MCP Development

One MCP server. Every AI client.

Before MCP, every AI assistant needed its own glue code for every system it touched. Now one well-built server exposes your tools, data and documents to any MCP-compatible client — Claude, ChatGPT, Cursor, your own agents — through a single protocol. We build that server properly.

Talk to us All AI development

mcp-server 3 tools · 3 clients

Model Context Protocol

erp.get_invoice Claude

crm.update_lead Agent

docs.search ChatGPT

One server · every AI client · your systems

In one paragraph

AGI Software Solutions builds custom MCP (Model Context Protocol) servers that connect AI assistants and agents to your business systems — ERP, CRM, databases, document stores and internal APIs. One server replaces the per-assistant integration code, with authentication, per-user permission boundaries, approval gates on writes and audit logging designed in from the start. Most business MCP servers reach production in 3–8 weeks.

What We Build

The integration layer AI agents were missing.

An MCP server is infrastructure: it decides what an AI is allowed to see and do in your systems. That makes the security model, not the protocol plumbing, the real engineering work — and the reason to build it deliberately rather than bolt it on.

Standard

Build once, connect everything

One server exposes your systems to Claude, ChatGPT, IDE agents and your own automations — and to the clients that have not shipped yet.

Security

Permissions at the server

Scoped tool access per user and per client, enforced where the data lives rather than promised in a prompt.

Control

Approval gates on writes

Reads flow freely; consequential writes can require a human sign-off. The server decides, not the model.

Audit

Every call on the record

Full logging of who asked for what, which tool ran and what changed — the evidence trail AI access to business systems needs.

Plain English

What is MCP (Model Context Protocol)?

MCP is an open standard, published by Anthropic in late 2024 and now adopted across the industry, that lets AI applications discover and call tools, read resources and use prompts from external servers through one protocol. Instead of building a separate integration for each AI product, you build one MCP server for your systems and every compatible client can use it.

For a business, an MCP server is the difference between "the AI can tell you about your data" and "the AI can safely look up this customer, draft this invoice, and file this document — within the permissions you set".

Compared

MCP server vs per-app API integrations

Why one protocol layer beats a pile of one-off connectors.

	Per-app integrations	One MCP server
Build effort	One integration per AI tool per system	One server, every MCP client
New AI client arrives	Another integration project	Works on day one
Permissions	Re-implemented in every connector	Enforced once, at the server
Audit	Scattered across tools	Every call logged in one place
Maintenance	Grows with each connector	One codebase to keep current

How We Build It

Five stages from problem to production.

The same process across every AI Development project, scaled to the size of the problem.

Discovery

We work out what the system actually has to do, what data exists, and what happens today when it goes wrong.

Design

Architecture, model choice, integration points and failure handling, defined before any of it gets built.

Integration

Connecting to the systems that hold your data, with security and permission boundaries handled properly.

Automation

The system takes on real work, in the workflows your team already uses rather than beside them.

Refine

Tuned against real usage and measured with evals, because how people use a system is never quite how it was designed.

Use Cases

Where mcp development pays off.

The patterns we see deliver, across startups, SMEs and enterprise teams.

ERP

AI access to your ERP

Invoices looked up, records created as drafts, stock queried in plain language — through tools scoped to exactly those operations.

Support

Assistant with real context

Support AI that can read the customer's actual orders, tickets and account state instead of guessing from a knowledge base.

Knowledge

Documents as resources

Policies, contracts and internal docs exposed as MCP resources, so every assistant answers from the same governed source.

Agents

Tools for your own agents

The same server that serves Claude serves the agents we build for you — one permission model across all of it.

Related Work

Systems we have shipped.

AI DevelopmentAI AutomationLogistics

Warehouse Intelligence

Demand forecasting, OCR-driven receiving and anomaly detection across 14 fulfilment centres.

47% fewer stockouts, 14 warehouses

AI DevelopmentAI Automation

Support Hub Inbox

One inbox across web chat, email, WhatsApp and Facebook, with AI replies trained on past resolutions.

60% faster replies, 99.95% uptime

AI DevelopmentMarketplace

AI Marketplace Platform

Support chatbot, live SEO analyser and a vector recommendation engine, all sharing one retrieval layer.

+10% GMV, 40% fewer support tickets

See all case studies

What Teams Say

Hear from the teams we work with.

“We needed a voice agent that could actually qualify leads, not a chatbot pretending to be one. The team shipped a sub-700ms pipeline in 6 weeks. It now handles 5k calls a day.”

Priya RajHead of Growth, Ninjatech

“What sold us was their willingness to put AI engineers and product designers on the same call. We got working prototypes by week two and a production rollout in three months.”

James ThorntonCTO, Allindex

“AGI designed a CRM system tailored to our client management process. It is intuitive, reliable, and has centralized all our communication and history in one dashboard. This has greatly improved client retention.”

Carlos MendesProduct Manager, Qilinlab

Common Questions

Before you get in touch.

What is an MCP server?

A service that exposes your systems to AI applications through the Model Context Protocol: tools the AI can call, resources it can read, and prompts it can use. Any MCP-compatible client — Claude, ChatGPT, Cursor, custom agents — can connect to it.

Do we need MCP, or a normal API integration?

If exactly one application will ever use the integration, a direct API is fine. If AI assistants or agents are part of the picture — and in 2026 they usually are — MCP means you build the integration once instead of once per AI product.

Is it safe to point an MCP server at production systems?

It is when the server is built for it: scoped tools rather than raw database access, per-user permissions, approval gates on writes, rate limits and full audit logging. That security design is most of our work.

How long does MCP server development take?

A read-only server over one or two systems: 3–4 weeks. Servers with write operations, approval workflows and several systems: 6–8 weeks. The variable is almost always the systems being connected, not the protocol.

Which AI clients support MCP?

Claude (desktop, web and API), ChatGPT, Cursor, Windsurf and most agent frameworks, with the list growing steadily. Building to the standard is precisely what keeps you off the integration treadmill.

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