MCP server (Model Context Protocol)
MCP server (Model Context Protocol) — An MCP server is a component implementing Model Context Protocol — an open standard introduced by Anthropic in 2024 that defines how large language models (LLMs) communicate with external systems: databases, APIs, business tools. An MCP server exposes tools, resources, and prompts to AI clients in a standardized, auditable way.
How an MCP server works
An AI client (Claude Desktop, AI-enabled IDE, custom application) connects to the MCP server via stdio or Server-Sent Events. The server exposes three object types:
- Tools — functions the LLM can call (e.g.,
read_invoice(id),search_customers(query)). - Resources — readable reference data (documents, settings).
- Prompts — prompt templates for typical tasks in the domain.
Client and server exchange JSON-RPC messages. The LLM sees the available tools and decides which to use.
Common pitfalls
- Over-broad authorization scopes — agents get blanket access when they need minimum.
- No per-user rate limiting — one looped agent can DDoS your backend.
- Synchronous calls in high-traffic — without queues and backpressure, the server falls over at peak.
- No audit trail — non-starter for regulated industries.
- Tight coupling to one LLM provider — missing the point of a model-agnostic standard.
How fewtokensai helps
I’m currently building a production MCP server at inFakt — engineered to serve thousands of users via AI assistants, wired into a bookkeeping automation processing 300,000+ invoices monthly. I audit existing MCP deployments, design architectures from scratch, and ensure compliance (GDPR, EU AI Act). Get in touch if you want to deploy MCP correctly from the first iteration.
References
- MCP spec: modelcontextprotocol.io
- Anthropic SDK: github.com/modelcontextprotocol