The Model Context Protocol (MCP) is the open standard that lets AI assistants and agents connect to your systems — files, databases, APIs, and internal tools — through one consistent, controllable interface. Software Depo designs and builds MCP servers that act as secure gateways between AI and your infrastructure.
MCP Server Development
Custom MCP servers exposing exactly the capabilities you choose: read a record, query a database, trigger a process, file a ticket — nothing more. We build against the current protocol specification with typed tools, resource endpoints, and clean error behavior.
Connectors and Integrations
MCP connectors for CRMs, ERPs, document stores, ticketing, and proprietary line-of-business systems — so Claude, Codex, Copilot, and custom agents can work with the systems you already run, under the permissions you define.
Security Comes First
Every MCP server is scoped: least-privilege access, authenticated transport, input validation, audit logging, and approval hooks for consequential actions. We treat an MCP server as a security boundary and design it like one. For independent security review of agent infrastructure, our sister practice BulletproofSoft covers AI and MCP security assessment.
Where MCP Fits
MCP servers power Claude and Claude Code integrations, Codex repository agents, enterprise copilots, and multi-agent systems. If you are standardizing how AI reaches internal data, MCP is the layer to invest in — build it once, use it across assistants.
Describe the systems your AI should reach — we will propose the server design, security model, and rollout plan.
Related services
- AI Agent Integration Services — MCP servers exist to be integrated: AI agent integration.
- Private and On-Premise AI Deployment — Running it inside your own boundary: private and on-premise AI deployment.
What Is the Model Context Protocol?
MCP is an open protocol that standardises how AI applications connect to external tools and data. Instead of every assistant needing a bespoke integration with every system, a system exposes an MCP server once and any MCP-capable client can use it. It is the same argument that produced ODBC for databases or LSP for editors: define the interface once, and the N×M integration problem becomes N+M.
For an organization, the practical consequence is that an integration you build for one assistant is not stranded when you adopt another.
What Does an MCP Server Actually Expose?
- Tools — actions the model can invoke: query a database, create a ticket, run a report, call an internal API.
- Resources — data the model can read: documents, records, files, query results.
- Prompts — reusable templates that encode how a particular task should be approached.
The design work is deciding what belongs in each category and how granular the tools should be. Tools that are too coarse are hard for a model to use correctly; tools that are too fine force long chains of calls for a simple task. This is the part that most affects whether the result is reliable, and it is a judgement call rather than a technical one.
Should You Build an MCP Server or Use an Existing One?
Check for an existing server before commissioning one. Many common systems already have community or vendor MCP servers, and adopting a maintained one beats writing your own. Build when the system is internal, when an existing server does not expose what you need, or when you need permission and audit behaviour that a general-purpose server does not provide.
The last point is the usual reason organizations end up building. Public servers tend to assume a single trusted user; enterprise deployments need per-user scoping and an audit trail.
How Do You Secure an MCP Server?
An MCP server is a piece of infrastructure that lets a language model take actions in your systems. It deserves the same scrutiny as any other API with write access:
- Identity. Prefer acting as the requesting user, inheriting their existing permissions, over a shared service account whose broad access every user effectively inherits.
- Least privilege per tool. Scope each tool to what it needs. A read tool should not hold write credentials.
- Human approval on consequential actions. Anything that spends money, contacts a customer or deletes data should route through a person until logs justify otherwise.
- Input treated as untrusted. Content returned from a tool can carry instructions aimed at the model. Prompt injection is the defining risk of this architecture.
- Full audit logging. Which tool, which parameters, which user, what result. You will be asked.
- Idempotency. A retry must not create a second ticket or a second order.
We build these controls in from the start rather than retrofitting them. For independent review, our sister practice covers AI and MCP security.
What Does an MCP Project Involve?
Typically: agreeing which systems and which operations are in scope; designing the tool surface; implementing the server against your APIs with authentication and scoping; adding logging and approval steps; evaluating behaviour against realistic tasks before rollout; and packaging it so it can be deployed, versioned and updated like any other service.
Most of the risk is in the tool design and the permission model, not in the protocol implementation. A first project scoped to one system, read-only, with one clearly valuable workflow will teach you more than a broad rollout.
Related: AI agent integration · private and on-premise AI · AI agent development.