Your business has data — orders, customers, inventory, content, internal docs, vendor APIs, dashboards. ChatGPT and Claude can’t see any of it by default. The Model Context Protocol (MCP) is how you connect them.
Number 5 builds custom MCP servers and MCP integrations that turn your private data and workflows into tools an LLM can actually use — safely, with permissions, inside whatever client your team already pays for.
WHAT MCP IS (PLAIN ENGLISH):
> MCP is an open protocol — introduced by Anthropic, now adopted across the industry — that lets any LLM client (Claude Desktop, Cursor, ChatGPT, custom agents) talk to any external system through a standardized interface.
> Think of it as “USB for AI assistants.” Instead of building a one-off integration for every chatbot, you build one MCP server — and every MCP-aware assistant can use it.
> A server exposes
tools (actions the LLM can take),
resources (data the LLM can read), and
prompts (templates) over a typed schema. The LLM picks what to call. You stay in control of what’s exposed.
WHY YOUR BUSINESS NEEDS ONE:
> Your team is already pasting screenshots into ChatGPT and Claude. An MCP server replaces that with structured access — the model reads the real data, in real time, with audit logs.
> A single MCP server can be reused across Claude, ChatGPT, Cursor, internal agents, and any future LLM client — no rebuild required when the model landscape shifts.
> Permissions and security live in the server, not in prompts. You decide who sees what, what writes are allowed, and what stays read-only.
> It’s the cleanest way to make ChatGPT and Claude useful to
operators — not just engineers.
WHAT WE BUILD:
>
Custom MCP servers over your own data and APIs — Postgres, MySQL, Shopify, Stripe, HubSpot, Salesforce, Klaviyo, Notion, Linear, Google Workspace, internal warehouses, anything with an API or a database.
>
Internal MCP integrations between SaaS tools so an agent can pull from one system and push into another — without a custom Zapier-style wiring job for every workflow.
>
ChatGPT & Claude tool integrations — deployed servers that show up in your team’s desktop and web clients, ready to use.
>
Security, auth & permissions — OAuth flows, scoped tokens, row-level access controls, audit logging, rate limits, dry-run modes for destructive actions.
>
Hosting & ops — we deploy to your infra (AWS, Cloudflare Workers, Fly.io, Vercel) or to ours, with monitoring and on-call.
HOW THE ENGAGEMENT WORKS:
> Week 1 — discovery. We map the data, the workflows, and the people who will actually use it.
> Weeks 2–3 — build. First MCP server in a staging client (Claude Desktop or Cursor) for your internal team to break.
> Week 4 — harden & ship. Auth, permissions, logging, deploy. Hand-off doc and Loom walkthrough.
> Optional month-to-month engagement afterward — new tools, new servers, new integrations as your operation evolves. No long-term contracts.
WHO IT’S FOR:
> Operators wasting hours pasting context into ChatGPT and Claude
> Companies with valuable internal data trapped in Postgres, Shopify, or a dozen SaaS tools
> Teams that already use Cursor or Claude Code for engineering and want the same leverage in finance, ops, and growth
> Founders building agentic products who need a reusable MCP layer instead of one-off integrations per model
FAQ:
>
What is a Model Context Protocol (MCP) server?
An MCP server is a small program that exposes your business’s data and actions to LLM clients like ChatGPT, Claude, and Cursor through a standardized open protocol. Instead of writing a custom integration for every chatbot, you build one MCP server and every MCP-aware AI assistant can call it. The server defines exactly which tools, resources, and prompts the LLM can use, with auth and permissions enforced server-side.
>
How is MCP different from a normal API?
A normal API is built for engineers writing code. An MCP server is built for an LLM to discover and use autonomously: each tool is described in natural language with a typed schema, the model decides which tools to call based on the user’s request, and the server handles auth, validation, and audit logging. You can wrap an existing API in an MCP server in days — you don’t rebuild your stack.
>
How long does it take to build a custom MCP server?
Most custom MCP servers ship in 2–4 weeks. A single-source server (e.g. read-only access to your Postgres warehouse with five core tools) can be in production in under a week. Multi-system servers with write actions, OAuth, and per-user permissions typically take 3–4 weeks. Number 5 builds, deploys, monitors, and iterates — you don’t need an internal AI engineer to own it.
>
Is MCP secure for production data?
Yes, when it’s built right. Security lives in the server, not in the LLM’s prompt. Number 5 implements scoped tokens, OAuth, row-level access, dry-run modes for any destructive action, full audit logging, and rate limits. The LLM only sees what your permission model allows it to see. You can run servers on your own infrastructure end-to-end.
>
Which LLM clients support MCP?
Claude Desktop, Claude Code, Cursor, and a growing list of agent runtimes including OpenClaw natively support MCP. ChatGPT integrates MCP through GPT Actions and tool integrations. The protocol is open, so any new LLM client launched in the next year is likely to support it — which is why building one MCP server is more durable than building five model-specific integrations.
RELATED:
>
Generative Engine Optimization (GEO)
>
SaaS Stack Audit & AI Cost Reduction
>
Prototype to Production
>
Full Technology Stack
Ready to expose your business to AI assistants the right way?
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