |
| polymarket-mcp-server | ★ 445 | Python | MIT | 53 |
| mcp_massive | ★ 325 | Python | MIT | 51 |
| agent-trade-kit | ★ 294 | TypeScript | MIT | 44 |
| Financial-Modeling-Prep-MCP-Server | ★ 130 | TypeScript | Apache-2.0 | 40 |
| awesome-trading-agents | ★ 84 | — | CC0-1.0 | 42 |
| dartlab | ★ 78 | Python | Apache-2.0 | 34 |
| openaccountants | ★ 59 | Python | — | 35 |
| TradingAgents-AShare | ★ 336 | Python | — | 41 |
The top finance mcp servers in 2026 are maverick-mcp, mcp-trader, stratevo. Agent Skills Hub ranks 30 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
maverick-mcp (543 stars) is the most adopted choice for general finance mcp servers workflows, written in Python. mcp-trader (260 stars) is a strong alternative. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with maverick-mcp — it has the deepest community and the most examples online.
Avoid pre-built finance mcp servers when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
Finance MCP Servers focuses specifically on financial data mcp servers and clis for ai agents — stock quotes, personal finance, accounting, trading apis, market analysis. Data Pipeline is a related but distinct category — see https://agentskillshub.top/best/data-pipeline/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose finance mcp servers when your primary goal is the specific task, and data pipeline when the workflow is broader.
For most teams, yes. maverick-mcp has 543 stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most finance mcp servers listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.