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"Running code or executing programming scripts" matching MCP servers:

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    Turns AI assistants into full-stack software engineers with 36 tools for cognitive reasoning, code validation, project scaffolding, and AI/IDE configuration generation across 130+ programming languages, databases, and frameworks.
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    Universal MCP server for executing TypeScript and Python code with progressive disclosure, reducing token usage by 98% by enabling on-demand access to all other MCP tools through code execution rather than loading tool definitions directly.
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    MIT
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    This MCP server is designed for planning with Claude Code, Cline, or Cursor and making changes with Cerebras to maximize speed and intelligence while avoiding API limits. It uses the Qwen 3 Coder model for high-quality code generation and can be embedded in IDEs.
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    MIT
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    A lightweight Model Context Protocol (MCP) orchestrator designed for efficiency at scale. It features TOON compression (reducing token usage by 30-90%) and Lazy Loading, making it the ideal solution for complex, multi-tool agentic workflows.
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    MIT
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    A security filter that blocks dangerous code patterns by comparing normalized structural syntax trees against a blacklist of known threats using vector embeddings. It acts as a gatekeeper to prevent malicious code execution by identifying dangerous structures regardless of specific identifiers or literals.
    11
    MIT
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    A secure Node.js execution environment that allows coding agents and LLMs to run JavaScript dynamically, install NPM packages, and retrieve results while adhering to the Model Control Protocol.
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    An implementation of Claude Code as a Model Context Protocol server that enables using Claude's software engineering capabilities (code generation, editing, reviewing, and file operations) through the standardized MCP interface.
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    MIT
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    Pairs Claude Code with Google's Gemini AI for complementary code analysis, enabling intelligent routing where Claude handles local-context operations while Gemini leverages its 1M token context for distributed system debugging and long-trace analysis.
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    MIT
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    Enables AI-powered code review and improvement, including analysis, refactoring suggestions, and automatic test generation, with an optional agentic loop for iterative refinement.
    MIT
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    Provides sandboxed code execution for AI agents with support for Python, JavaScript, and shell commands. Includes comprehensive safety features like destructive pattern blocking, timeout protection, and restricted file access for secure production use.
    41
    MIT