MCP Trigger
Turn Processes into MCP Tools for Agentic AI.
In order to expose a Frends Process as a callable tool for AI agents and Large Language Models (LLMs), MCP Trigger can be used.
You can learn more about Triggers in general here.
What is MCP Trigger?
The MCP Trigger exposes a Frends Process as a callable Tool for AI agents and Large Language Models (LLMs) using the Model Context Protocol (MCP). MCP is an open standard for AI-to-application communication, functioning as a universal interface between AI systems and the services they interact with. When a Process uses an MCP Trigger, it becomes discoverable and executable by any MCP-compatible AI client — without requiring custom API wrappers or bespoke integrations.
Each Frends Agent acts as an independent MCP Server, hosting an endpoint at /mcp by default. The endpoint accepts JSON-RPC requests over HTTPS, and AI clients interact with it using the standard MCP methods tools/list and tools/call. When an AI client calls tools/list, Frends returns only the Tools the authenticated client is authorized to access. When the client calls tools/call with a Tool name and input arguments, Frends executes the corresponding Process and returns the result.
Usage
To use a Trigger in your Process, add it to your Process canvas, and connect it to the beginning of your Process using a Sequence flow. If you are using multiple Triggers in your Process, all Triggers must connect to the same shape, which is also the first non-Trigger shape in your Process.
Once added to your Process, you can change the Trigger's type to match your requirement and give it a unique name. By default, the Trigger's type will provide a corresponding name for the Trigger. You can then configure the parameters for the chosen Trigger type.
For the MCP Trigger, you should define a name and a description for the tool, and optionally provide an inputSchema and outputSchema to describe the data the tool accepts and returns. Once the Process is activated, the Frends Agent exposes it as a callable MCP tool at its /mcp endpoint. Any MCP-compatible AI client that has been granted access through an API Policy can then discover and invoke the Process as a tool.
Authentication and Authorization
MCP endpoints are secured using Frends API Policies. Each AI client must authenticate before it can call tools/list or tools/call. The API Policy determines which Tools the client is allowed to access — tools/list returns only the Tools the authenticated client has been granted permission to use.
The supported authentication methods for MCP Policies are OAuth 2.0 and JWT tokens. API Key authentication is not available for MCP Policies. The HTTP method for MCP Policies is always POST and cannot be changed. The target endpoint URL in the policy corresponds to the tool's name.
A dedicated Create MCP Policy shortcut is available in the API Policy view to streamline policy setup for MCP tools. Essentially, the MCP API Policy must include /mcp endpoint for it to apply to MCP Tool calls.
Configuration
To set up an MCP Trigger, add the shape to your Process canvas and connect it as part of your Process. After that, you can select the Trigger type and configure the parameters for it.
The common parameters available for all Triggers are explained on the Trigger reference page. The following configuration parameters are available for MCP Trigger. Well-written names and descriptions are important because AI agents use them to decide which tool to call and how to use it.
Name
The name field is the unique identifier for the tool. It is used as the tool name in tools/list and tools/call responses, and as the target endpoint identifier in the associated API Policy.
The name must be unique across all activated Processes in the Tenant. Process Editor validates this in real time and marks the Trigger with an error if a duplicate is detected.
Title
The title field is an optional, human-readable display name for the tool. It is shown to AI clients and users in contexts where a friendlier label is preferable to the identifier in name.
Description
The description field explains what the tool does in plain language. While optional, a clear and accurate description significantly improves how reliably AI agents select and invoke the tool. A vague or missing description can cause AI clients to call the wrong tool or fail to call the right one.
Input Schema
The inputSchema field defines the parameters the tool accepts, expressed as a JSON Schema object. When an inputSchema is defined, the Process Editor provides IntelliSense and autocomplete for #trigger.data.body.* variable references throughout the Process.
An example inputSchema for a tool that creates a CRM lead:
Another example to expect no input parameters for the tool:
Output Schema
The outputSchema field defines the structure of the data returned by the Process, also as a JSON Schema object. It is optional, but when provided it enables the Create Return Shape button in the Process Editor. Clicking this button automatically generates a return shape template populated with sample data derived from the schema, removing the need to map response fields manually.
Example outputSchema that returns a single plain string:
More complex outputSchema to return an object with count field for number of results, as well as the result objects as array:
Annotations
Annotations are optional hints that inform AI clients about the behavioral characteristics of the tool. They do not affect how Frends executes the Process, but they help AI agents make better decisions about when and how to call a tool.
Each annotation can be individually set to be Yes or No for the Trigger.
The available annotations are:
readOnly signals that the tool only reads data and never modifies or deletes anything. AI clients can treat such tools as safe to call freely.
destructive warns that the tool may modify or delete data. AI clients that respect this hint may prompt the user for confirmation before calling the tool.
idempotent signals that calling the tool multiple times with the same inputs produces the same result. AI clients can safely retry an idempotent tool if a call fails or times out.
openWorld indicates that the tool may return fields beyond what is described in outputSchema. AI clients should not assume the output is exhaustively defined by the schema.
Do not log trigger parameters
Toggle option that when enabled, hides the Trigger parameters from being logged in Process Instances.
Reference Values
In addition to the default Trigger reference values, MCP Trigger provides the following additional values.
#trigger.data (Frends.ExecutableProcess.CaseInsensitivePropertyTree)
Object containing the data provided by the AI client when calling the tool, including the tool name and its input arguments.
#trigger.data.tool (String)
The name of the tool that was called by the AI client, as specified in the tools/call request. This corresponds to the name field configured on the MCP Trigger.
#trigger.data.arguments (Object)
The input arguments passed by the AI client in the tools/call request. Each property on the object corresponds to a named argument. For example, if an AI client calls the tool with fullName = "Jane" and email = "[email protected]", then #trigger.data.arguments.fullName resolves to "Jane" and #trigger.data.arguments.email resolves to "[email protected]".
If an inputSchema is defined, the Process Editor provides autocomplete for all property names declared in the schema when typing #trigger.data.arguments..
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