Make your internal systems AI-accessible
We design and ship Model Context Protocol servers so Claude, GPT or your own agents can securely call your APIs, databases and tools — with an audit trail on every call.
MCP demos well in an afternoon. Production is the hard part.
The Model Context Protocol is an open standard for letting AI models call external tools and read external data. It is the adapter between a model and the systems you already run.
You build one MCP server instead of a bespoke integration per model. Any MCP-compatible client — Claude, an IDE, or an agent you wrote yourself — then reaches your APIs, databases and tools through the same contract.
Getting that server into production is where the work sits. Tool schemas, authentication, blast radius, latency and observability all have to hold up before a model is given write access to anything that matters.
Tool schemas have to make sense to a model, not just to a developer.
The protocol is young, so most published examples age within a release or two.
Security is the whole job — an MCP server is a path into internal systems.
Failures have to explain themselves to the caller, not just to your logs.
Agents make many calls per task, so per-call latency compounds.
Composable tools change how you design the internal APIs behind them.
Production MCP servers, end to end
Servers that give a model exactly the access you intended, and nothing past it.
API-to-MCP bridges
We wrap the REST, GraphQL or gRPC APIs you already run as MCP tools, with input validation and error messages a model can act on.
Custom MCP servers
Purpose-built servers for database queries, file operations, workflow automation, or any internal capability you want a model to reach.
Auth & authorisation
Per-tool access control using API keys, OAuth and role-based permissions that mirror the rules your application already enforces.
Security guardrails
Input validation, rate limits, audit logging and sandboxed execution, so an autonomous caller cannot reach past what you granted it.
Monitoring & debugging
Full visibility into how a model uses your tools. Debug any server with MCP Viewer (mcpviewer.ai), our free MCP inspector, and export tool-call metrics to the stack you already run.
Performance work
Caching, connection pooling and async execution, sized for agents that make hundreds of tool calls inside a single task.
Every engagement includes
- MCP server built, deployed and handed over in your repo.
- Bridges for the REST, GraphQL or gRPC APIs you already run.
- Tool schema design, reviewed against real agent transcripts.
- Authentication and per-tool access control.
- Rate limits, audit logging and sandboxing.
- Tool-call monitoring, plus debugging with MCP Viewer.
- Load testing against agent-shaped traffic.
- Documentation and a working session with your team.
Ready to make your APIs AI-accessible?
Book a free 30-minute call and we'll work out which of your systems is worth exposing first — and what it takes to do it safely.