How We Built an MCP Server for a US AI Startup (Real Case Study)

US AI startups often need their assistant to talk to real systems—CRM, database, support tickets. One of our clients came to us with exactly that need: they had an AI product but no way to connect it safely to their backend. Here is how we built an MCP server for them (anonymised, but the details are real).

A US-based AI startup had built a conversational assistant for their B2B product. Users could ask questions, but the AI could not check order status, pull account data, or create support tickets. They needed a secure, standardised way to expose these actions so their AI (and future AI clients) could call them. They did not want to hand credentials to the model or build one-off integrations for each vendor.

We recommended an MCP server that would expose a small set of tools: get_order_status, get_account_summary, create_support_ticket, and search_knowledge_base. The server would sit between their AI and their existing REST APIs. We would handle auth (API key per client), rate limiting, and logging. Timeline: 4 weeks for the first version.

Step 1: We mapped their existing APIs and chose which endpoints to expose as MCP tools. We designed the tool names and parameters so they were clear to both the AI and their team. Step 2: We implemented the MCP server in TypeScript using the official SDK, wiring each tool to their backend. Step 3: We added authentication, error handling, and docs. Step 4: We deployed to their cloud and ran tests with Claude and their own frontend. Handover included a short doc and a 2-week support window.

They now have an MCP server that any MCP-compatible client can use. Their AI can answer "What is the status of order #12345?" and "Create a support ticket for this issue" by calling the server. No credentials in the model, no duplicate logic. They can add more tools later without changing the protocol. The client is based in the US; we are in Bengaluru—async work and weekly calls made the collaboration smooth.

If you are a US or Canada AI startup and need your AI to act on your data, an MCP server is a proven approach. Scope and cost depend on how many tools you need and how complex your backend is. We typically deliver in 3–6 weeks. Hendoi Technologies builds MCP servers for US and Canada clients. Get in touch for a free consultation and quote.

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