Offer prompts

Tools let an AI agent call your API; skills give it the know-how to use them. Prompts are different again: they are reusable, parameterized message templates that a client surfaces directly to the user — most often as slash-commands. Picking a prompt drops a ready-made starting message into the conversation, for example:

Summarize ticket {{ticket_id}} in a {{tone}} tone.

This guide explains how to author prompts and how AI clients consume them.

How clients consume a prompt

When a prompt is enabled, the runtime advertises the MCP prompts capability and offers it to connected clients via the standard protocol:

• prompts/list — the client lists the available prompts, each with its title, description, and declared arguments. Clients typically render these as slash-commands the user can pick.

• prompts/get — when the user selects a prompt and fills in its arguments, the client fetches the rendered message. The runtime substitutes the argument values into the template and returns a single user message (plus any embedded resources you attached).

Because prompts are user-controlled, they are not called automatically by the model — the user chooses them.

Arguments and placeholders

A prompt can declare arguments the user fills in at selection time. Each argument has a snake_case name, a description, and a required flag. Reference an argument anywhere in the body — or in an embedded resource's text — as {{argName}}:

Summarize ticket {{ticket_id}} in a {{tone}} tone.

At prompts/get, {{ticket_id}} and {{tone}} are replaced with the values the user supplied. Required arguments must be provided; optional ones left blank resolve to an empty string. A placeholder with no matching argument is left as-is, so a typo is easy to spot.

Argument autocomplete (suggested values)

Each argument can carry an optional list of suggested values. When you add them, the runtime advertises the MCP completions capability and answers the client's completion/complete requests for that argument — so as the user types, the client can offer ranked autocomplete suggestions instead of a blank field.

• Enter one value per line in the argument's Suggested values box.

• Suggestions are matched against what the user has typed (prefix matches first, then substring), and a response returns at most 100 values.

• Suggested values are hints, not a fixed list — clients still allow free-form input, and the value is never validated against the suggestions.

Leave the box empty for free-text arguments with no suggestions.

Embedded resources

A prompt can optionally carry embedded resources — inline reference blocks (uri, mimeType, text) returned alongside the message. Use these to attach context the model should see, such as a policy snippet or an example payload. Placeholders in a resource's text are substituted too.

Managing prompts from an agent

Prompts can be managed two ways: from the dashboard (below) or by an AI agent connected to the FlowCP MCP server. The agent surface exposes prompts as tools — list_prompts, create_prompt, update_prompt, and delete_prompt — so an agent can author and toggle prompts for a server without leaving the chat. Like the dashboard, new prompts start disabled.

Opening the prompts list

From your app's dashboard, click the Prompts tab. The page lists every prompt on the selected server, each with an on/off switch. If your app has multiple environments (branches), use the branch buttons to switch between them.

Creating a prompt

1. Click New prompt.

2. Fill in the fields:

   • Name — a snake_case identifier, e.g. summarize_ticket. This is the prompt name clients call.

   • Title — an optional display title shown to the user.

   • Description — a one-line summary so the user knows what the prompt does.

   • Message template — the body of the message, with {{argName}} placeholders for any arguments.

   • Arguments — add one row per argument (name, description, required), with optional suggested values (one per line) that power argument autocomplete.

   • Embedded resources — optional reference blocks returned with the message.

3. Click Create prompt.

New prompts are created disabled, following the platform's default-deny model — see Default-deny model.

Generating prompts with AI

To get started quickly, click AI Suggestions on the Prompts page. FlowCP reads the selected server's tools and asks an LLM to propose three prompts that help a user drive those tools, complete with {{argName}} placeholders and declared arguments.

Each suggestion shows its name, description, and a preview of the template. Click Use this on the one you want — it opens the prompt editor pre-filled (including its arguments) so you can review and refine it before saving. Nothing is created until you click Create prompt, and (like every prompt) it starts disabled.

AI Suggestions require an AI provider to be configured for the platform. If it isn't, the button reports that suggestions aren't available — you can still author prompts manually.

Enabling a prompt

Toggle the switch on a prompt's row to On to offer it to AI clients. Changes take effect immediately — no re-publish required. Disabled prompts are never listed or returned.

Editing and deleting

• Click the pencil icon to edit a prompt's name, title, description, body, arguments, or embedded resources. Renaming re-derives the slug.

• Click the trash icon to delete a prompt. This cannot be undone.

Writing effective prompts

• Lead with the task. The body should read like the message a user would type to kick off the work.

• Declare every placeholder. Each {{argName}} should have a matching argument so the client can prompt the user for it.

• Keep arguments minimal. Ask only for what changes between uses.

• Use embedded resources for context, not for the instruction itself.