---
title: "How to configure MCP Tool for the Start node"
description: "Instructions for configuring the MCP Tool trigger for the Start node — Input Schema, Return Schema, AI assistant connection."
---

# How to configure MCP Tool for the Start node?

The **MCP Tool** trigger exposes the section as a tool for AI assistants (Cursor, Fastline, etc.) connected to the platform via MCP (Model Context Protocol). The AI can call the section, pass parameters, and receive the result.

## When you need it

- The AI assistant needs to get data from ConnectiveOne (exchange rates, catalog, checks).
- You need to call already configured scenario logic from the AI client without duplicating code.
- You use Cursor or another application with MCP support.

## What to know

- **Input Schema** — description of input parameters (names, types, required). The AI passes these when calling.
- **Return Schema** — description of the result and list of keys the platform returns from the section in the MCP response.
- **Result constant** — for the AI to receive useful data, the section must write the result to a special constant at the end (via "save to constant" action).
- **Parameters in scenario** — Input Schema parameters are automatically created as constants with prefix `mcp_input_` and are available as `{{ mcp_param_name }}`.

## Before you start

- [x] You are signed in with integrator or administrator rights.
- [x] You have a section with logic to call from the AI.
- [x] The AI client supports MCP (Cursor, Fastline, etc.).

## Steps

### 1. Select MCP Tool trigger type

1. Select the Start node on the canvas.
2. In Node Inspector, in the **Trigger type** field, select **MCP Tool**.

### 2. Fill in Input Schema

Input Schema defines the parameters the tool accepts from the AI.

1. In the **Input Schema** block, click **Add parameter**.
2. For each parameter specify:
   - **Name** — in Latin, e.g. `currency`, `date`, `product_id`.
   - **Type** — string, number, boolean, etc.
   - **Required** — whether the parameter is required.
   - **Default value** — optional.
   - **Description** — short description for the AI (recommended).

**Example parameters:**

| Name | Type | Required | Description |
|------|------|----------|-------------|
| currency | string | yes | Currency code (USD, EUR, UAH) |
| date | string | no | Date in YYYY-MM-DD format |

Parameters appear as constants in the scenario (e.g. `mcp_param_currency`) and can be used in nodes as `{{ mcp_param_currency }}`.

### 3. Fill in Return Schema

Return Schema defines which result keys are returned from the section.

1. In the **Return Schema** block, specify **Keys** — list of key names in the response (e.g. `rate`, `date`, `source`).
2. Add **Description** — short description of the tool for the AI.

### 4. Write result to constant

**Critical:** Without writing the result, the MCP response will be empty.

1. At the end of the section logic, add a **save to constant** action or equivalent.
2. Write the result to a constant whose keys match the Return Schema.
3. Ensure Return Schema keys match the written data.

### 5. Copy configuration for AI client

Node Inspector has two buttons:

- **Copy config for .cursor/mcp.json** — configuration with authorization via headers (recommended).
- **Copy config with URL token** — URL with token in the query (if the client does not support headers).

Copy the appropriate option and add it to the MCP configuration in your AI client.

### 6. Connect the AI client

Detailed connection instructions: [Connect AI assistant via MCP](/en/scenariodialog/how-to/connect-ai-assistant-mcp.md).

Briefly:
1. Open MCP settings in the AI client.
2. Add MCP server with URL and headers (or URL with token) from step 5.
3. Save and reload MCP.

### 7. Connect the section to a Fast Line Pro agent (optional)

If a Fast Line Pro agent should call this section in a dialog:

1. Save the section or scenario.
2. Under **Connect to Fast Line Pro**, select agents and save the scenario.
3. In Fast Line Pro, open the agent card and click **Publish**.

**Connect to Fast Line Pro** appears when **Scenario MCP ↔ FLP agent binding** is enabled for your instance. Alternatively, use **Add MCP Tool from scenario** on the agent card. See [Bind a Scenario MCP Tool section to an agent](/en/fastlinepro/how-to/bind-scenario-mcp-tool-to-agent.md).

## What happens next

The AI assistant sees the section as a tool and can call it by name, passing parameters according to Input Schema. The response contains keys from Return Schema.

## How to verify it works

- Check that the tool appears in the AI client.
- Call the tool with minimal parameters.
- If the response is empty — verify result writing to constant and Return Schema keys.

## Common issues

| Problem | Solution |
|---------|----------|
| MCP returns empty result | Add an action at the end of the section to write the result to the constant. Verify Return Schema keys match the written data. |
| AI does not see the tool | Check MCP connection, token, bot_id. Reload MCP client. |
| Parameters not substituted | Ensure you use `{{ mcp_param_name }}` with correct names (Latin, lowercase). |

## Related materials

- [What is the Start node](/en/scenariobuilder/explanation/what-is-start-node.md)
- [Connect AI assistant via MCP](/en/scenariodialog/how-to/connect-ai-assistant-mcp.md)
- [Configure MCP Tool parameters](/en/scenariodialog/how-to/configure-mcp-tool-parameters.md)
- [Bind a Scenario MCP Tool section to a Fast Line Pro agent](/en/fastlinepro/how-to/bind-scenario-mcp-tool-to-agent.md)
- [Configure Start node triggers](/en/scenariobuilder/how-to/configure-start-node-triggers.md)