"Samsung Pay" matching MCP servers:

Enables MCP clients to execute Python code directly via stdin, preserving quotes, f-strings, and backslashes exactly as written, and returns stdout, stderr, and exit codes.

MCP server providing AI agents access to 38 pay-per-call APIs (LLM, image, code, audio, crypto, web, IPFS) via HTTP 402 micropayments in USDC/USDm.

Execute Python, JavaScript, or SQL code in a sandboxed environment and retrieve stdout, execution time, and errors with pay-per-call via x402 (USDC on Base L2).

A Model Context Protocol implementation that plays sound effects (completion, error, notification) for Cursor AI and other MCP-compatible environments, providing audio feedback for a more interactive coding experience.

Enables AI agents to access pay-per-call web unlocking, structured extraction, clean scraping, Python sandbox execution, persistent key-value memory, and clearinghouse task bounties, with settlement via Bitcoin Lightning L402 or Base/Solana USDC x402.

Self-hosted Docker sandboxes for coding agents: create a sandbox with its own Docker daemon, deploy a docker-compose stack into it, read and write its files, and get a URL for every app. Tokens can be limited to one sandbox.

This server facilitates the invocation of AI models from providers like Anthropic, OpenAI, and Groq, enabling users to manage and configure large language model interactions seamlessly.

A client library for connecting to and interacting with MCP servers hosted on smcphub.com, allowing users to find, publish, and call tools on MCP servers.

A template repository for creating custom Model Context Protocol (MCP) servers in Python that can be integrated with applications like Claude Desktop or Cursor.

Enables AI agents to execute arbitrary Python code securely in a sandboxed environment with resource limits and security constraints via MCP protocol.

A Python implementation of the Model Context Protocol that allows applications to provide standardized context for LLMs, enabling creation of servers that expose data and functionality to LLM applications through resources, tools, and prompts.