Service · Remote-first senior specialists
Give an AI agent access to real systems without handing over the keys. MV.tech builds custom Model Context Protocol (MCP) servers and AI connectors in Python that expose approved databases, APIs and internal tools to enterprise AI platforms, with scoped authorisation, validated queries, logged calls and evaluation before access is widened.
The starting point
What we deliver
Model Context Protocol servers built around your systems: tool definitions with typed inputs, explicit failure modes, resources, pagination and sensible limits. Tools are designed around tasks a person would recognise rather than mirroring every API endpoint, because an agent chooses far better from a short, well-named list.
Connectors, plugins and extensions for the AI platforms your organisation has already adopted — including Claude, Gemini Enterprise and Cortex AI environments — so approved data reaches assistants through one maintained integration instead of several ad-hoc ones.
Connect agents to warehouses, operational databases, REST APIs, ticketing and finance systems. Access runs through a scoped service identity against an approved set of tables or endpoints: read-only by default, whitelists rather than blocklists, and no path to anything outside the agreed surface.
Where an agent generates SQL, it generates it against a governed reporting layer. Queries are parsed and checked before execution — permitted objects only, row and cost limits, no writes — and results are validated and shaped before they are returned. Retrieval respects the permissions the requesting user already has.
Authorisation designed in rather than bolted on: OAuth flows, short-lived tokens, secrets held in a managed store such as Secret Manager, per-user identity carried through to the data layer, and a log of every tool call with its arguments and outcome — the kind of evidence a SOC 2 programme asks for.
Package the sequences people repeat — retrieve, generate SQL, validate, format, export — as reusable skills and tools rather than prompts copied between teams, so behaviour is versioned, tested and improved in one place.
Agree a test set of real requests, including ones the server should refuse, and measure tool-selection accuracy, answer correctness, latency and cost before widening access. Ship containerised with Pytest coverage, CI, structured logging and a documented upgrade path as the protocol and the clients change.
A useful first project
Choose one system your teams query constantly and one small group of users. Define the three or four tools that cover most of their requests, wire them to a scoped service identity, and put validation and logging around every call from the first day. Test against the requests those users actually make, including the ones the server should refuse. That first server establishes the authorisation pattern, the logging format and the evaluation set, and every connector added afterwards inherits them instead of reinventing them.
See our delivery processWorking together
Agree which tasks belong to the agent, which systems and objects are in scope, what it must never do, and who approves an exception.
Implement the server and its authorisation, then test against real requests and refusal cases before anyone outside the pilot group connects.
Ship with logging, monitoring and CI, document the tool contract, and agree how tools change as your systems and the protocol evolve.
Teams our engineers have worked with
Before we begin
Not answered here? Ask us directly or read the full FAQ.
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.
Related services
ETL/ELT pipelines, BigQuery, Snowflake and Redshift warehouses, migrations, semantic models and reporting your team can trust.
ExploreDedicated offshore data engineering and analytics capacity from India for ETL, cloud warehouses, BI, migration and data quality work.
ExploreKnowledge assistants grounded in your documents, MCP servers and custom connectors exposing approved tools, and LLM features with evaluation built in.
ExploreRelated guides
MCP explained
What an MCP server exposes, when building one makes sense, and the security decisions involved.
AI approach
When each approach fits, with permissions, evaluation and cost in plain language.
Buyer's checklist
Who does the work, how the first milestone is scoped and how correctness is proven.
Your next step
Bring your goal, current tools and preferred working hours. We use the first 30-minute conversation to clarify fit and an initial scope, and you leave with a written next step.
Remote from Ahmedabad, India · Overlap with any timezone · No obligation
