Chaining MCP Tools: Search Read Analyze Write in TypeScript

Chaining MCP Tools: Search → Read → Analyze → Write in TypeScript Building sophisticated AI agents requires more than simple prompt-response interactions. Real-world automation demands multi-step workflows where AI performs sequential operations—gathering information, processing it, and taking action. This is where NeuroLink's Model Context Protocol (MCP) tool chaining capabilities transform what's possible with TypeScript. The Anatomy of a Tool Chain Consider a typical developer workflow: searching GitHub for relevant code, reading files, analyzing patterns, and creating issues. Previously, this required orchestrating multiple API calls, managing state, and handling errors across different services. NeuroLink unifies these operations into a single, coherent AI workflow. Let's build a code review agent that chains MCP tools: import { NeuroLink } from "@juspay/neurolink"; const neurolink = new NeuroLink(); // Connect GitHub MCP server await neurolink.addExternalMCPServer("github", { com