Hire an MCP Server Developer to Ship in 10 Days

A SaaS founder in Austin gets the same support ticket every week: "Can I just ask my AI assistant to pull this data instead of logging into your dashboard?" Their customers already live inside Claude or Cursor all day, and every time someone has to tab out to a browser to check an order status or trigger a report, that's friction that shows up as churn, not just annoyance.

The cost is not abstract. Engineering teams that try to build Model Context Protocol support in-house typically spend several weeks on transport plumbing, auth, schema design and registry submission before a single customer touches it — time that a two- or three-person SaaS team usually doesn't have spare, and that agencies bill out at rates far above a fixed project fee. Get the auth model wrong and you either lock out real users or expose data you didn't mean to.

This guide covers what a Custom MCP Server actually is, how the build runs end to end, what it costs, and what you need ready before you hire someone to build it — including our own offer page if you decide to move forward with Bharat AI Saathi.

What Custom MCP Server actually does

A Custom MCP Server sits between your existing API or database and any MCP-compatible AI client — Claude Desktop, Claude.ai, ChatGPT, Cursor, or an internal agent. It does not replace your product. It exposes a curated slice of it as "tools" an AI assistant can call directly, with proper authentication, rate limiting and logging in front of every request.

Concretely, this means taking 3 to 15 of your existing API endpoints — say, get_order_status, create_refund, search_inventory — and wrapping each one in an MCP tool definition with a typed schema, so an AI assistant knows exactly what arguments to send and what shape of response to expect. The server handles OAuth or API-key auth so each user only sees their own data, runs over streamable HTTP so it can be hosted rather than requiring a local process, and ships with conformance tests so it doesn't silently break when Anthropic or OpenAI update their client behavior.

How it works, step by step

1. Kickoff call — we map your existing API surface and agree which endpoints become tools versus which stay off-limits.
2. Tool and resource design — each endpoint gets a tool name, a JSON schema for inputs, and a defined response shape, reviewed with your team before any code is written.
3. Server build — implementation in TypeScript or Python SDK, with streamable HTTP transport and your chosen auth method (OAuth2 or scoped API keys).
4. Auth and rate limiting — per-user scoping so one customer's assistant can never see another customer's data, plus request throttling to protect your backend.
5. Testing — conformance tests against the MCP spec, plus manual runs from Claude, ChatGPT and Cursor to confirm tool calls resolve correctly.
6. Deployment and registry listing — hosted on Cloudflare Workers or Docker, with a README, client config snippets, and a listing in the official MCP registry.
7. Daily operation — your customers add the server URL to their AI client once; after that, every session can call your tools directly.

Trigger: Customer opens Claude Desktop and types "What's the status of order #48213, and if it's delayed, issue a $20 credit." Action: Claude calls get_order_status(order_id=48213) against your MCP server, receives {"status": "delayed", "days_late": 3}, then calls create_credit(order_id=48213, amount=20, reason="delay"), both authenticated as that specific customer via their scoped API key. Result: Claude replies with the order status and confirms the credit was issued — no dashboard, no support ticket.

Before and after

What it costs and what is included

There is no lock-in: you own the code, the registry listing, and the hosting account. Payback logic is straightforward rather than speculative — if the server removes even a handful of routine support tickets or manual lookups per week, the monthly care fee is typically small relative to the staff time it replaces, though we don't promise a specific number since it depends on your ticket volume and team cost.

Delivery timeline

Day 0 — Kickoff

Endpoint mapping call, access to staging API credentials, agreement on which tools ship in v1.

Days 1–7 — Build

Tool schema design, server implementation, auth wiring, and internal testing against Claude, ChatGPT and Cursor.

Days 8–10 — Go-live

Conformance testing, registry submission, README and client config handoff, and a walkthrough call with your team.

What you need to have ready

• API documentation or OpenAPI spec for the endpoints you want exposed
• A staging or sandbox environment with test credentials
• Decision on auth model: OAuth2 (if you already have an identity provider) or scoped API keys
• A technical contact who can approve schema decisions during the build
• Any compliance constraints (data residency, PII handling, industry-specific rules) documented up front
• Sign-off on which endpoints are explicitly out of scope for AI access

Doing it yourself vs done-for-you

DIY is a reasonable choice if you have an engineer who already knows the MCP SDKs and has slack in their sprint. It gets expensive fast if that engineer is your only backend hire and this becomes a multi-week side project.

Compliance and risk notes

• Scope each tool to the minimum data it needs to return — avoid exposing full database rows when a single field answers the use case.
• If you operate under GDPR, UK GDPR, or similar regimes, confirm where the MCP server and any logs are hosted, since data residency requirements may apply.
• Review the terms of service for Claude, ChatGPT and Cursor's connector/plugin policies before listing, as platform rules on data handling can change.
• Log every tool call with enough detail to audit later, but avoid logging full request payloads if they may contain sensitive customer data.
• This guide is not legal advice; consult your own counsel on consent and data-handling obligations specific to your industry.

Mistakes buyers make

• Exposing write actions too early — start with read-only tools (status checks, lookups) before adding tools that create refunds or modify records.
• Skipping the auth design conversation — assuming API-key auth is "good enough" without checking if per-user scoping is actually needed.
• Ignoring the registry listing — an unlisted server is harder for your own customers to discover and configure.
• Treating this as a one-time project — MCP spec versions change; without a care plan, servers quietly drift out of compatibility.
• Mapping every endpoint at once — starting with 3–6 well-tested tools is more reliable than launching 15 half-tested ones.

FAQ

What's the difference between an MCP server and a normal REST API wrapper?

An MCP server speaks the Model Context Protocol specifically, which AI clients like Claude and Cursor understand natively — tool schemas, resource definitions, and streamable transport are all part of the spec, not something you'd get from a generic REST wrapper.

Can I expose only some of my API, not all of it?

Yes — this is the recommended approach. Most builds start with 3–6 carefully chosen endpoints rather than the full API surface.

Do you need production access to my systems?

No, staging or sandbox credentials are sufficient for the build and testing phases; production access is typically only needed at go-live.

What happens after the MCP spec updates?

Spec-version updates are included in the $349/month care plan, so your server stays compatible without you tracking changes yourself.

Is TypeScript or Python better for this?

Both official SDKs are production-ready; the choice usually follows whichever language your existing API and team already use, to keep maintenance simple.

What if I only need local/stdio use, not hosted multi-tenant access?

That's the $1,500 tier — suited to indie SaaS tools where a single developer or small team runs the server locally rather than serving many customers over hosted auth.

Next step

Send us your API documentation or OpenAPI spec and a short note on which 3–6 actions you want exposed first, by email at support@bharataisaathi.com or on WhatsApp. Review the full scope on the Custom MCP Server offer page or see our general process on how we work. We'll confirm scope and timeline before any payment is made.