◆Methodology
How MCP-INDEX discovers, classifies, and analyzes MCP servers for security vulnerabilities.
> PIPELINE LOADING...
Disclaimer: All findings on MCP-INDEX are automated analysis results. They indicate potential vulnerabilities that require manual verification. False positives are possible.
01
DISCOVER
GitHub search 5 queries
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02
CLASSIFY
Server / Client / Unknown
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03
FETCH
max 8 files, 2 depth
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04
ANALYZE
LLM audit prompt
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05
SCORE
0-100, 4 severity levels
> Daily sweep at 22:00 UTC // Re-analysis on repo update // Avg analysis time: 5s per repo // Avg cost: $0.0018 per repo
◆Repository Discovery
MCP-INDEX searches GitHub using five keyword queries:
- ◆modelcontextprotocol
- ◆mcp server
- ◆@modelcontextprotocol/sdk
- ◆mcpServers
- ◆claude_desktop_config
◆MCP Server Classification
Each repo is classified using dynamic file discovery (GitHub tree API):
- ◆mcp_server — Server SDK imports + tool registration
- ◆mcp_client — Client patterns (McpClient)
- ◆unknown — MCP keywords but no implementation
◆LLM Analysis
Two-phase analysis using deepseek-v4-pro via CrofAI:
- 1.Fetch repo tree via GitHub API, rank by entry point score
- 2.Follow imports up to 2 levels deep (max 8 files / 200KB)
- 3.Phase 1: LLM determines purpose + threat model
- 4.Phase 2: LLM audits with purpose-aware decision tree
◆Risk Scoring
Score calculated from LLM findings only:
- Critical: +40, High: +25, Medium: +15, Low: +5, capped at 100
≥ 70: CRITICAL
≥ 40: HIGH
≥ 20: MEDIUM
< 20: LOW
◆Limitations
- ◆Static analysis only — No runtime verification
- ◆LLM accuracy — False positives possible
- ◆GitHub-only — npm, PyPI not covered
- ◆Rate limited — GitHub API may delay analysis