https://mcp.moltlinestudio.com/codereview
Scanned Sep 5, 2026
Upgrade available
Your server uses protocol 2025-06-18. The latest version is 2025-11-25 with session management, origin validation, and notification support.
No authentication required — server is open to anonymous access, which may be intentional for public tools
Multiple similar MCP servers exist. Consider differentiating with unique features.
Server responded to ping
Valid Mcp-Session-Id assigned (32 chars)
Server correctly rejects invalid MCP-Protocol-Version with 400
Server correctly rejects malformed JSON-RPC with 400
Server correctly rejects invalid Origin with 403
Server returns 405 for DELETE (acceptable — session termination not supported)
Server returns 202 Accepted for JSON-RPC notifications
Server info: name, version
No OAuth? That's OK
OAuth is optional, but consider it for production deployments to protect your server from unauthorized access.
external: request, file, path; LLM-native: format
5+ similar servers — crowded space
Domain "git" has broad developer audience
Competitors found
Connecting CodeScene's CodeHealth MCP to Claude Code to refactor a PySpark ETL job and fix AI-generated technical debt.
Cycode introduces its new MCP server, joining the Model Context Protocol (MCP) ecosystem to help secure AI-generated code in real time.
I Built an Open-Source Security Scanner for MCP Servers. Here's Why. RSA 2026 does something to you if you pay attention. Not the keynotes.
The Model Context Protocol (MCP) allows AI applications to connect to external tools, APIs, databases, files, and workflows through standardized MCP servers ...
The potential risk may be even higher in the context of MCP servers, as these systems bridge foundation models with databases, file systems, and ...
“review_diff: 85 pts | Risk: read | Naming: 10/15, Desc: 20/20, Input: 15/20, Annot: 15/15”
“ai_code_smell_scan: 80 pts | Risk: read | Naming: 10/15, Desc: 15/20, Input: 15/20, Annot: 15/15”
“complexity_report: 85 pts | Risk: read | Naming: 10/15, Desc: 20/20, Input: 15/20, Annot: 15/15”
“secret_scan: 80 pts | Risk: read | Naming: 10/15, Desc: 15/20, Input: 15/20, Annot: 15/15”
“review_checklist: 80 pts | Risk: read | Naming: 10/15, Desc: 15/20, Input: 15/20, Annot: 15/15”
“security_deep_dive: 85 pts | Risk: read | Naming: 10/15, Desc: 20/20, Input: 15/20, Annot: 15/15”
“get_reviewer_persona: 90 pts | Risk: read | Naming: 15/15, Desc: 20/20, Input: 15/20, Annot: 15/15”
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