Boothcheck vs Finlab AI
A side-by-side look at the Boothcheck and Finlab AI MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Boothcheck for finance & fintech work over a hosted endpoint; choose Finlab AI for finance & fintech over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Boothcheck when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
whats_priced_in, most_stretched, compare_priced_in.
Choose Finlab AI when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
list_strategies, get_strategy, get_stock_evidence.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Finance & Fintech | Finance & Fintech |
| Quality signal | 57/100 · Good | 64/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Remote HTTP/SSE |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 417 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 5 | 6 |
| Full listing | View Boothcheck | View Finlab AI |
Tools & capabilities
Boothcheck tools 5
whats_priced_inInvert a US stock's current price into the bet it implies: the operating-income growth rate, how many years it must be sustained, and (where the route holds one as an input) the terminal operating margin assumption, versus what the company has actually delivered (from SEC EDGAR filings, reverse-DCF). boothcheck does not return a Boothcheck house fair value, price target, or buy/sell rating — it shows what the price assumes so the user can judge the bet. Covers about 2,000 US tickers.most_stretchedRank US stocks by how much growth is already baked into their price (implied operating-income growth from a reverse-DCF read of the current price). Returns the top N with the growth and duration each price assumes (plus any held margin input). Not a sell list — a ranking of where the bar to clear is highest.compare_priced_inSide-by-side of what two US stock prices are each betting on: implied growth and duration (plus any held margin input) from a reverse-DCF inversion of each price. No Boothcheck house fair value or rating — a comparison of the assumptions embedded in the two prices.list_coverageInventory of US tickers with an implied-expectations model record, with its resolution level (whole-company, segment, financials, REIT, and kin). Model-record coverage does not guarantee a published priced-in decomposition or completed narrative report. Paginated at 100; inventory cached for up to one hour.get_report_summaryThe bullet takeaways of a boothcheck narrative research report plus which sections exist. Public reports are summarized keylessly; a signed-in Founding Access account key opens the full cached library. Never a fair value, target, or rating.
Finlab AI tools 6
list_strategies列出 FinLab 公開策略庫中的台股量化策略，含每一檔策略的真實回測績效摘要（CAGR、Sharpe、最大回撤）與基準比較。想看完整策略內容與 Python 程式碼時，請接著呼叫 get_strategy。get_strategy取得 FinLab 策略庫中單一策略的完整快照：策略說明、真實回測績效與基準比較、以及可直接執行的 finlab Python 程式碼。slug 可先用 list_strategies 查詢。get_stock_evidence查詢台股個股的每日更新數據實證：基本面體質定位（獲利、估值、籌碼因子百分位）、目前正在觸發的量化訊號與其歷史前瞻報酬統計、估值位階等。例如 symbol="2330" 查台積電。資料來自 FinLab 每日以 finlab 套件計算的真實數據。get_data_catalog取得 FinLab 量化資料庫的目錄摘要：涵蓋台股與美股的分類數、欄位數、資料列數、年份範圍，以及代表性資料集（股價、月營收、財務指標等）。適合回答「FinLab 有哪些資料」。get_finlab_docs取得 FinLab Skill 的開發文件（與 GitHub koreal6803/finlab-ai main 分支同步，skill 更新後自動生效）：不帶參數回傳 SKILL.md 主文件與參考文件清單；帶 doc 參數回傳指定參考文件全文。涵蓋 900+ 資料欄位、sim() 回測 API、60+ 策略範例、因子分析與機器學習等，撰寫 finlab Python 程式前建議先查閱。how_to_start取得 FinLab 快速上手指南：如何用 AI 輔助流程（Claude cowork / Codex）在自己電腦安裝 FinLab 並跑第一個台股策略真實回測，以及免費線上體驗入口。適合回答「怎麼開始用 FinLab」「怎麼回測台股」。
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Boothcheck
{
"mcpServers": {
"boothcheck": {
"url": "https://boothcheck.com/api/mcp"
}
}
}
Finlab AI
{
"mcpServers": {
"finlab-ai": {
"url": "https://mcp.finlab.finance/mcp"
}
}
}
Frequently asked questions
Boothcheck is categorized under Finance & Fintech and uses a remote HTTP/SSE transport. Finlab AI belongs to Finance & Fintech and uses a remote HTTP/SSE transport. Pick Boothcheck for finance & fintech work and Finlab AI for finance & fintech.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Boothcheck has no declared pricing — check the project's own site. Finlab AI has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add boothcheck and finlab-ai as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Boothcheck has a quality signal of 57/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Finlab AI has 64/100 (Good) with 3 views, 0 installs and 417 GitHub stars.