MCP developers who expose your stack to every LLM client. One MCP server, plugged into Claude Desktop, Cursor, Windsurf, ChatGPT, Gemini. Auth handled, schemas tuned for tool-calling accuracy, transport picked to fit your deployment.
MCP engagements cover the six places custom Model Context Protocol servers stall without experienced engineers: teams building separate integrations per AI client (3–5× the work), brittle tool descriptions that cause the LLM to call the wrong tool or fill the wrong arguments, MCP servers deployed without auth (or with auth that doesn't respect tenant scope), wrong transport choice for the deployment (stdio vs. HTTP vs. SSE), schema hallucinations where the LLM invents parameters, and no rate limiting so one runaway agent takes the server down. MCP is the emerging standard — Claude, Cursor, Windsurf, ChatGPT, Gemini all support it. Built right, one server serves every client.
MCP (Model Context Protocol) is how you expose tools, data, and prompts to LLM clients in a portable way — write once, every MCP-compatible AI client can call it. The failure mode isn't 'MCP is hard'; it's that production MCP servers need auth, rate limits, audit logging, and schemas that the tutorial examples skip. This page is for hiring senior MCP engineers who have shipped production servers against every one of those failure modes.
Teams build one connector for Claude, another for Cursor, another for ChatGPT — 3× the engineering time, 3× the maintenance. One MCP server replaces the lot. We migrate existing per-client code to a single MCP surface when that's the right shape.
LLM tool-calling accuracy lives in the tool description. Vague descriptions cause the model to call the wrong tool, fill the wrong argument, or ignore the tool entirely. We rewrite descriptions against real traces, add examples, and tune until the accuracy shows up in evals.
Some tutorials show stdio-transport MCP servers running unauthenticated — fine for personal Claude Desktop use; a security hole in any multi-user or production deployment. We default to API-key auth (works everywhere) and deploy OAuth 2.0 + PKCE where the client supports it (Claude Desktop, Cursor, ChatGPT Desktop).
stdio is right for desktop-local tool access. HTTP is right for server-hosted production. SSE is right when the tool streams partial results. Picking wrong locks you out of the deployment shape you need; we pick on the scoping call based on client mix and infra.
Without strict JSON Schema validation and good descriptions, the LLM invents optional parameters or passes the wrong shapes. We use Zod (Node) or Pydantic (Python) for strict schemas, validate every call, and return structured errors the model can recover from.
One buggy agent can burn through database read quotas or hammer an internal API. Production MCP needs rate limits (per user, per tool, per minute) and an audit log of every call — timestamp, user, tool, inputs, result-size. We wire both by default.
The rescue path we run on every MCP engagement. Fixed price, fixed scope, no hourly surprises.
Send the repo. We audit the MCP app — auth, DB, integrations, deploy — and return a written fix plan in 48 hours.
Patch the highest-impact failure modes first — the RLS hole, the broken webhook, the OAuth loop. No feature work until production is safe.
Real migrations, signed webhooks, session management, error monitoring. Tests for every regression so MCP prompts can't re-break them.
Deploy to a portable stack (Vercel / Fly / Railway), hand back a repo your next engineer can read, and stay on-call for 2 weeks.
Send the repo. We audit the MCP app — auth, DB, integrations, deploy — and return a written fix plan in 48 hours.
Patch the highest-impact failure modes first — the RLS hole, the broken webhook, the OAuth loop. No feature work until production is safe.
Real migrations, signed webhooks, session management, error monitoring. Tests for every regression so MCP prompts can't re-break them.
Deploy to a portable stack (Vercel / Fly / Railway), hand back a repo your next engineer can read, and stay on-call for 2 weeks.
Evaluating MCP against another tool, or moving between them? Start here.
Most MCP engagements route through the MCP Server Build service.
Three entry points. Every engagement is fixed-fee with a written scope — no hourly surprises, no per-credit gambling.
Hyder Shah leads Afterbuild Labs, shipping production rescues for apps built in Lovable, Bolt.new, Cursor, v0, Replit Agent, Base44, Claude Code, and Windsurf — at fixed price.
Send the repo. We'll tell you what it takes to ship MCP to production — in 48 hours.
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