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 TypeScript, C#, Python.
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 — stock-sdk, Equibles, mcp-trader — have earned an average of 3,350 GitHub stars, reflecting strong community validation. 22 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 TypeScript; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with stock-sdk — it ranks highest in both star count and quality score.
为前端设计的无需 Python、无需后端服务、零依赖的获取股票数据 JavaScript SDK。
Self-hosted, open-source financial data MCP server for AI agents — SEC filings, XBRL financials, 13F holdings, insider & congressional trades, short interest, FRED, CFTC/CBOE and daily prices across 64 MCP tools. Equibles Cloud adds earnings call transcripts, live quotes, options chains with Greeks, guidance and a screener.
A Model Context Protocol (MCP) server for stock traders
A curated list of MCP servers for AI finance agents
An Self-Evolving Stock Strategy Research Agent 策略回测和自我进化
Open-source AI Trading OS, agent trading, and vibe trading, with Jev System One integration. Research, build Python strategies, backtest, and paper/live trade across crypto, stocks, and forex. Launch your own multi-tenant trading SaaS with built-in user management, billing, payments, and settlement.
A股全栈数据工具包：行情K线·当日逐笔·研报·信号·资金面·新闻·财务·公告·打板·ETF期权·舆情·宏观利率·期货大宗(含大商所日K)·事件驱动·可转债 | 15层·87端点·34数据源·除iwencai外免Key | A-share data for AI agents: K-lines, ticks, reports, fund flow, news, financials, filings, options, macro, futures, events, convertibles | 15 layers·87 endpoints·34 sources
AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading ,trading-agents,ai-trader,ai-trading
skills：一句话获取 A 股每日市场数据 — 赚钱效应、热门题材、连板天梯、游资龙虎榜。零配置，无需注册，无需 Token。
PanWatch — AI stock monitoring for A-shares, HK & US markets, powered by TradingAgents. Portfolio insights, real-time alerts & automated reports.｜盯盘侠：覆盖 A股/港股/美股的 AI 盯盘、持仓分析、实时提醒与自动报告。
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。
Vibe-Research: Your Personal Trading Research Agent · A股/美股/港股 的个人投研 Agent：每日复盘、资讯雷达、个股数据、板块中心、我的持仓、研究记录、回测。Vibe-Research 把数据和功能配齐，由你自己的 Agent 驱动投资研究。基于开源的 Codex Harness 打造。
US stock market data for AI coding assistants — zero-auth, official sources. CBOE options with full Greeks + 0DTE flow, FINRA market-wide short volume, SEC EDGAR filing stream, and a free market-wide screener. 13 layers, 30+ endpoints, 11 sources. Every source labeled with its compliance tier.
Korea Investment & Securities Open API Github
A股行情分析与AI智能投研智能体：股票分析、量化交易分析、盘后复盘桌面工作台——easy stock
Claude Code skill: 5 AI agents analyze crypto, stocks, forex & commodities in parallel, adapt to your risk profile, and render an interactive bilingual (EN/ES) dashboard. Hybrid keyless market data. Educational market research, not financial advice.
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。
AI agent skill that answers macro questions — housing, gold, BTC, geopolitics — with probability estimates mined from 13 financial data sources (Polymarket, Kalshi, CFTC, SEC & more). For Claude Code / Cursor / Codex / OpenClaw. | 让 AI Agent 从金融数据中挖掘宏观趋势的数字先知
MaverickMCP - Personal Stock Analysis MCP Server
High-performance Python backtesting & live-trading framework: 45%+ faster than upstream, 50+ indicators, tick-to-daily strategies, plus an AI-native workflow (MCP server, agent skills, web platform).
```bash
pip install back-trader-cpp
```Stock scanner with multiple fundamental and technical criteria. Features 80+ filters, AI chatbot (Groq/DeepSeek/Gemini), theme discovery, and StockBee-style breadth indicators.
AI 时代的伯克希尔：基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code / Codex. 4 masters' methodologies + multi-agent adversarial analysis.
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
Claude Skills for Indian equity investors and traders — NSE/BSE stocks, F&O derivatives, institutional flows, and market breadth analysis
A股 AI 金融智能决策中台 · 129 个 MCP 工具 · eltdx 通达信协议 + akshare + 同花顺 + 东财 + 本地数据 · SmartRouter 多源降级路由 · 免费零鉴权
ApocData · AI-native financial database for China A-share market. Drop-in Skill / MCP for Claude,ChatGPT, Qwen, Kimi, DeepSeek agents.
Backtrader-powered backtesting framework for algorithmic trading, featuring 20+ strategies, multi-market support, CLI tools, and an integrated MCP server for professional traders.
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| stock-sdk | ★ 2.0k | TypeScript | ISC | 72 |
| Equibles | ★ 230 | C# | AGPL-3.0 | 64 |
| mcp-trader | ★ 273 | — | — | 45 |
| monarch-mcp-server | ★ 393 | Python | MIT | 72 |
| awesome-finance-mcp | ★ 208 | — | — | 70 |
| alphaevo | ★ 176 | Python | Apache-2.0 | 65 |
| QuantDinger | ★ 12.4k | Python | Apache-2.0 | 74 |
| a-stock-data | ★ 10.3k | Python | Apache-2.0 | 78 |
| QuantDinger | ★ 9.8k | Python | Apache-2.0 | 77 |
| hhxg-top-hhxg-python | ★ 73 | Python | MIT | 65 |
| PanWatch | ★ 2.0k | Python | MIT | 72 |
| TradingAgents-MCPmode | ★ 337 | Python | — | 47 |
| Vibe-Trading | ★ 34.5k | Python | MIT | 75 |
| Vibe-Research | ★ 2.6k | TypeScript | MIT | 70 |
| global-stock-data | ★ 1.6k | — | Apache-2.0 | 83 |
| open-trading-api | ★ 1.6k | Python | — | 67 |
| easy-stock | ★ 1.1k | Go | — | 68 |
| maia-skill | ★ 129 | TypeScript | MIT | 58 |
| TradingAgents-AShare | ★ 834 | Python | — | 70 |
| digital-oracle | ★ 856 | Python | MIT | 68 |
| maverick-mcp | ★ 678 | Python | MIT | 75 |
| backtrader | ★ 191 | Python | GPL-3.0 | 74 |
| stock-screener | ★ 280 | Python | — | 54 |
| ai-berkshire | ★ 16.6k | HTML | MIT | 73 |
| sp500-mcp-server | ★ 106 | TypeScript | AGPL-3.0 | 72 |
| mcp-server | ★ 77 | Python | Apache-2.0 | 68 |
| indian-trading-skills | ★ 65 | Python | MIT | 66 |
| tradex-hub | ★ 55 | Python | — | 60 |
| ApocData-skill | ★ 51 | Shell | Apache-2.0 | 61 |
| ai-trader | ★ 1.1k | Python | GPL-3.0 | 57 |
The top finance mcp servers in 2026 are stock-sdk, Equibles, mcp-trader. 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.
stock-sdk (2.0k stars) is the most adopted choice for general finance mcp servers workflows, written in TypeScript. Equibles (230 stars) is a strong alternative and uses C# instead. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with stock-sdk — 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. stock-sdk has 2.0k 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.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: