What is the Model Context Protocol (MCP)? A plain-English guide

The Model Context Protocol (MCP) is an open standard that lets AI agents call external tools and read external data through one uniform interface. Rather than building a bespoke integration for every app and every data source, an AI client speaks a single protocol to any MCP server. ImonesMCP is one such server: it exposes the Lithuanian company registry so that an agent can search and read company data the same way it would use any other MCP tool.

What MCP is (and isn't)

MCP is often described as "a USB-C port for AI." The analogy holds: one standard connector, many devices. An MCP client (your AI app) connects to an MCP server (a provider of tools and data), and the client discovers what the server can do without any hard-coded knowledge of it.

What MCP is:

• An open protocol for exposing tools (actions the agent can call) and resources (data it can read) to AI models.
• A uniform contract, so the same client can talk to many servers, and the same server can serve many clients.
• Transport-flexible: servers can run locally or, like ImonesMCP, remotely over Streamable HTTP.

What MCP is not:

• It is not a model or an AI itself — it is the plumbing between a model and the outside world.
• It is not a specific vendor's product. It is a shared standard that many clients and servers implement.
• It is not a scraping trick. A well-built MCP server serves structured data from a real source with a typed interface.

MCP vs. a plain API

If you have integrated a REST API before, a fair question is: why not just call one? The difference is who does the wiring. With a plain API you read documentation, write client code, map fields, handle auth, and update all of it whenever the API changes — per data source, per app. With MCP, the server publishes its own tool definitions, and the client reads them at connect time. The agent learns what is available and how to call it without any bespoke integration code on your side. One connection, and the same agent can use a registry, a calendar, and a code repository through the identical mechanism. MCP does not replace APIs — servers like ImonesMCP sit on top of real data sources — but it standardizes how an AI model consumes them.

Why a company registry fits MCP

Company registry data is a near-perfect fit for MCP. Agents constantly need to answer questions like "is this supplier a real, active company?" or "what sector is this counterparty in?" — questions whose answers live in an authoritative dataset, not in the model's training data.

Exposing that registry as MCP tools means an agent gets fresh, structured, typed answers on demand. It calls a search tool, receives JSON, and reasons over it — no brittle HTML parsing, no stale snapshots. And because the interface is standard, the same registry works in Claude, in Cursor, or in a custom agent without any rework.

Tools ImonesMCP exposes

ImonesMCP exposes six tools over MCP. Each does one job and returns structured data:

• search_companies — search with filters: fuzzy name/code/VAT query, activityCode/activityDivision, municipality, vatActive, minRevenue/maxRevenue, registration dates, plus sort, limit, and offset.
• get_company — the full profile by registration code or vatCode: legal form, status, VAT, primary activity, municipality, and latest revenue.
• get_company_financials — yearly figures (revenue, pre-tax and net profit, assets, equity) plus raw statement line items, by code.
• get_company_people — aggregate ownership counts (Lithuanian/foreign, natural/legal). No named individuals, by design.
• resolve_activity_code — look up EVRK economic-activity sections and divisions by code, prefix, or free-text query.
• aggregate_companies — count companies grouped by activityDivision, municipality, legalForm, or status, with optional filters.

For a hands-on walkthrough of the first two, see how to look up any Lithuanian company.

How an agent uses them

When you connect ImonesMCP to a client, the agent reads the tool definitions and decides which to call based on your request. A single question can chain several tools. Ask "Is [company] a real, VAT-registered company, and how has its revenue trended?" and the agent might:

1. Call search_companies to resolve the name to a registration code.
2. Call get_company to confirm status and VAT registration.
3. Call get_company_financials to read the revenue trend.

You never name the tools yourself. You ask a question in plain language; the agent orchestrates the calls and answers with data traceable to the official registry.

Getting connected

Because ImonesMCP is a remote MCP server over Streamable HTTP, any MCP client can use it. In OAuth-capable clients like Claude Desktop you add the server and sign in — no key to manage. For CLI agents and automation you use an API key as a bearer token. The step-by-step setup for each client is in connect ImonesMCP to your AI agents, and the endpoint and tool reference live in the docs.