afterbuild/ops
§ S-06/mcp-server-build

Your tools, exposed to every LLM. Via MCP.

One MCP server. Works with Claude Desktop, ChatGPT, Cursor, Windsurf, Gemini. Your team chooses the AI client; your data layer stays under your control. $5,999.

$5,999fixed fee
2 weeksship date
3–8tools · every MCP client
Quick verdict

MCP Server Build — a 2-week engagement that inventories 3–8 tools your stack should expose to LLM clients, drafts JSON schemas tuned for tool-calling accuracy, ships a production MCP server with authentication (API keys or OAuth where the client supports it), picks the right transport (HTTP / SSE / stdio), adds rate limiting and action logging for audit, and deploys (Docker, Vercel, or self-hosted). One server works with Claude Desktop, Cursor, Windsurf, ChatGPT, Gemini — every MCP-compatible client simultaneously. Ships with a runbook so your team can add tools, rotate auth, and inspect usage on their own.

§ 01/mcp-patterns

Five MCP patterns we ship most.

Most MCP builds fit one of these five shapes. The scoping call maps your specific need to the nearest shape and confirms the tool inventory.

MCP Server Build · 5 shipping patterns · works with every MCP client
SituationTodayWhat the build ships
Your team uses 3+ AI clients (Claude, ChatGPT, Cursor, Windsurf)Each AI client wants its own integration — 3× the work, 3× the maintenanceOne MCP server exposing your tools to every AI client simultaneously
Analysts / support / ops ask LLMs questions your internal DB could answerNo way to give the LLM safe, scoped access to your dataMCP server with read-only query tools, scoped by user and rate-limited
You want engineers to automate deploys / rollbacks / status queries via Claude or ChatGPTAPI tokens floating in chat logs; no audit trail on LLM-initiated actionsMCP server with OAuth-scoped tools, action logging, and kill-switch
You have a niche SaaS product and want AI clients to expose it to usersNo standard way to describe your API to an LLM tool-callerMCP server with schemas tuned for tool-use accuracy; ship once, every client works
Built a Claude tool-use spec and now need it to work in Cursor and ChatGPT tooLocked into one client; porting is a rewriteMCP migration — same tools, same auth, every MCP-compatible AI client works
§ 02/2-week-schedule

The 2-week MCP-build schedule.

Week 1 ends with the server answering a Claude Desktop ping. Week 2 ends with the server deployed to your infra, tools wired to production data, auth live, runbook handed off.

  1. W1week 1

    Scoping + tool inventory + schema

    Week 1 starts with a scoping call where we inventory the 3–8 tools your stack should expose (queries, writes, workflows) and rank them by value and risk. We draft the JSON schema for each tool, tune descriptions for LLM tool-calling accuracy, and pick transport (HTTP for server-hosted, SSE for streaming-heavy, stdio for desktop clients). By end of week 1 the server answers a test ping from Claude Desktop.

  2. W2week 2

    Implementation + auth + deployment

    Week 2 ships tool implementations (connecting to your actual stack — Postgres, Stripe, internal APIs), authentication (API keys by default; OAuth where the client supports it), rate limiting per user / per tool, action logging for audit, and deployment (Docker image, Vercel functions, or self-hosted depending on your infra). Closes with a runbook: how to add a tool, rotate auth, inspect logs, kill abusive sessions.

§ 03/sample-tool

A sample MCP tool we shipped last month.

Tool with Zod schema, OAuth-scoped tenant access, rate limit, and audit logging. For reference, see the official MCP spec.

mcp-server/tools/query-orders.ts
typescript
01// mcp-server/tools/query-orders.ts (trimmed)02import { Server } from "@modelcontextprotocol/sdk/server/index.js";03import { z } from "zod";04import { db } from "@/lib/db";05 06const QueryOrdersInput = z.object({07  customer_id: z.string().describe("Customer ID to query orders for"),08  status: z.enum(["pending", "paid", "refunded", "all"]).default("all"),09  limit: z.number().int().min(1).max(100).default(20),10});11 12export function registerQueryOrdersTool(server: Server) {13  server.tool(14    "query_orders",15    "List orders for a given customer. Returns up to 100 rows. Read-only; scoped to the authenticated user's tenant.",16    QueryOrdersInput,17    async (input, ctx) => {18      const { tenant_id, user_id } = ctx.auth; // OAuth-scoped from bearer token19      const rows = await db.query.orders.findMany({20        where: {21          tenant_id,22          customer_id: input.customer_id,23          ...(input.status !== "all" && { status: input.status }),24        },25        limit: input.limit,26      });27 28      // Audit: every tool call is logged for later review29      await db.mcpAuditLog.create({30        tenant_id,31        user_id,32        tool: "query_orders",33        input,34        row_count: rows.length,35        timestamp: new Date(),36      });37 38      return { content: [{ type: "text", text: JSON.stringify(rows, null, 2) }] };39    },40  );41}
Every tool ships with a schema, auth scope, and audit log — this is the shape of production MCP.
§ 04/ledger

What the build delivers.

Five deliverables. Your server. Every MCP-compatible AI client.

§ 05/engagement-price

Fixed fee. 3–8 tools. One server.

One MCP server per build. If you need a second server for a different tool domain, the second build runs at $3,999 (leveraging the patterns from the first).

mcp
price
$5,999
turnaround
2 weeks
scope
Production MCP server · 3–8 tools · auth · transport · deployment · runbook
guarantee
Works with Claude, Cursor, Windsurf, ChatGPT, Gemini — every MCP client.
book the build
§ 06/vs-alternatives

MCP Server Build vs per-client integrations vs vendor connectors.

Four dimensions. The lime column is what you get when you build on the MCP standard instead of locking yourself to one AI client.

MCP Server Build · one server · every MCP client · OAuth · audited
DimensionPer-client integrationsVendor connectorClaude tool-use onlyAfterbuild Labs MCP
ApproachSeparate integrations per clientWait for vendor connectorCustom Claude tool-use specAfterbuild Labs MCP Server Build
Price3–5× the engineering timeFree but slow and limitedLocked to Claude; rebuild for every other client$5,999 fixed · 2 weeks · works with every MCP client
Client compatibilityWhatever you builtWhatever the vendor supportsClaude onlyClaude Desktop · Cursor · Windsurf · ChatGPT · Gemini
Auth modelInconsistent per clientVendor's choice, often weakClaude session tokenAPI keys or OAuth · scoped · audited · rate-limited
§ 07/fit-check

Who should book the build (and who should skip it).

Book the build if…

  • Your team uses more than one AI client (Claude Desktop + Cursor is the most common combo).
  • You want engineers, analysts, or support to call your internal tools from an AI client safely.
  • You already built a Claude tool-use spec and need it to work in Cursor, Windsurf, ChatGPT too.
  • You're a SaaS vendor and want to expose your product to AI clients as a distribution channel.
  • You want OAuth-scoped, per-user, rate-limited, audited access to your data — not API key share-alike.

Do not book the build if…

  • You only use Claude and never plan to use another AI client — Claude tool-use is simpler.
  • You need a full autonomous agent with planning + tools — book AI Agent MVP ($9,499) instead.
  • You need RAG over docs, not tool-calling — book RAG Build ($6,999) instead.
  • You don't have internal tools worth exposing — MCP is plumbing, not value on its own.
  • Your stack is behind a VPN or air-gapped network — MCP can work there but needs infrastructure scoping.
§ 08/build-faq

MCP Server Build — your questions, answered.

FAQ
What is MCP in plain English?
Model Context Protocol (MCP) is a standard for exposing tools, data, and prompts to LLM clients. Think of it like 'REST for AI' — you write the server once, and every MCP-compatible client (Claude Desktop, Cursor, Windsurf, ChatGPT, Gemini) can call your tools without a custom integration per client. It was introduced by Anthropic in late 2024 and has been adopted by most major AI client vendors.
Which AI clients support MCP today?
Claude Desktop (first-class, stdio and HTTP), Cursor (HTTP/SSE), Windsurf (HTTP/SSE), ChatGPT Desktop (added support in 2025), Zed editor, and several smaller editor clients. OpenAI's Responses API added MCP tool support in late 2025, and Gemini has experimental support. The list grows every quarter — if MCP is a standard, this is the direction.
Is OAuth supported?
Yes — MCP supports OAuth 2.0 + PKCE via the draft spec shipped in Q1 2026. Claude Desktop, Cursor, and ChatGPT Desktop all support OAuth flow for MCP servers today. We default to API-key auth (simpler, works everywhere) but deploy OAuth when your tools expose sensitive user data (e.g., per-user mailbox access, per-user billing data). Enterprise OAuth with SSO is a Q2 2026 roadmap item that we cover in the scoping call.
How does this compare to a custom OpenAI Assistants or Claude tools integration?
OpenAI Assistants tools are locked to OpenAI. Anthropic's tool-use spec is locked to Claude. MCP is portable — one server, every client. If you're confident you'll only ever use one AI client, the vendor-specific path is simpler. If you expect your team or users to switch clients (or use multiple), MCP is the bet. We map the trade-off explicitly on the scoping call.
Do we need one MCP server per team or per tool?
One MCP server per tool domain is typical. E.g., one server for 'customer data' (query, update, export), one for 'deploy / ops' (deploy, rollback, status), one for 'billing' (invoice, refund). Each server is small, owns its auth scope, and can be deployed independently. That's cleaner than one monster server exposing everything — and MCP clients happily connect to multiple servers at once.
What's the ongoing maintenance cost?
MCP servers are small Node / Python services. Typical ongoing cost: whatever your hosting charges for a small container or a few serverless functions ($10–$50/month). Engineering maintenance is adding new tools when the business needs them; each added tool takes a few hours following the patterns we leave behind. Runbook covers auth rotation, tool deprecation, and client compatibility checks.
Next step

Expose your stack to every LLM. Via MCP.

Two weeks. $5,999 fixed. A production MCP server exposing 3–8 tools from your stack — auth, rate limits, audit logging, deployed to your infra, works with every MCP-compatible AI client.

Book free diagnostic →