by 12122J · MCP Server · ★ 63
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 63 |
| Forks | 2 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71/100 |
| Last Updated | 2026-07-14 |
| Created | 2026-06-02 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~17k |
Claude Code DeepSeek Delegator ### 🚀 3.0 is out — now model-agnostic One tool, any model: DeepSeek, Kimi, GLM, Qwen, Grok, Groq, OpenRouter, even your local ollama — and you can mix them per task. Plus a rebuilt interactive installer, a model picker inside Claude Code, and a receipt for every cent. v2 installs keep working untouched.
Looking for a claude-delegator-deepseek-mcp alternative? If you're comparing claude-delegator-deepseek-mcp with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
📜 An MCP server for conversation history search and retrieval in Claude Code
An introduction to the world of AI Agents
Google Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to
Turn your Claude Pro/Max subscription into an OpenAI-compatible API for your IDEs and devices — LAN auth, per-
OpenAPI definitions, converters and LLM function calling schema composer.
Local-first, governed AI agent runtime for Python — embed it in your app, or run it as a CLI or ACP server. Pe
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: