What you get
- A production-ready MCP server in TypeScript or Python, built on the official SDKs
- A curated set of tools named and described for how your team talks, not how the API is built
- Read access by default and write access only where you decide, with confirmation for anything risky
- Local (stdio) and remote (Streamable HTTP with OAuth) transports, depending on how your team works
- Automated tests, a setup guide for each AI client you use, and the full source in your repository
How a project runs
- Free written scoping. You fill in a short brief: the system, and the questions people ask it. We tell you whether an off-the-shelf server is enough.
- Tool design, 1 to 2 days. We write the list of tools with inputs, outputs and permission level. You approve it before we write code.
- Build and test, 5 to 15 days. We build against a sandbox or staging copy and test with real questions from your team.
- Deploy and hand over. We deploy to your environment or ours, connect your AI clients and record a walkthrough video for your team.
When a custom server makes sense
Off-the-shelf MCP servers are a good start. Teams come to us when the generic server cannot see their custom fields, exposes far more than it should, has no audit trail, or when one question needs data from several systems at once.
Security is part of the build
Every connector ships with scoped credentials, input validation, rate limiting, server-side limits on query size, and a log of every tool call. Destructive actions are not exposed at all unless you ask for them, and then only behind confirmation. See our MCP security checklist.