Build secure Model Context Protocol (MCP) servers that expose your data and tools to AI applications - standardised integrations, governed access, and supportable operations.
Model Context Protocol (MCP) is an open protocol that standardises how AI applications connect to external data sources and tools - often described as a “USB?C port” for AI applications. By using MCP, an AI client can request context and invoke tool actions through a consistent interface, rather than relying on bespoke, vendor?specific connectors.
LW IT Solutions designs and builds MCP servers that connect your AI experiences to the systems that matter: document repositories, ticketing platforms, knowledge bases, databases, and internal APIs. We treat MCP integration as a governed platform capability - implementing authentication, authorisation, logging, and operational runbooks - so you can unlock real automation and context retrieval without compromising security or maintainability. This service is suitable whether you are integrating with OpenAI tools (including Codex MCP support) or other MCP-capable clients.
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Service Overview
Highlights
- Standards-based MCP implementation compatible with multiple AI clients
- Clear tool contracts with documented inputs, outputs, and limits
- Least-privilege access model with approval and audit support
- Operational telemetry for usage, errors, and investigation
- Deployment approach aligned to your hosting and change model
Business Benefits
- Expose internal data and actions to AI clients through a single, standardised interface
- Reduce integration sprawl by consolidating connectors behind governed MCP servers
- Control AI tool access with clear authentication, authorisation, and audit trails
- Increase confidence in AI automation through validated tool behaviour and logging
- Create a repeatable pattern for onboarding new tools without re-engineering clients
Typical use cases
- Connecting AI assistants to internal knowledge bases and document stores
- Allowing agents to create or update tickets in service management systems
- Providing controlled read or write access to line-of-business APIs
- Replacing bespoke AI connectors with a single MCP-based integration layer
- Preparing an organisation for wider agent adoption with governed tooling
Objectives & deliverables
What Success Looks Like
- Expose approved data sources to AI applications through a standard, reusable integration layer
- Enable tool actions (create/update/search) with controlled permissions and auditability
- Reduce connector sprawl by standardising integrations behind MCP servers
- Implement security guardrails for AI tool access (least privilege, approvals, logging, and evidence)
- Create a repeatable pattern for onboarding new tools and data sources safely
What You Get
- MCP server build and deployment package for the agreed scope
- Tool catalogue with documented schemas and usage guidance
- Security model: authentication/authorisation approach, least privilege mapping, and approvals model where required
- Operational pack: monitoring, alerting, runbooks, and support ownership model
- Backlog: additional tools/sources to onboard and recommended hardening items
How It Works
- Discovery - confirm client(s), tool/data scope, and risk constraints; agree success measures.
- Design - define MCP server architecture, tool contracts, security model, and operational ownership.
- Build - implement tools/connectors with validation, logging, and defensive execution.
- Test - validate tool behaviours, permission boundaries, negative paths, and rate/abuse controls.
- Operationalise - monitoring, runbooks, and change governance for new tools and updates.
- Handover - documentation, enablement session, and prioritised enhancement backlog.
Engagement Options
- Single MCP Server - build one MCP server with a small set of approved tools
- Tool Expansion - add new tools or data sources to an existing MCP server
- Platform Foundation - design and deploy a reusable MCP integration platform
- Operational Hardening - review and improve security, logging, and reliability
Common Bundles
Customers who use this service often bundle with these services
OpenAI Agents (AgentKit) & Agents SDK Builds
Build production-grade OpenAI agent workflows using AgentKit and the Agents SDK, with tool integration, tracing, evaluation, and controlled operations.
AI Safety, Governance & Risk
Implement practical AI safety and governance with policies, approvals, logging, data boundaries, and controls that reduce operational and compliance risk.
RAG / Chat with Your Data
Build governed RAG chat with your data solutions using secure retrieval, permissions-aware context, and measurable answer quality controls.
API & System Integrations
Design and implement API integrations connecting business systems with secure authentication, retries, logging, and supportable middleware patterns operations.
Frequently Asked Questions
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