MCP server development and custom AI integrations.

It is a small service that describes a set of tools and resources in a standard way, so any compatible AI application can use them without a bespoke integration. In practice it is ordinary backend engineering: authenticate, validate the input, call the underlying system, shape the response, log the call. The protocol is the straightforward part; the access model is the work.

The AI automation, RAG and MCP development page covers the assistant itself: knowledge sources, retrieval, model behaviour and the user-facing workflow. This page covers the layer underneath it — the servers and connectors that give agents governed access to live systems. Many projects need both and they are often scoped together. If you are not sure which one you are asking for, describe the workflow and we will say.

It can, with deliberate design. Write tools are kept separate from read tools, restricted to specific objects and operations, made idempotent where possible, and in most cases placed behind a human approval step that happens outside the model’s control. We would start read-only, prove tool selection is reliable, then add write capability one operation at a time.

Any MCP-compatible client, which now covers the major assistant and IDE products, plus platform-specific connectors and plugins where an organisation has standardised on one vendor. We test against the clients you actually use, because a tool description that works well in one client can be read differently in another.

By not relying on the model to enforce anything. Permissions are checked server-side before a tool runs, tools expose only approved objects, generated SQL is validated against a whitelist, destructive operations are either not exposed or require human approval, and retrieved content is treated as data rather than as instructions. Logging every call means an unexpected request is visible afterwards, not merely prevented in theory.

We quote after a scoping call, and terms are agreed per engagement. The recommended starting point is small: one server, one system, one small group of users and the three or four tools that cover most of their requests. MV.tech is a remote-first team based in Ahmedabad, India, with a lean model that avoids large-consultancy overhead.

Instead of handing the assistant the keys, it calls an MCP server that runs under a scoped service identity against an approved set of tables, read-only by default and whitelisted rather than blocklisted. Where the agent generates SQL, the query is parsed and checked before it runs, with permitted objects only, row and cost limits and no writes, and results are validated before they are returned. Every call is logged with the caller, tool, time and outcome, and we test against real requests, including ones the server should refuse, before access is widened.