MCP Developer for Hire: Ship a Production MCP Server in 10 Days

Looking for an MCP developer for hire? Get a production MCP server with auth, tests, and registry listing built in 10 days from $3,000.

A SaaS founder in Austin gets an email from a customer asking whether their product "works with Claude." Not a feature request exactly — the customer already uses Claude Desktop for most of their daily work and wants to pull data from your dashboard without opening a browser tab. You say you'll look into it. Three months later it's still on the backlog, because nobody on a four-person team has time to learn the Model Context Protocol spec, wire up OAuth, write conformance tests, and figure out where to host a streamable HTTP server.

That delay has a cost. Every week without an MCP integration is a week a competitor might ship one first, and in agentic-AI-adjacent categories (data APIs, fintech tooling, dev platforms), "works inside Claude/Cursor/ChatGPT" is becoming a checkbox buyers look for before they sign. For a team already stretched across product and support, building this in-house usually means either a contractor search that eats two weeks before code gets written, or an engineer context-switching off the roadmap for a sprint.

This guide covers what a Custom MCP Server actually is, how the build runs day by day, what it costs, and what you need ready before kickoff — see the full offer at Bharat AI Saathi's Custom MCP Server page for scope and pricing detail.

What Custom MCP Server actually does

The Model Context Protocol is the open standard Anthropic published so AI assistants can call external tools in a structured, authenticated way instead of scraping screens or guessing at APIs. When you hire an MCP developer for this, they take your existing SaaS, database, or REST API and expose a defined slice of it — say, "create invoice," "search customer records," "get shipment status" — as MCP tools with typed schemas, so Claude, ChatGPT, or Cursor can call them directly inside a conversation.

Concretely, the deliverable is a server (built on the TypeScript or Python MCP SDK) that:

Maps 3–6 of your API endpoints to MCP tools (up to 15 on larger scopes), each with input/output schemas an LLM can reason about
Handles auth via OAuth or API keys, so each user's calls run under their own permissions, not a shared service account
Runs over streamable HTTP transport (or stdio-only for a lighter, local-only tier)
Ships with conformance tests, rate limiting, and logging, then gets deployed to Cloudflare Workers or Docker
Gets listed in the official MCP registry with a README and ready-to-paste client configs for Claude, Cursor, and ChatGPT

The result is not a chatbot bolted onto your product — it's your product becoming a set of callable tools that any MCP-compatible assistant can use on a customer's behalf, inside whatever workflow they're already running.

How it works, step by step

Kickoff call. You walk through your API or database, name the 3–6 actions that matter most (the ones your support team gets asked to do manually), and flag any auth constraints.
Tool design. The developer proposes a tool/resource map — tool names, parameters, return schemas — and you sign off before code starts.
Server build. Implementation happens against a staging copy of your API, using the SDK matching your stack (TypeScript or Python).
Auth wiring. OAuth or API-key flow is connected so each caller authenticates as themselves, not as a blanket admin credential.
Testing. Conformance tests confirm each tool matches its schema; rate limiting and logging are added so a misbehaving client can't hammer your backend.
Deployment and listing. Server goes live on Cloudflare Workers or Docker, then gets submitted to the MCP registry with README and client configs.
Daily operation. Your customers (or your own team) add the server to Claude Desktop, Cursor, or a ChatGPT connector, and start calling your product from inside their AI assistant.

Trigger: User in Claude Desktop types "What's the status of order #4471 and has it shipped?" Action: Claude calls the get_order_status MCP tool exposed by the server, authenticated as that user via their API key. Action: Server queries the orders database, returns {status: "shipped", carrier: "UPS", tracking: "1Z999AA1..."}. Claude: "Order #4471 shipped via UPS, tracking number 1Z999AA10123456784."

Before and after

Situation	Before	After
Customer wants data from Claude	They log into your dashboard manually	They ask Claude directly, which calls your MCP server
Support ticket: "can you look up X"	Human agent checks the database	Customer's own AI assistant queries it via an authenticated tool call
API discoverability for AI tools	Undocumented REST endpoints, no LLM-readable schema	Typed tool schemas listed in the official MCP registry
Access control for AI-driven calls	Shared API key, hard to audit	Per-user OAuth, each call traceable to a real account
Engineering time to support this	Open-ended, no one owns it	Fixed 10-day build, monthly care plan after
Sales conversations with AI-forward buyers	"Not yet, it's on the roadmap"	"Yes, here's our MCP server in the registry"

What it costs and what is included

Item	Price	Notes
Custom MCP Server (full build, up to 15 tools)	$3,000 one-time	Streamable HTTP, OAuth/API-key auth, registry listing
Local/stdio-only tier	$1,500 one-time	For indie SaaS not needing hosted remote access
Monthly care plan	$349/month	Hosting, spec-version updates, 2 new tools/month, uptime monitoring
Third-party platform costs (hosting, model usage, telephony if applicable)	Billed direct	You keep your own accounts, no markup, no lock-in

There's no invented ROI figure to quote here, but the payback logic is straightforward: one build sprint from an in-house engineer typically costs more in salaried time than the flat $3,000 setup fee, and the $349/month care plan is usually cheaper than the maintenance burden of keeping a spec-compliant server updated yourself. If the integration helps close even one enterprise deal that asked for AI-assistant compatibility, it has likely paid for itself.

Delivery timeline

Day 0: Kickoff

Call to confirm endpoints, auth model, and the 3–6 (or up to 15) tools to expose. Access credentials for a staging environment are shared.

Days 1–7: Build

Tool schema design, server implementation, auth wiring, and internal testing happen in this window. You get a mid-build checkpoint around day 4–5 to review the tool list and sample calls.

Days 8–10: Go-live

Conformance tests finalized, deployment to Cloudflare Workers or Docker, registry submission, README and client configs delivered. Server is live and callable from Claude, Cursor, or ChatGPT by day 10.

What you need to have ready

API documentation or database schema access for the endpoints you want exposed
A staging environment or sandbox credentials (not production, for the build phase)
Decision on auth model: OAuth app registration, or API-key issuance process
List of the 3–6 (or up to 15) actions you want AI assistants to be able to perform
Any compliance constraints — data residency requirements, fields that must never be exposed to an LLM, PII handling rules
A technical contact who can approve the tool/schema design before the build starts

Doing it yourself vs done-for-you

Factor	DIY (in-house build)	Custom MCP Server (done-for-you)
Time to first working tool	Days to weeks, depending on engineer familiarity with the spec	Live within 10 days, fixed
Spec compliance	Manual tracking of MCP spec updates	Included in the $349/month care plan
Registry listing	Requires learning submission requirements	Handled as part of delivery
Common failure point	Auth done as one shared key instead of per-user OAuth	Built with per-user auth from the start
Ongoing maintenance	Falls on whoever built it, often deprioritized later	Dedicated monthly care plan with 2 new tools/month
Cost structure	Engineer time, hard to predict	Fixed $3,000 (or $1,500) + $349/month
Best fit	Teams with a spare engineer and no near-term deadline	Teams that want it live and stable this month

Compliance and risk notes

Scope the tool set tightly — expose only the actions and fields the client's use case actually needs; don't mirror your entire API surface into MCP tools by default.
If your product handles regulated data (health, financial, personal), confirm with your own counsel which fields, if any, should never be returned to an LLM-driven caller.
Data residency matters if your customers are in the EU or UK — check where the Cloudflare Workers or Docker deployment will physically run and whether that satisfies your contractual obligations.
Auth should be per-user (OAuth or scoped API keys), not a single shared credential, so every AI-driven action is attributable to a real account for audit purposes.
MCP registry and client platform (Anthropic, OpenAI, Cursor) policies change; the care plan exists specifically to track and apply those updates rather than leaving the server to drift out of compliance.
This is general guidance, not legal advice — consult your own counsel on data handling obligations specific to your industry and jurisdiction.

Mistakes buyers make

Exposing too many endpoints at once. Start with 3–6 high-value tools; a sprawling 15-tool server on day one is harder to test and harder for an LLM to use well. Fix: scope tight, expand via the monthly care plan.
Using a shared API key for all callers. This makes every action untraceable to a specific user. Fix: insist on per-user OAuth or scoped keys from the design phase.
Skipping the registry listing. Some teams deploy the server but never list it, so nobody outside their own team can discover or connect to it. Fix: registry listing is part of the standard deliverable — make sure it happens.
No plan for spec updates. MCP is an evolving standard; a server built once and never touched again will eventually break against newer clients. Fix: the $349/month care plan covers this specifically.
Treating this as a marketing gimmick instead of a real integration. If the underlying API is unreliable or undocumented, the MCP layer will just surface those problems to an AI assistant instead of a human. Fix: clean up the API contract before or during the build, not after.

FAQ

Do I need to already have a public API?

No. The server can wrap an internal database or a private API — it doesn't need to be public beforehand. It just needs stable, documented endpoints the developer can build against during staging.

Can this work with ChatGPT as well as Claude?

Yes. MCP is an open standard, and the deliverable includes client configs for Claude, Cursor, and ChatGPT connectors, so the same server serves all three without separate integrations.

What's the difference between the $3,000 tier and the $1,500 tier?

The $1,500 tier is stdio-only and local — good for an indie SaaS where users run the server on their own machine. The $3,000 tier includes streamable HTTP hosting, OAuth, and registry listing for a remote, multi-user setup.

Who pays for hosting and model usage?

You do, directly to the platform (Cloudflare, OpenAI, Anthropic, etc.) — there's no markup added. This keeps your bills transparent and means there's no lock-in to a reseller relationship.

What happens if the MCP spec changes after launch?

That's covered under the $349/month care plan, which includes spec-version updates, uptime monitoring, and two new tools added per month, so the server doesn't quietly go stale.

How many tools can the server expose?

The standard scope covers 3–6 tools, with room to grow to 15 within the same build if your use case needs it. Additional tools beyond that can be added later through the care plan.

Next step

Email support@bharataisaathi.com or message via WhatsApp with your API docs or database schema and the 3–6 actions you want exposed to AI assistants. Review full scope and pricing on the Custom MCP Server page, and see the delivery process on How We Work. A kickoff call is usually all it takes to start the 10-day clock.