MCP-Based AI Integrations
Model Context Protocol servers that give AI assistants safe, typed, auditable access to your internal systems.
Every AI tool integration used to be bespoke. A custom function wrapper for one assistant, rewritten for the next, with no shared conventions for authentication, errors or permissions. The Model Context Protocol standardises that: you expose your systems once as an MCP server, and any compatible AI client can use them.
Who this is for
Companies who want AI assistants working against their real internal data rather than a copied export. Teams already maintaining several one-off tool integrations. Organisations that need AI access to internal systems with proper authentication and an audit trail rather than a shared API key in a prompt.
Problems we solve
- Rewritten integrations. The same database tool implemented separately for every assistant.
- Over-broad access. An AI client handed credentials far wider than its task requires.
- No audit trail. No record of which assistant read which record, on whose behalf.
- Tools the model misuses. Weak descriptions and untyped parameters producing malformed calls.
- Errors that mislead. Failures returned as empty results, so the model reports success.
What we build
- MCP servers exposing your databases, APIs and internal services as typed tools
- Scoped, per-client authentication with least-privilege permissions
- Resource endpoints for read access to documents and records
- Structured, honest error responses so the model can recover rather than guess
- Full audit logging of every tool invocation and its result
- Rate limiting and quotas per client
How we work
Tool design is the whole job. We write descriptions and parameter schemas for a model as the reader, keep each tool narrow and single-purpose, and return errors that state what went wrong and what to try instead. Read and write operations are separated so read-only access can be granted freely while writes stay tightly scoped. Every server ships with an audit log from the start, because retrofitting one is painful.
Technologies we use
The Model Context Protocol specification, implemented in TypeScript or Python, over both stdio and HTTP transports. Compatible with Claude, Claude Code and other MCP clients, and deployed alongside your existing services with OAuth or token-based authentication.
Business benefits
- One integration serves every current and future AI client
- Assistants work from live data instead of stale exports
- Least-privilege scoping keeps AI access proportionate to the task
- Complete audit trail of what was accessed and by whom
Where it pays off
- Assistants querying production data safely, under read-only scope
- Internal tools exposed to AI without opening the whole API surface
- Engineering assistants with controlled access to repositories and issue trackers
- Support assistants reading live customer and order records
- Standardising several existing ad-hoc tool integrations onto one protocol
Common questions
Is MCP worth adopting yet?
If you are building more than one AI integration, yes — it saves rewriting the same tool layer. For a single narrow integration a direct implementation may still be simpler, and we will say so.
Can we limit what an assistant may do?
Yes, and you should. Permissions are scoped per client and per tool, so an assistant can be given read access to orders without any ability to modify them.
Does this expose our database to a model vendor?
No. The MCP server runs on your infrastructure. The client requests a specific tool call and receives only that result — the model never gets database credentials or open query access.
Want AI working against your real systems safely? An MCP server is usually the right foundation.