MCP server development
Custom MCP server connecting AI assistants safely to internal systems and third-party APIs — narrow, validated tools instead of raw data access.
What we build
Safe plumbing between AI assistants — Claude, ChatGPT, or your own — and your business systems: CRM, ticketing, databases, internal APIs. Instead of raw data access, the assistant gets a small set of validated, permission-controlled actions we define together, using the Model Context Protocol. Choose this when your team already uses AI and needs it to do things in your systems without being trusted with the keys.
What you get
- The MCP server's source code in a Git repository
- A tool catalogue listing each tool, its input schema and its required permission
- An access-control test proving each tool refuses callers without its permission
- A transport smoke test that passes against the running server
How the work unfolds
- Design the tool surface around real workflows
- Build the MCP server
- Enforce identity and access on every tool
- Write tool descriptions that guide the model
- Smoke-test over real transports
What shapes the price
Before you see a number, our scoping conversation asks:
- How many of your systems should the assistant be able to reach through this server? Each wrapped system is its own tool set, credentials and edge cases.
How an engagement starts
This work builds on Discovery workshop, so scope, boundaries and constraints are agreed before anything is built.
Teams often combine it with: