pal-mcp-server: The power of Claude Code / GeminiCLI / CodexCLI + [Gemini / OpenAI / OpenRouter / Azure / Grok / Ollama /

by BeehiveInnovations · MCP Server · ★ 11.8k

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How to install pal-mcp-server

git clone https://github.com/BeehiveInnovations/pal-mcp-server.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 66/100

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.

Quick Facts

Stars11,750
Forks1,041
LanguagePython
CategoryMCP Server
Quality Score66/100
Open Issues145
Last Updated2025-12-15
Created2025-06-08
Platformsclaude-code, cli, codex, gemini, mcp, python
Est. Tokens~265k

About pal-mcp-server

PAL MCP: Many Workflows. One Context. Your AI's PAL – a Provider Abstraction Layer Formerly known as Zen MCP PAL in action 👉 Watch more examples Your CLI + Multiple Models = Your AI Dev Team Use the 🤖 CLI you love: Claude Code · Gemini CLI · Codex CLI · Qwen Code CLI · Cursor · and more With multiple models within a single prompt: Gemini · OpenAI · Anthropic · Grok · Azure · Ollama · OpenRouter...

Compatible Skills

These tools work well together with pal-mcp-server for enhanced workflows:

  • ruby_llm — semantic(0.23)+complementary+shared_fw(ollama,openai)+similar_pop+shared_platform (59%)
  • ai-review — semantic(0.20)+complementary+shared_fw(ollama,openai)+same_lang+shared_platform (58%)
  • open-strawberry — semantic(0.19)+complementary+shared_fw(ollama,openai)+same_lang+shared_platform (58%)
  • CLIProxyAPI — semantic(0.19)+complementary+shared_fw(openai)+similar_pop+shared_platform (55%)

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How this security grade is produced

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:

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