What is Lician Financial Data?
Lician Financial Data is a AI tool that Provides stock prices, financial statements, insider trades, institutional holdings, analyst ratings, and SEC filings for over 141,000 US and European companies via the Lician API.. It has a Nerq Trust Score of 39/100 (E). 0 GitHub stars. Published by https://github.com/sebastianbo/quant-platform/tree/HEAD/mcp-server. Last analyzed October 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 0 stars, 0 downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
What Lician Financial Data Does
Lician Financial Data is a mcp_server in the AI tool category. Provides stock prices, financial statements, insider trades, institutional holdings, analyst ratings, and SEC filings for over 141,000 US and European companies via the Lician API.. It is published by https://github.com/sebastianbo/quant-platform/tree/HEAD/mcp-server and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.
Who Should Use Lician Financial Data
Lician Financial Data is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | https://github.com/sebastianbo/quant-platform/tree/HEAD/mcp-server |
|---|---|
| Category | AI tool |
| License | Not specified |
| Type | mcp_server |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=lician-financial-data
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| MarkItDown | 42 | 92.8K |
| Time | 50 | 89.4K |
| Filesystem | 50 | 89.4K |
| Fetch | 50 | 89.4K |
| Sequential Thinking | 50 | 89.4K |
Frequently Asked Questions
Last updated October 2026. Trust scores based on automated analysis of public data.