The UK public sector is on the brink of a technological revolution, with Artificial Intelligence promising to streamline services, increase efficiency, and improve citizen outcomes. From local councils to NHS trusts and central government departments, the potential of Large Language Models (LLMs) is immense. Yet, this potential is often limited by a significant challenge: how can AI systems securely access the data and tools they need to be truly useful? The answer may lie in a concept that is rapidly gaining traction: Model Context Protocol (MCP). This isn't just another piece of jargon; it's a foundational framework for connecting AI systems to real-world data and tools in a standardised, secure way.

What Exactly is Model Context Protocol?

Imagine you're working with a skilled assistant, but they're sitting in a locked room with no access to your filing cabinets, databases, or internal systems. No matter how capable they are, their usefulness is severely limited by what information you can manually provide to them. Model Context Protocol solves this fundamental problem. It's an open standard that acts like a universal connector—think of it as a USB-C port for AI applications. MCP allows AI assistants to securely connect to external systems, data sources, and tools without requiring bespoke integrations for each connection. Rather than manually copying and pasting information into an AI interface, MCP enables AI systems to directly:

• Access data sources such as local files, databases, document repositories, and knowledge bases
• Use tools like search engines, calculators, or APIs
• Execute actions in connected systems, such as updating records or creating tasks

An MCP implementation consists of three key components:

• MCP Servers: These provide specific capabilities—for example, a GitHub server that allows the AI to read repositories, or a Google Drive server that provides access to documents
• MCP Clients: These are the AI applications (like Claude, VS Code, or ChatGPT) that connect to MCP servers
• Standardised Protocol: A universal communication method that allows any MCP client to work with any MCP server, avoiding the need for custom integrations

By establishing these standardised connections, AI systems can work with live, relevant data whilst maintaining security boundaries, dramatically increasing their usefulness for real-world tasks.

Security: The Cornerstone of Public Sector AI

For any public body, data security and privacy are non-negotiable. The handling of citizen data is governed by strict legislation like the UK GDPR and the Data Protection Act 2018. This is where MCP's architectural approach proves its worth. 1. Controlled Access: MCP servers can be configured with granular permissions, ensuring AI systems only access the specific data and tools they're authorised to use. For instance, an MCP server might provide read-only access to certain databases whilst completely restricting access to sensitive personnel records. 2. Secure, Local Processing: Unlike cloud-based integrations that might send data to third parties, MCP can operate entirely on-premises. An AI assistant running locally can connect to internal MCP servers without data leaving the organisation's infrastructure. 3. Audit and Compliance: Because MCP provides a standardised integration layer, all interactions between AI systems and data sources can be logged and monitored. This creates a clear audit trail showing exactly what data was accessed, when, and for what purpose—essential for regulatory compliance. 4. Separation of Concerns: MCP separates the AI application from the data access layer. This means security policies can be enforced at the server level, independent of which AI system is connecting. If policies change, you update the server configuration once rather than reconfiguring multiple AI tools.

MCP in Action: Use Cases for the UK Public Sector

Let's move from theory to practice. How could MCP be applied across UK public services?

Use Case 1: Local Council Planning Department

The Challenge: A planning officer needs to review a new development application, which requires checking the proposal against local plan policies, historical planning decisions for the area, environmental impact assessments, and public consultation responses—all stored in different systems. The AI Application: An LLM assistant helps the officer by automatically gathering relevant information from multiple sources. The MCP Solution:

• Planning Database Server: Provides access to historical applications and decisions for the area
• Document Repository Server: Connects to the council's document management system containing local plan policies and environmental reports
• Public Consultation Server: Accesses the system storing public feedback and objections

The officer simply asks: "What are the key planning considerations for the proposed development at 123 High Street?" The AI, connected via MCP, retrieves relevant policies from the local plan, identifies similar past applications, summarises environmental concerns, and collates public objections—all whilst the data remains securely within council systems.

Use Case 2: NHS Patient Pathway Coordination

The Challenge: An NHS trust wants to help care coordinators understand a patient's complete journey across different departments and systems to ensure continuity of care. The AI Application: An AI assistant provides care coordinators with a unified view of patient information from multiple sources. The MCP Solution:

• Electronic Patient Record Server: Provides controlled access to clinical notes (with appropriate access controls)
• Appointment System Server: Connects to scheduling databases
• Care Plan Repository Server: Accesses treatment plans and discharge summaries

The care coordinator asks: "What appointments and follow-ups are scheduled for patient NHS123456, and what were the outcomes of their last three consultations?" The AI uses MCP to query the relevant systems, presenting a coherent timeline whilst respecting clinical information governance rules configured at the server level.

Use Case 3: Central Government Policy Analysis

The Challenge: A policy adviser at DWP needs to analyse how a proposed benefit change might affect different demographic groups, requiring access to anonymised case data, economic models, and previous policy evaluations. The AI Application: An LLM helps synthesise insights from multiple data sources to inform policy decisions. The MCP Solution:

• Anonymised Case Database Server: Provides access to de-identified benefit claim data
• Economic Model Server: Connects to departmental forecasting tools
• Policy Archive Server: Accesses historical policy papers and evaluation reports

The adviser asks: "What was the impact on single-parent households when we last adjusted the taper rate, and how might a 5% change affect current claimants?" The AI uses MCP to query historical data, run economic scenarios, and retrieve past evaluation findings—all from authorised government systems with appropriate data protections.

Implementation Considerations for the Public Sector

Deploying MCP in a public sector context requires careful planning: Data Governance: Each MCP server must be configured with strict access controls that align with your organisation's data classification and information governance policies. Consider what data each AI system truly needs to access. On-Premises Deployment: For highly sensitive environments, MCP can be deployed entirely on-premises, with AI applications and MCP servers running within your secure infrastructure. Standardisation Benefits: Because MCP is an open standard, you're not locked into a single vendor. Multiple AI applications can connect to the same MCP servers, and you can switch AI providers without rebuilding integrations. Incremental Adoption: Start with low-risk, high-value use cases. For example, connect an AI assistant to a public document repository before moving to more sensitive systems.

The Path Forward

Implementing Artificial Intelligence in the public sector is not just about choosing the right model; it's about building the infrastructure to connect that model to real-world data and systems safely. Model Context Protocol provides the standardised, secure connectivity layer needed to move from isolated AI experiments to integrated, enterprise-ready solutions. By enabling AI systems to work with live organisational data through controlled, auditable connections, MCP allows public bodies to innovate with confidence, ensuring that this powerful technology is harnessed for the public good, safely and responsibly.