Cinderfi — Retirement Planning vs Finlab AI
A side-by-side look at the Cinderfi — Retirement Planning 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 Cinderfi — Retirement Planning 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 Cinderfi — Retirement Planning when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a remote HTTP/SSE transport.
- You have the keys it needs:
CINDERFI_MCP_KEY.
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 | 37/100 · Fair | 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 | CINDERFI_MCP_KEY | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 2 | 416 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | Not listed | 6 |
| Full listing | View Cinderfi — Retirement Planning | View Finlab AI |
Tools & capabilities
Cinderfi — Retirement Planning tools
No tool names declared yet. Check the project README from the listing page.
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.
Cinderfi — Retirement Planning
{
"mcpServers": {
"cinderfi-retirement-planning-2": {
"url": "https://mcp.cinder.fi/mcp"
}
}
}
Finlab AI
{
"mcpServers": {
"finlab-ai": {
"url": "https://mcp.finlab.finance/mcp"
}
}
}
Frequently asked questions
Cinderfi — Retirement Planning 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 Cinderfi — Retirement Planning 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.
Cinderfi — Retirement Planning 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 cinderfi-retirement-planning-2 and finlab-ai as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Cinderfi — Retirement Planning has a quality signal of 37/100 (Fair) with 0 views, 0 installs and 2 GitHub stars. Finlab AI has 64/100 (Good) with 3 views, 0 installs and 416 GitHub stars.