"How to run a Python script in a sandboxed environment" matching MCP servers:

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Executes IDAPython scripts in IDA Pro through any MCP-compatible AI assistant, enabling AI-powered binary analysis with full access to IDA's API.

Enables static analysis of scripts without executing them, providing plain-English behavior explanations, decoded obfuscated content, IOC extraction, risk scoring, and MITRE ATT&CK mapping.

Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

This MCP server provides direct access to ruff linting, formatting checks, and ty type-checking for Python projects, with token-efficient, structured output.

Analyzes Python code and provides guided refactoring suggestions without automatically modifying code.

Exposes the 23 Gang of Four design patterns to AI coding agents for generation, canonical examples, AST-based detection, validation, and anti-pattern refactoring in Python codebases.

Enables deterministic static analysis of Python code, providing tools to inspect classes, functions, imports, dependencies, and more, without executing the code.

MCP server providing automated code linting, rule explanations, and configuration templates for wemake-python-styleguide, with structured violation reports and offline rule database.

A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

A runtime-free MCP server that converts source code into AST🌲, regardless of language.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Enables automated migration of Vue projects to React with intelligent analysis, task management, and learning from user modifications.

Zero-dependency MCP server inspector for protocol conformance, tool annotations, and capability checks.

Enables Python code linting by integrating the Model Context Protocol with tools like pylint and the OpenAI API. It allows for dynamic tool discovery and uses LLMs to orchestrate tool selection and provide refined analysis of linting results.

Enables comprehensive security vulnerability scanning and code quality analysis for Python applications. Provides detailed reports with scoring, actionable suggestions, and comparison tracking specifically designed for backend developers working with frameworks like Django, Flask, and FastAPI.

Exposes repository reconnaissance tools — file listing and verbatim reading, regex search, dependency-tree parsing across Python/Node/Rust/Go, and test-coverage ingestion — so agents can gather line-numbered evidence from real source files. This lets an audit pipeline physically re-verify every claim and drop findings that cannot be located in the code.