AgentRPC MCP

This Skill addresses the challenge of AI agents calling functions across network and language boundaries. In practical development, when backend services are deployed in private VPCs, Kubernetes clusters, or multi-cloud environments, AI agents often cannot directly access these services, limiting their functionality. AgentRPC enables AI agents to securely call functions written in any language and located in any network through a unified RPC interface, without requiring open ports or changes to network architecture, by registering functions.

Usage is straightforward: simply import the AgentRPC SDK (supporting TypeScript, Go, Python) into your target service, register your functions, and set an API key. The AgentRPC cloud platform will then host these functions and generate an MCP-compatible server address. You can add this server to the MCP configuration in tools like Claude Desktop or Cursor, and then invoke registered functions via natural language instructions, such as "query user orders in the database" or "execute a Python script for data analysis."

It is ideal for teams or individuals needing to integrate internal services into AI agents. This is especially relevant for backend developers, agent developers, and DevOps engineers already using AI programming tools like Claude or Cursor, who want their AI assistants to directly interact with private APIs, databases, or microservices. It also suits scenarios requiring cross-language function calls (e.g., a Go service calling a Python function).

When registering functions, it is recommended to split them into fine-grained operations to avoid exposing overly permissive actions. Additionally, pay attention to function timeouts and error handling, as AgentRPC's long-polling mechanism supports long-running operations but requires functions to return results correctly. Furthermore, the MCP server depends on a Node.js environment; Node.js v18 or above is recommended for optimal compatibility.