Service AI Engineering
An MCP server lets Claude, Cursor and dozens of other AI apps use your tools. Built once, used everywhere. We have built 30+ of them.
Why MCP
Before MCP, exposing your product to AI meant building a custom plugin for every client and rebuilding it every time the client changed. MCP is the standard that ended that. Build the server once, every compatible client uses it. The integration cost stops being linear in the number of AI tools your customers use.
An MCP tool, illustrated
What a tool call against an MCP server looks like. Claude invokes the tool, the server queries the customer's database, structured output comes back, agent responds. Scroll up and back down to replay.
What is the top reason customers cancelled last quarter?
What we build
Clean tool schemas. Documented auth. Real client testing. Distribution that works. We treat the server as a product surface, not a wrapper.
01
Tool naming, argument design, and error messages are an interface design problem. We get them right so models call your tools without coaxing or retry loops.
Tools that work on the first call because the schema says what it means.
02
Local STDIO for desktop Claude. Streamable HTTP for hosted deployments. We pick the right transport for your security model and ship both when you need both.
Same server runs in Claude Desktop and on production infra.
03
OAuth, SSO, scoped service tokens, per-user audit trails. The MCP server inherits your existing auth, never bypasses it. Your security team reviews it like any service.
Passes enterprise procurement without exception.
04
Where the workflow needs it, we wire MCP resource subscriptions so Claude sees data updates without re-querying. Real time without polling.
Latency drops from 800ms polls to sub-100ms push.
05
Every server we ship is tested with Claude Desktop, Claude Code, and the MCP Inspector. Real interactions, not mocked transports.
No "works in tests, fails in client" surprises.
06
NPM package, Docker image, Smithery listing, install one-liner, runbook. Your users install the server in 30 seconds, not 30 minutes.
Built around what the people using it actually need to do.
30+
MCP servers running in production across our team and customer infrastructure
From WordPress operations to malware scanners to documentation pipelines.
The tool layer
Tool names, argument shapes, and descriptions matter more than the implementation. We design them so the model calls them right the first time, not the third.
// tools/list-tickets.ts
import { z } from 'zod';
import { tool } from '../helpers';
export const listTickets = tool({
name: 'list_tickets',
description: 'List support tickets matching the given filter. Use this when the user asks about tickets in a status, assigned to a person, or filed in a date range.',
inputSchema: z.object({
status: z.enum(['open', 'pending', 'closed']).optional(),
assignee: z.string().email().optional(),
since: z.string().datetime().optional(),
limit: z.number().min(1).max(100).default(25),
}),
handler: async (args, ctx) => {
const tickets = await ctx.zoho.searchTickets(args);
return { content: [{ type: 'text', text: formatTickets(tickets) }] };
},
});
Process
01
Two weeks. We audit the API surface, identify the workflows the server should support, design the tool schemas, and pick the transport. You approve the schemas before any code.
Fixed scope, fixed price.
02
Two to four weeks. Tools, auth, resources, transport, distribution. Tested with Claude Desktop and MCP Inspector on every commit. Beta package by week three.
Real client testing from day one.
03
One to two weeks. Public release, install runbook, user docs, telemetry. We hand the server off with the same care as a public API.
Your team owns the server at week eight.
What it costs
A read-only server over one API and a multi-tenant server that writes to production systems are the same sentence and very different projects. Here is what moves the number.
What moves the number
Small, well-defined work is usually better handled as an hourly block than scoped as a project, and we will say so rather than inflate it into one.
Rough idea to delivery
Quick enquiry
Tell us which system it should reach and whether it needs to write as well as read. That one answer changes most of the estimate.
Prefer the full form? Tell us about your project
Common questions
Model Context Protocol is the standard for connecting AI clients (Claude Desktop, Cursor, Claude Code, etc.) to tools and data. If you build an MCP server for your product, every MCP-compatible AI client can use it. One integration, every client.
Four to eight weeks for a server with 10 to 20 tools and resource support. Two weeks discovery and tool design, two to four weeks build, one to two weeks testing with real clients and distribution setup. Faster if your APIs already exist.
Depends on the data. Local STDIO when the data lives on the user machine or behind a firewall. Hosted streamable HTTP when the data is centralized and the user authenticates remotely. We typically ship both for the same server.
OAuth flows for hosted servers, service tokens for STDIO, full per-user audit logging in both cases. The server never bypasses your existing IDP. Your security team reviews it like any service that touches customer data.
Yes. We have wrapped REST APIs, GraphQL endpoints, internal CLIs, and database query layers as MCP servers. Existing auth and rate limits flow through. Your engineering team keeps the underlying service unchanged.
MCP work is estimated against scope rather than sold at a list price. A read-only server over a documented API and a multi-tenant server that writes to production are very different projects. The section above sets out what moves the number.
Where to go next
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“A partner, very trustworthy, very reliable. Gets the job done.”
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Ready to open your tools to AI?
Discovery call is free. Fixed-price quote within 48 hours. NDA on request.
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