mcp-server-code-execution-mode: An MCP server that executes Python code in isolated rootless containers with optional MCP server proxying

by elusznik · MCP Server · ★ 337

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How to install mcp-server-code-execution-mode

uvx --from git+https://github.com/elusznik/mcp-server-code-execution-mode mcp-server-code-execution-mode run

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 71/100

Flagged: sudo usage. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

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.

agentic-aiagentsanthropicclaudeclaude-codecode-executiondockermcpmodel-context-protocolorchestration

Quick Facts

Stars337
Forks29
LanguagePython
CategoryMCP Server
LicenseGPL-3.0
Quality Score71/100
Open Issues6
Last Updated2025-12-05
Created2025-11-10
Platformsclaude-code, docker, mcp, python
Est. Tokens~22k

About mcp-server-code-execution-mode

MCP Code Execution Server: Zero-Context Discovery for 100+ MCP Tools Stop paying 30,000 tokens per query. This bridge implements Anthropic's discovery pattern with rootless security—reducing MCP context from 30K to 200 tokens while proxying any stdio server.

Compatible Skills

These tools work well together with mcp-server-code-execution-mode for enhanced workflows:

  • Claude_api — semantic(0.17)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (59%)
  • claude-cognitive — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • token-optimizer — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • mcp-memory-service — semantic(0.12)+shared_fw(anthropic,cloudflare)+same_lang+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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