MCP servers connecting AI to your internal systems
Custom Model Context Protocol servers that expose your internal systems to AI assistants as scoped, permissioned tools, with auth and audit logging.
The Model Context Protocol is an open standard for exposing tools, data, and prompts to AI assistants through a server the assistant connects to. A custom MCP server lets Claude, Claude Code, and other MCP-compatible clients query your internal systems through one permissioned interface instead of a bespoke integration per tool. It is for teams who want their AI tools to reach real company data without pasting it into a chat window.
The problem
Assistants are useful in proportion to what they can see. Most cannot see anything internal, so people paste data in by hand, which is slow and puts company information somewhere nobody is tracking.
- People copy records out of internal systems into a chat window to ask questions about them.
- Every assistant needs its own integration, so the work is repeated per tool.
- There is no record of what was accessed, by whom, or when.
- Access is all or nothing, when what you want is read-only on three specific things.
- Engineers using Claude Code cannot reach the internal API docs, ticket system, or database schema.
What we build
A scoped MCP server
Tools and resources exposing exactly the operations you intend, built against the protocol so any MCP-compatible client can use it.
Authentication and permissions
Identity carried through to the underlying system, so a user reaches only what their own account allows rather than what a shared service account allows.
Read and write boundaries
Read-only by default. Write operations added deliberately, individually, with their own approval requirements.
Audit logging
Every call recorded with caller, arguments, and result size, so access to internal data is reviewable.
Tool descriptions that work
Clear names, descriptions, and schemas, because a tool the model cannot understand is a tool it calls wrongly or ignores.
Deployment and distribution
Local stdio for developer tooling or remote HTTP for shared internal use, with a documented setup path for the team.
What you get at handoff
- Server code in your repository with tests for each tool.
- Setup instructions for connecting the clients your team uses.
- Permission model documented, including what is deliberately not exposed.
- Audit log destination configured in your own systems.
- A guide for adding a tool without reopening the security questions.
Stack
Protocol
Model Context Protocol, stdio transport, Streamable HTTP transport
Language
Python, TypeScript
Clients
Claude Desktop, Claude Code, Other MCP-compatible clients
Backends
Postgres, Snowflake, Internal REST APIs, Jira, Document stores
Security
OAuth, Scoped tokens, Per-user identity, Audit logging
Related services
FAQ
What is MCP, in plain terms?
An open standard for connecting AI assistants to external systems. You run a server that advertises a set of tools and resources; a compatible assistant connects and can call them. The point is that you build the integration once rather than once per assistant.
Is this safe to point at internal data?
It is as safe as the permission model you build, which is why that is most of the work. Read-only by default, per-user identity rather than a shared service account, only the operations you intend exposed, and every call logged. An MCP server built without those is a liability.
Do we need MCP, or just an API?
If a program is calling your system, an ordinary API is fine. MCP earns its place when an AI assistant is the caller, because it standardises how tools are described and discovered. If nobody on your team uses an MCP-compatible client, you do not need one yet.
Can it write to systems, not just read?
Yes, and it should be approached carefully. Write tools get added individually, with narrow scope and usually an approval step. Starting read-only and adding writes once the access patterns are understood is the sensible order.
What does this do for our engineers?
It puts internal context inside the tools they already use. An engineer in Claude Code can query the real schema, look up a ticket, or read internal API docs without leaving the editor or pasting anything anywhere.
Ready to scope MCP Servers?
Send the workflow, tool stack, or reporting problem. We will tell you what should be automated, what should stay manual, and what is worth building first.