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.
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.
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.
| Situation | Today | What 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 maintenance | One MCP server exposing your tools to every AI client simultaneously |
| Analysts / support / ops ask LLMs questions your internal DB could answer | No way to give the LLM safe, scoped access to your data | MCP server with read-only query tools, scoped by user and rate-limited |
| You want engineers to automate deploys / rollbacks / status queries via Claude or ChatGPT | API tokens floating in chat logs; no audit trail on LLM-initiated actions | MCP server with OAuth-scoped tools, action logging, and kill-switch |
| You have a niche SaaS product and want AI clients to expose it to users | No standard way to describe your API to an LLM tool-caller | MCP 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 too | Locked into one client; porting is a rewrite | MCP migration — same tools, same auth, every MCP-compatible AI client works |
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.
- 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.
- 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.
- 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.
- 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.
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.
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}What the build delivers.
Five deliverables. Your server. Every MCP-compatible AI client.
- 01Production MCP server exposing 3–8 tools from your stack
- 02Authentication (API keys or OAuth when supported)
- 03Schema + tool descriptions tuned for LLM tool calling
- 04Transport: HTTP, SSE, or stdio — your choice
- 05Deployment + runbook (Docker, Vercel, or self-hosted)
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).
- 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.
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.
| Dimension | Per-client integrations | Vendor connector | Claude tool-use only | Afterbuild Labs MCP |
|---|---|---|---|---|
| Approach | Separate integrations per client | Wait for vendor connector | Custom Claude tool-use spec | Afterbuild Labs MCP Server Build |
| Price | 3–5× the engineering time | Free but slow and limited | Locked to Claude; rebuild for every other client | $5,999 fixed · 2 weeks · works with every MCP client |
| Client compatibility | Whatever you built | Whatever the vendor supports | Claude only | Claude Desktop · Cursor · Windsurf · ChatGPT · Gemini |
| Auth model | Inconsistent per client | Vendor's choice, often weak | Claude session token | API keys or OAuth · scoped · audited · rate-limited |
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.
MCP Server Build — your questions, answered.
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 →