JSON-RPC

Build Production Go MCP Servers: The Definitive Guide Introduction: The Rise of Agentic Infrastructures The ecosystem of AI is shifting from passive chat boxes to autonomous agents. Building a Go MCP server allows developers to safely connect AI models with databases and APIs. Anthropic’s Model Context Protocol (MCP) establishes this secure, bidirectional communication between AI client environments and backend service APIs. When we deployed our first suite of agentic tools, our Claude desktop client crashed immediately due to a single un-routed fmt.Println statement. We quickly realized that while spinning up a simple Python-based calculator running over standard I/O is trivial, building a highly resilient MCP server in an enterprise environment requires a completely different level of engineering rigor. ...

Prerequisite: Familiarity with the concepts introduced in Part 1 — Protocol. Review it first if the terminology in this part is unfamiliar. Part 2 — Building Production-Grade MCP Servers in Go/Python Answer-first: Building production-grade MCP servers requires adhering to Domain-Driven Design (DDD) bounded contexts, stateless scaling, and structured JSON-RPC error handling. By using Go memory buffer pools (sync.Pool) and context cancellation timeouts, production MCP servers process high-concurrency tool calls with sub-15ms execution latency. ...

Prerequisite: Familiarity with the concepts introduced in Executive Summary. Review it first if the terminology in this part is unfamiliar. Part 1 — MCP Core Protocol Architecture & Transport Evolution Answer-first: Model Context Protocol (MCP) relies on dual-transport abstractions (stdio for zero-overhead local process IPC and SSE for remote network RPCs) transmitting JSON-RPC 2.0 messages. Understanding the protocol state machine ensures sub-20ms message framing across distributed AI agent tool servers. ...

Executive Summary — Model Context Protocol in Production: The Control Plane of AI Answer-first: Model Context Protocol (MCP) establishes an open, vendor-agnostic JSON-RPC 2.0 standard for connecting AI agents to enterprise data sources, tools, and prompts. Replacing ad-hoc custom integrations with production MCP Gateways enforces 100% data isolation, mTLS identity verification, and central telemetry auditing across enterprise microservices. Key Takeaways: Unified JSON-RPC Standard: Eliminates custom API integration glue code across LLM frameworks (Claude, Cursor, LangChain). Zero Trust Identity Enforcement: Uses OAuth 2.1 PKCE and SPIFFE/SPIRE mTLS certificates to authenticate AI agent tool calls. Sub-20ms Transport Overhead: High-performance SSE and stdio transport layers minimize communication latency. Before the introduction of the Model Context Protocol (MCP), connecting AI agents to enterprise data stores was fragmentation chaos. Every developer built custom glue code to connect LLMs to PostgreSQL databases, JIRA APIs, internal GitHub repos, and Kubernetes clusters. ...