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MCP server development: connect AI to your systems safely.

Model Context Protocol is the standard way to give AI assistants access to tools and data. We build MCP servers that are secure enough for production, not just for a demo.

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Sprint
2–3 weeks per system
Ideal for
Teams using Claude, Copilot or custom agents
Connects to
Salesforce, SQL Server, Fabric, SharePoint, Jira, custom APIs
Security
OAuth 2.1, least-privilege scopes, audit logs

The challenge

The hard part of MCP is governance

Writing a basic MCP server takes an afternoon. Deciding which user can call which tool, keeping writes safe, limiting how much data reaches the model and logging every call for audit takes engineering.

We design each server around your identity provider and data rules, then test it with the AI clients your teams already use.

Scope

What each MCP sprint delivers

  • Tool designA small set of clear, typed tools that map to real tasks, instead of exposing raw database access.
  • Identity and scopesSign-in through Entra ID, Okta or Google, with the caller's own permissions applied to every request.
  • Safe writesRead-only by default. Write tools require confirmation or an approval step you control.
  • Context limitsPagination, field filtering and summarisation so responses stay small and costs stay low.
  • Audit and monitoringEvery tool call logged with user, inputs and result size, ready for your SIEM.

How it runs

Clear stages. You decide at the end of each.

Pick the system

Agree the tasks people want the AI to do and the data it may touch.

2–3 days

Build

Server, tools, auth and tests.

1–2 weeks

Pilot

Real users try it from Claude or your agent, and we tune the tools.

1 week

Hand over

Deployment, runbook and monitoring.

2–3 days

Questions

What teams ask us about this.

Which AI clients can use the servers you build?

Any MCP-compatible client, including Claude Desktop, Claude Code, Claude's web connectors and custom agents built on the Claude Agent SDK or other frameworks.

Can MCP servers run inside our network?

Yes. We deploy them as containers in your cloud or data centre, behind your existing gateway.

Will this expose sensitive data to the model?

Only what the tools return. We filter fields, mask identifiers and keep responses to what the task needs.

Related

ServicesClaude AI developmentClaude API apps, Claude Agent SDK agents, MCP integrations and Claude Code rollout for engineering teams. Built with prompt caching, evals and audit logs.ServicesAI agent developmentWe design and ship production AI agents and copilots that use your systems safely: tool calling, RAG, audit logs and human approval. Pilots in 4–6 weeks.ServicesSalesforce AgentforceAgentforce and Einstein setup, custom actions in Apex and Flow, and grounding on your CRM data with role-based access. AI assistants for sales and service.

Start a project

Tell us what you want AI to do. We'll tell you if it can.

A 30-minute call with an engineer. You leave with a straight answer on feasibility, rough cost and the first step.

contact@nubitsaitech.com
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