What is Tavily Aitavily MCP?
46/100
Trust Score (D)
⚠️ Use Caution
Tavily Aitavily MCP is a ai_tool that An AI ai_tool. It has a Nerq Trust Score of 46/100 (D). 0 GitHub stars. Published by Unknown. Last analyzed August 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
46
TRUST SCORE
D
GRADE
0
STARS
0
DOWNLOADS
What Tavily Aitavily MCP Does
Tavily Aitavily MCP is a ai_tool in the ai_tool category. An AI ai_tool. It is published by Unknown and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.
Who Should Use Tavily Aitavily MCP
Tavily Aitavily MCP is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | Unknown |
|---|---|
| Category | ai_tool |
| License | Not specified |
| Type | ai_tool |
| 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=tavily-aitavily-mcp
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| LLaVA | 61 | 24.5K |
| Qwen2.5-VL-7B-Instruct | 69 | 1.5K |
| Qwen2.5-7B-Instruct | 59 | 1.1K |
| Qwen2.5-1.5B-Instruct | 59 | 617 |
| Qwen3-4B | 63 | 556 |
Frequently Asked Questions
What is Tavily Aitavily MCP used for?
Tavily Aitavily MCP is a ai_tool tool. An AI ai_tool.
Is Tavily Aitavily MCP free?
License: Check project page. Tavily Aitavily MCP has 0 GitHub stars.
Is Tavily Aitavily MCP safe?
Tavily Aitavily MCP has a Nerq Trust Score of 46/100 (D). Evaluate carefully.
What are alternatives to Tavily Aitavily MCP?
Top alternatives: LLaVA, Qwen2.5-VL-7B-Instruct, Qwen2.5-7B-Instruct. See full comparison.
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Last updated August 2026. Trust scores based on automated analysis of public data.