MCP server development. Your systems, usable by AI clients on your terms.
We build Model Context Protocol (MCP) servers that give AI clients controlled access to your product, data or internal tools, with authentication, permissions and logging.
For software companies that want their product usable from AI assistants, and for teams that want their own agents to work with internal systems.
MCP server development
We define which tools and data an AI client may use, build the MCP server around those boundaries and test it with real clients. Access is read-only by default; anything that changes data is explicit, permissioned and logged.
- (01)Tools and resources scoped to real tasks
- (02)Authentication and per-user permissions
- (03)Audit logs for every call
- (04)Tested with real AI clients
Tools we use for this.
- Model Context Protocol
- TypeScript
- Python
- OAuth 2.1
- Node.js
- Docker
We choose the stack that fits your requirements. Tool names describe our skills, not partnerships.
Sprint.
4 weeks.
Four weeks on one goal, with working software at the end.
Included
- (01)Tool and permission design
- (02)MCP server implementation
- (03)Authentication and rate limits
- (04)Testing with real AI clients
- (05)Deployment to your infrastructure
- (06)Documentation and handover
Not included
- (01)Changes to your core product beyond the integration points
- (02)Third-party API or hosting costs
- (03)Listings in third-party directories
How it runs
- (01) · Week 1
Design
Agree tools, data, permissions and the actions that need approval.
- (02) · Weeks 2–3
Build
Implement, secure and test the server against real AI clients.
- (03) · Week 4
Ship
Deploy, document and hand over.
Also available as: Workshop (½–2 days) · Build & Support (Staged, ongoing). Compare the formats.
You might also
look at these.
What is MCP?
The Model Context Protocol is an open standard that lets AI applications connect to external tools and data in a consistent way. One MCP server can serve several AI clients.
Is it safe to let an AI client into our systems?
Only with clear boundaries. We start read-only, scope every tool to a specific task, enforce per-user permissions and log every call. Actions that change data can require human approval.
Can you build one for our customers to use?
Yes. A public MCP server lets your customers’ AI assistants use your product. We design it so each customer reaches only their own data.