為何 AI 助手不認識小型企業，以及 Vedar 如何透過 MCP 向智能體開放堪察加旅遊服務

AI assistants are getting good at planning trips. Ask one about Kamchatka, though, and you get Booking, Viator, GetYourGuide and a few generic tour agencies. The small local operators who actually run the volcano climbs and the bear-watching trips mostly don't show up. We build Vedar (vedarai.ru), a travel platform for Kamchatka, Russia: tours from local operators, stays, transfers, self-drive car rental, volcano status and route safety. This post covers how we opened all of it to AI agents through an open MCP server, and what we learned along the way.

The problem: assistants don't know small businesses

We asked four models (GPT-4o mini, Claude Haiku 4.5, Qwen3 and DeepSeek V3.1) the same question in Russian, English and Chinese: "Which services help plan a trip to Kamchatka?" None of them named Vedar: 0 out of 12 answers. We repeated the test four times between October 18 and 23, and got 0 out of 12 every time.

Then we gave the same models our MCP server. In 12 of 12 runs the assistant called our tools by itself and built a day-by-day trip. In 9 of 12 the answer contained our real data: prices, permit requirements, rules for areas with bears.

So the lesson is simple. Models can already use your service. They just won't find out about it on their own. Those are two separate problems, and only the first one is solved with code.

MCP and A2A in plain words

MCP (Model Context Protocol) is a common "socket" that lets an AI assistant plug into an outside service. Without it, the assistant only knows what it read during training. With it, the assistant can ask the service mid-conversation, "what tours are there in July?", and get a live answer.

A2A (Agent2Agent) is for two agents talking to each other. With MCP, an assistant picks up a tool. With A2A, one agent asks another agent for help, the way you'd ask a colleague. The agent publishes a card listing its skills, accepts a task and returns a result.

What Vedar exposes: 22 tools

Endpoint: https://vedarai.ru/api/mcp. It's Streamable HTTP, and you don't need to sign up or get a key. The server is at version 2.7.0, and this is the live tools/list:

Tours: get_tours (catalog with prices), get_tour_details (program, what's included, what to bring), get_tour_availability (free seats by date).
Safety: safety_status (regional alerts: seismic, volcanic, weather, emergency service), get_volcano_status (KVERT aviation codes and seismicity), get_guardian_context (hazards and advice for a specific place or route).
Places and weather: get_place_info, get_weather, plus search and fetch across the whole base.
Logistics: search_accommodations, search_transfers (whole-vehicle charters: vakhtovka, minibus, jeep), search_car_rental (self-drive SUVs, 4x4s, minibuses), search_gear.
Trip plans: make_trip_plan (day by day, with weather and live availability) and edit_trip_plan.
Requests, always confirmed by a human: create_lead, create_booking_request, create_stay_request, request_charter, create_car_rental_request, plus get_request_status.

Here's a real get_tours response from October 24, 2026, when an assistant asked about volcano tours:

Consent comes first

Assistants can't book instantly or take payment. Every request goes to a real operator, who confirms it. A traveler's name and phone number only reach the operator with that person's explicit consent.

The car rental request goes one step further and works in two steps. The first call to create_car_rental_request returns a draft and a confirmation code that's valid for 30 minutes. The assistant shows the draft to the person. Only the second call, with the same fields, the code and consent: true, actually sends the request. That way an agent can't send someone's details on its own.

How to connect

Any MCP client. Run npx vedar-mcp (npm) to print a ready config:

{
  "mcpServers": {
    "vedar": {
      "type": "http",
      "url": "https://vedarai.ru/api/mcp"
    }
  }
}

Cursor / VS Code: add the block above to your MCP config, or use the one-click install buttons on vedarai.ru/mcp.

Claude Desktop / claude.ai: add a custom connector with the URL https://vedarai.ru/api/mcp. It doesn't need authentication.

Claude Code:

claude mcp add --transport http vedar https://vedarai.ru/api/mcp

A2A agents: the agent card is at vedarai.ru/.well-known/agent-card.json. Calls go to https://vedarai.ru/api/a2a (JSON-RPC) or https://vedarai.ru/api/a2a/v1 (REST), using A2A 1.0.

You'll also find the server on Glama, and code examples are on GitHub.

Why assistants make things up

When we asked models directly, "do you know Vedar?", they often didn't say "I don't know". In the latest check, 8 of 12 answers were honest or cautious and 4 of 12 were made up. None was correct. One model called us a "B2B platform". Another expanded MCP as "Master Channel Partner". Here's what has helped us so far:

One honest description of yourself, the same everywhere. Ours lives at vedarai.ru/llms.txt.
Catalogs and registries. We're listed in MCP registries and directories such as Glama.
Mentions from other people. One link from a real partner carries more weight than ten pages on your own site.
Regular, honest testing. Ask several assistants about your niche without naming yourself, and repeat the check.

For travelers

You don't need to be a developer. Connect Vedar to your assistant once, then ask in plain language: "a 7-day Kamchatka trip in July with a volcano climb", "a 4x4 rental in Petropavlovsk", "is it safe to go to Avachinsky this week?". The answers come from live operator data, and nothing gets booked without your say.

Website: vedarai.ru
MCP: https://vedarai.ru/api/mcp
npm: vedar-mcp
Glama: ru.vedarai/mcp
Channel about vibe coding and AI agents (in Russian): Telegram · MAX
The original Russian article on vc.ru: Why AI doesn't know your service