Dvcode Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Dvcode Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
A Model Context Protocol (MCP) server for querying DeepVLab account statistics and model usage analytics.
git clone https://github.com/yourusername/deepvlab-mcp.git
cd deepvlab-mcp
pip install -r requirements.txtAdd the server to your claude_desktop_config.json:
{
"mcpServers": {
"deepvlab-analytics": {
"command": "python",
"args": ["/path/to/deepvlab-mcp/server.py"],
"env": {
"PYTHONPATH": "/path/to/deepvlab-mcp"
}
}
}
}Note: Replace `/path/to/deepvlab-mcp` with your actual absolute path.
Restart Claude Desktop and simply ask:
| Tool | Description |
|---|---|
login | Launch browser to authorize and get token |
get_user_summary | Get user info and balance overview |
get_model_rates | List all models and their rates |
get_credits_usage | Detailed credit usage stats |
get_model_usage | Usage stats per model |
get_daily_usage | Daily usage trends |
get_quotas | Quota information |
calculate_cost | Calculate cost for specific token counts |
estimate_batch_cost | Estimate costs for batch requests |
compare_model_costs | Compare costs across multiple models |
MIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.