Model Context Protocol development

Model Context Protocol (MCP) Development

Connect AI assistants and agents to your systems through one secure, open standard.

The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data. We design and build secure MCP servers that let assistants such as Claude, ChatGPT, GitHub Copilot and Copilot Studio work with your APIs, databases and documents.

What we cover

  • MCP server design
  • OAuth authentication and scopes
  • API and database tools
  • Prompt-injection and abuse controls
  • Logging and audit
  • Hosting and deployment

Requirement-led advice · Documented handover

  • Advice based on your requirements
  • Works with your existing stack
  • Documented, maintainable delivery

What Is the Model Context Protocol?

MCP is an open protocol, first released by Anthropic in November 2024, that standardises how AI applications connect to external systems. An MCP server exposes tools (actions the model can call), resources (data it can read) and prompts (reusable templates). An MCP client, built into an AI application, discovers those capabilities and uses them on the user's behalf. Servers can run locally over standard input and output, or remotely over HTTP.

Because many AI applications now support MCP, including Claude, ChatGPT, GitHub Copilot, Microsoft Copilot Studio, Cursor and Visual Studio Code, one well-built server can serve all of them. Remote servers use OAuth-based authorization, so each user acts with their own permissions. The same openness creates risks: a server gives a model real power over your systems, so scopes, validation and audit matter.

When MCP is a good fit

  • You want several AI tools to use the same internal systems.
  • Agents need to take actions, not only answer questions.
  • You want a standard interface instead of a custom plug-in for each AI product.
  • Developers want AI coding tools to reach internal docs, tickets or databases.

What Teams Build With MCP

Typical MCP servers.

  • Database access

    Read-only, governed queries over business data.

  • Internal API tools

    Expose approved operations from your systems.

  • Knowledge access

    Search documents, wikis and policies.

  • Ticketing and project tools

    Create and update issues and tasks.

  • Developer tooling

    Give coding agents access to builds, logs and docs.

  • CRM and ERP actions

    Look up customers and create records.

  • Reporting tools

    Run defined reports and return results.

  • Gateways and policy

    Central control of which tools each client may use.

MCP Services We Deliver

Each piece of work has a clear output, so you know what you will receive.

  • Tool design

    Decide which tools and data to expose, and to whom.

    Deliverables: tool catalogue, access model

  • Server development

    MCP servers in TypeScript, Python or C# with tests.

    Deliverables: server code, tests, documentation

  • Authentication

    OAuth-based authorization, scopes and per-user access.

    Deliverables: auth design, configuration

  • Security review

    Input validation, rate limits and prompt-injection controls.

    Deliverables: threat model, security fixes

  • Deployment

    Hosting, secrets, monitoring and versioning.

    Deliverables: deployment, monitoring, runbook

  • Client rollout

    Configure servers in Claude, Copilot, ChatGPT and IDEs.

    Deliverables: client configuration, user guide

MCP Building Blocks

Concepts and tools we work with.

Protocol

  • Tools
  • Resources
  • Prompts
  • Local and remote transports

SDKs

  • TypeScript SDK
  • Python SDK
  • C# SDK
  • Other official SDKs

Security

  • OAuth-based authorization
  • Scoped tokens
  • Input validation
  • Audit logging

Clients

  • Claude and Claude Code
  • ChatGPT
  • GitHub Copilot and VS Code
  • Copilot Studio and Cursor

Listing a feature here does not mean every project uses it. We recommend what fits your requirements, skills, licensing and running costs.

How MCP Fits Your Stack

An MCP server is a thin, well-defined layer over systems you already have: REST or GraphQL APIs, SQL databases, SharePoint content and line-of-business applications. We usually host servers on Azure or AWS alongside the systems they wrap.

MCP works with coding agents such as Claude Code, GitHub Copilot and Cursor, with Copilot Studio agents, and with frameworks such as LangChain and LangGraph.

Implementation Considerations

  • Expose the smallest useful set of tools, with clear names and descriptions.
  • Start read-only; add write actions with confirmation and logging.
  • Treat content returned to the model as untrusted input that may contain instructions.
  • Use per-user authorization rather than one shared service account where possible.
  • Version tool definitions and test them with each client you support.
  • Log every tool call for audit and troubleshooting.

How We Work With Model Context Protocol (MCP)

The same four steps we use across our technology work, adapted to your scope.

  1. Discover

    Goals, users, constraints and the systems Model Context Protocol (MCP) must work with.

  2. Select

    Confirm Model Context Protocol (MCP) is the right fit and agree the architecture and scope.

  3. Build and integrate

    Deliver in short iterations with testing, reviews and documentation.

  4. Operate and improve

    Monitor, support and improve the solution as your needs change.

Model Context Protocol (MCP) Questions, Answered

What is the Model Context Protocol?

An open standard for connecting AI applications to tools and data through MCP servers.

Which AI tools support MCP?

Many, including Claude, ChatGPT, GitHub Copilot, Microsoft Copilot Studio, Cursor and Visual Studio Code. Support details vary by product.

Is MCP secure?

MCP supports OAuth-based authorization, but security depends on the server design. We limit scopes, validate input, guard against prompt injection and log every call.

How is MCP different from an API?

MCP sits on top of your APIs and describes them in a way AI applications can discover and use consistently.

Should servers run locally or remotely?

Local servers suit developer tools. Remote servers suit shared business systems and central control.

Which languages do you use?

Usually TypeScript, Python or C#, matching your existing systems.

Connecting AI to Your Systems?

Tell us which systems and AI tools you want to connect, and we will suggest a secure design.

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