FTShare-MCP
Integrates real-time financial and market data into AI agents, eliminating manual data sourcing delays and enabling data-driven investment decisions at scale…
FTShare MCP tools for financial data, market data and AI Agent investment workflows|金融数据 MCP 工具文档与接入说明。
- Ask Claude to retrieve historical stock price data for portfolio performance analysis
- Generate market trend reports using real-time financial data across multiple assets
- Automate investment decision workflows by analyzing market conditions and economic indicators
Integrates real-time financial and market data into AI agents, eliminating manual data sourcing delays and enabling data-driven investment decisions at scale without custom API integration overhead.
Investment teams and financial analysts deploying Claude-based agents requiring live market data and portfolio workflow automation.
https://github.com/FTShare-Lab/FTShare-MCP
By FTShare-Lab
How to Get It
claude mcp add FTShare-MCP -- npx -y FTShare-MCP
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Retrieve historical stock price data for portfolio performance analysis
Trust Signals Auto-scanned
Data & Access
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 56 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 56 GitHub stars; contributors unknown; last commit 0d ago; license MIT.