Skyfire MCP Server

Overview

The mcp-server-skyfire implementation brings the Model Context Protocol (MCP) into the realm of digital payments by integrating with Skyfire’s payment infrastructure. It solves a common pain point for AI developers: enabling conversational agents to execute real‑world financial transactions without exposing sensitive credentials or building custom payment logic. By exposing a single, well‑defined tool——the server lets an AI model initiate a transfer to any Skyfire user through a standard, declarative interface.

At its core, the server acts as an MCP bridge. When an AI assistant receives a user request to pay someone, it can call the tool with two simple parameters: the recipient’s Skyfire username and the amount to send. The server authenticates itself with a pre‑configured API key, forwards the request to Skyfire’s SDK, and returns a structured response that the model can embed in its reply. This pattern keeps payment logic isolated from the assistant’s core code, reduces security surface area, and guarantees that only authorized clients can trigger transfers.

Key capabilities of the server include:

• Unified payment entry point: A single MCP tool that encapsulates all necessary validation and error handling, making it trivial for models to perform transactions.
• Robust error reporting: MCP‑specific errors (, , ) map directly to Skyfire failure states, allowing the assistant to surface clear feedback to users.
• Type safety and validation: The implementation leverages schemas to enforce parameter types, preventing malformed requests from reaching Skyfire.
• Standard I/O operation: Running over stdin/stdout keeps deployment lightweight and compatible with most MCP‑enabled runtimes.

Typical use cases span from e‑commerce chatbots that can “pay the seller” after a purchase, to financial planning assistants that can transfer funds between accounts on behalf of users. In each scenario the assistant simply invokes , receives a confirmation text, and presents it to the user—no extra plumbing required.

Integration into existing AI workflows is straightforward. A developer can deploy the server as a separate process, expose its MCP endpoint to an assistant platform (such as Claude or GPT‑4o), and then reference the tool in prompts. Because the server follows MCP conventions, any assistant that supports tools can automatically discover and use without custom adapters. This plug‑and‑play model accelerates feature rollout, improves security by centralizing credentials, and provides a clear audit trail of payment actions initiated through AI.