A Model Context Protocol server is a small, secure service that exposes your internal tools and data to AI assistants in a standard way, so ChatGPT, Claude and your own agents can use them without a bespoke integration for each. EqualPixels builds and hosts MCP servers for teams that want AI to act inside their systems rather than only talk about them.
What is MCP, in plain terms
Model Context Protocol is an open standard for connecting AI assistants to external tools and data sources. Before it, every assistant needed its own custom connector for every system. With it, you build the connector once and any compatible assistant can use it.
Think of it as an API designed for models rather than for developers: it describes what each tool does, what arguments it takes, and what it returns, in a form an assistant can reason about.
What we build
- Custom MCP servers over your database, CRM, ERP, ticketing system, document store or internal APIs
- Read-only knowledge servers that let assistants search your documentation with permissions applied
- Action servers that let assistants create records, update statuses and trigger workflows, with approval steps on anything irreversible
- Authentication and access control, so each user sees only what their role allows, with a full audit trail
- Hosting and monitoring, in your cloud or ours, with logging of every tool call
- Client-side integration into Claude, ChatGPT, internal chat interfaces and your own agents
Why a business would want one
| Without MCP | With MCP |
|---|---|
| A separate integration per assistant | One server, every compatible client |
| Credentials copied into multiple tools | Central authentication and audit |
| People paste data into chat windows | The assistant queries the source directly |
| Answers go stale immediately | Answers reflect the system right now |
How we build them safely
- Least privilege by default. Read-only first. Write access is added deliberately, tool by tool.
- Scoped to the user, not the server. Permissions follow the person asking.
- Approval on irreversible actions. Anything that spends money, sends a message or deletes data gets a confirmation step.
- Every call logged. Who, what, when, with what arguments and what result.
- Rate limits and input validation. Treat model input as untrusted, because it is.
- Tested with the actual clients your team uses, not only against the specification.
Frequently asked questions
What is an MCP server and does my business need one?
It is a standard connector that lets AI assistants use your internal tools and data. You need one if your team is already pasting company information into AI chats, or if you want assistants and agents to take action in your systems rather than just describe what they would do.
Is it safe to give an AI assistant access to our systems?
It is safe when access is scoped per user, limited to specific tools, read-only where possible, approval-gated for anything irreversible, and fully logged. It is not safe when someone hands an assistant an admin API key. The architecture is what makes the difference.
How long does an MCP server take to build?
A focused read-only server over one system typically takes two to four weeks including authentication and testing. Adding write actions and approval flows extends that, mostly because of the security review rather than the code.
Which assistants can use it?
Any MCP-compatible client, which currently includes Claude, ChatGPT connectors, several development tools, and any custom agent you build. That interoperability is the main reason to use the standard rather than a bespoke integration.
Can it run inside our own network?
Yes. We deploy MCP servers in your own cloud or on-premises where data residency or regulation requires it, so your data never crosses a boundary you have not approved.