MCP Server
Development
Most teams don't need another “AI integration.” They need a reliable way to connect models to real systems without breaking everything. That's what we build.
What MCP actually is
The protocol behind
AI that does things
Model Context Protocol (MCP) is an open standard — created by Anthropic in late 2024 — that defines how AI models interact with external systems. It works over JSON-RPC and introduces three primitives: tools (actions the AI can perform), resources (structured data it can read), and prompts (reusable interaction patterns).
Before MCP, connecting AI to a database or API meant writing one-off glue code for every system. MCP standardises that layer. One protocol, any AI client, any backend system. It's what turns a chatbot into something that can actually check your inventory, update a booking, or pull a report from your CRM.
The ecosystem is growing fast. Claude, ChatGPT, AntiGravity, Cursor, and dozens of other AI clients already support MCP natively. If your systems are MCP-enabled, they're accessible to all of them.
Without MCP
- Custom glue code for every AI-to-system connection
- Integrations that break when either side changes
- No standard way to control what AI can access
- Each new AI client needs its own integration
- Security is bolted on, not built in
What we build
Servers, clients, and the
layer between them
We build production-ready MCP servers and clients using popular libraries including TypeScript, Python and .Net. The goal isn't a demo — it's a system that handles real traffic, real data, and real edge cases without falling over.
MCP Server Development
We define your tools, resources, and prompts, wire them to your backend systems, and handle auth, permissions, and error cases. Your existing APIs and databases stay untouched — the MCP server sits in front of them as a controlled intermediary.
MCP Client Development
We build MCP client capabilities into your applications so they can consume MCP servers — yours or third-party. This means your AI assistant can access Slack, Google Workspace, your CRM, and your internal tools through one protocol instead of a dozen brittle integrations.
Auth & Governance
Every MCP server we build includes scoped permissions, role-based access control, and audit logging. AI doesn't get blanket access to your systems — it gets exactly the access it needs and nothing more. OAuth, API keys, and custom auth flows as required.
How we approach it
Most MCP implementations
fail for predictable reasons
Vague tool definitions that confuse the model. Too much business logic pushed into prompts where it's unreliable. No permission model, so the AI can access anything. No error handling, so one failed call crashes the whole interaction. We've seen all of these and we design around them from the start.
Contract-first design. We define exactly what the AI can and can't do before writing any code. Clear tool schemas with typed inputs and predictable outputs. Business rules stay server-side where they belong — not in the prompt where the model might improvise.
Production-first mindset. We build for reliability, not demos. That means proper error handling, latency management, retry logic, and monitoring. An MCP server that works in a demo but times out under real load isn't useful to anyone.
Stack flexibility. We typically build with the TypeScript or Python MCP SDKs, connecting to REST APIs, databases, or whatever your systems expose. We adapt to your environment — we don't force you onto ours.
Where MCP actually helps
Concrete examples, not
abstract promises
Customer Service That Acts
AI checks booking details in your database, modifies reservations, processes refunds. Not “I'll pass that to a colleague” — actually doing it, with the right permissions and audit trail.
Quote Generation
AI talks with clients, checks real-time inventory via MCP tools, calculates pricing from your rate tables, and generates the quote. What took hours takes minutes.
Internal Knowledge Access
Expose internal documentation, product data, or operational records as MCP resources. AI retrieves structured, up-to-date information instead of guessing from training data.
Multi-System Orchestration
Instead of building one-off integrations between every pair of systems, MCP gives AI a single standardised way to coordinate across CRMs, ERPs, email, calendars, and custom tools.
Honest guidance
When MCP is the
wrong choice
MCP is powerful, but it's not always the right answer. If you just need a simple automation — “when X happens, do Y” — something like Zapier or a basic webhook is faster and cheaper. MCP adds value when AI needs to make decisions about which tools to use and how to use them.
If your backend systems don't have stable APIs or any programmatic interface, MCP can't magically create one. You'll need that foundation first. And for quick one-off prototypes where reliability isn't critical, the overhead of proper MCP architecture isn't justified.
We'll tell you this upfront. There's no point building an MCP server when a simpler solution does the job. We'd rather point you in the right direction than sell you something you don't need.
MCP is a good fit when
- AI needs structured access to real systems
- You want reusable integrations, not one-offs
- Control and auditability matter
- You need to support multiple AI clients
- Your systems have existing APIs to build on
MCP vs alternatives
Why not just use
direct API calls?
You can wire AI directly to your APIs. We've done it. The problem is that every new AI client, every new model, every new use case needs its own integration code. MCP standardises that layer so you build the connection once and any MCP-compatible client can use it.
Direct API Calls
Fast to build, but tightly coupled to one AI client. No standard permission model. Every new use case needs new code. Fine for prototypes, problematic at scale.
Automation Tools
Zapier, N8N, Make, and similar tools are great for “if this then that” workflows. But they don't give AI the ability to reason about which action to take. Different problem, different tool.
MCP
Standardised, reusable, secure. Build once, use across any MCP client. Built-in permission model. Designed for AI that needs to decide what to do, not just follow a script.
Why Provanta
We've built these.
Not just read about them.
We've been building MCP servers and clients since the protocol was released. Not as experiments — as production systems that handle real data. We know where MCP shines, where it breaks, and how to architect around the rough edges.
We'll push back if something doesn't make sense for your situation. We focus on making it usable — not just technically correct.
Let's Talk →Common questions
MCP development
FAQs
What is an MCP server?
It's a service that exposes your systems to AI using the Model Context Protocol. It defines tools (actions), resources (data), and prompts (interaction patterns) that AI clients can discover and use through a standardised JSON-RPC interface.
How long does it take?
A focused MCP server connecting to one or two systems typically takes 2–4 weeks. More complex setups with multiple backends, custom auth, and extensive tool definitions take 6–10 weeks. We'll give you a realistic timeline upfront.
Can MCP work with our existing systems?
Yes — if your systems have APIs, database access, or any programmatic interface. MCP sits on top of what you already have. We don't need to replace anything; we add a controlled layer in front of it.
Is MCP secure?
The protocol supports it, but security isn't automatic. It requires proper implementation — scoped permissions, authentication, audit logging. Every MCP server we build includes these by default. We don't ship servers with open access.
Client stories