"A sandbox environment to run Python code and install dependencies" matching MCP servers:

Enables secure execution of Python code in a sandboxed WebAssembly environment using Pyodide and Deno. Automatically handles package management and captures complete execution results including stdout, stderr, and return values.

Model Context Protocol server to run Python code in a sandbox.

Your AI agent can't touch Stripe, Gmail, Slack or more, without your tap. mild.run intercepts every sensitive action and sends you a notification to approve or block — before anything executes. Agent never holds credentials.

Enables AI agents to interact with the Execute.run bot API for managing Shell balances, transferring funds, and executing LLM requests. It provides tools for identity verification, transaction tracking, and performing compute tasks through the Execute.run platform.

An MCP server for securely executing Python code, supporting STDIO, Streamable HTTP, and SSE transports, with easy deployment on Heroku.

Enables MCP-compatible AI agents to deploy applications to Google Cloud Run by providing tools for deploying code, listing services, and managing Google Cloud projects.

MCP server exposing a parametric policy engine for evaluating, simulating, and managing rules, with dynamic plugin support for domain-specific calculations.

Connects AI assistants to the Sandbox Studio API to manage AWS sandbox accounts, leases, templates, events, and settings through natural language conversations.

embedded code search engine CLI -AST based that just works - improves speed and efficiency for coding agent

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.

A production-grade MCP server for the Code-Fundi API, enabling AI agents to map codebases, search semantically, and analyze blast radius. It provides tools for repository management, AI-powered research, and impact analysis before shipping changes.

Enables LLMs to query live data from over 250+ sources via CData Python Connectors using natural language. Wraps connectors as a Model Context Protocol server.

Persistent project memory + architectural decisions + pre-execution safety hooks for Claude Code. Local-only storage, multi-repo workspace, automatic knowledge extraction via background auditor.

Enables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.

Exposes the SiftingIO market-data SDK to AI assistants, enabling live prices, fundamental data, and market information retrieval.

A local, cross-editor MCP server that provides persistent memory for coding agents, capturing and recalling decisions, conventions, and fixes across sessions without API keys.

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.

Injects the Code-Me brand identity, tone, and visual guidelines into LLMs with zero latency using built-in context files.

Deterministic code review MCP server that provides tools for file selection, rule matching, comment positioning, and reflection, ensuring stable review quality without LLM calls.

Publishes static sites to Drop2Run from an AI agent: hand it files written in the chat or a folder on disk, and it returns a live HTTPS URL. Six tools — publish_files, publish_dir, list_sites, delete_site, and login / login_code — with no git, no build step and no repository.