prompteye-mcp

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Generate new prompt suggestions for a group

generate_prompt_suggestions

Schedules a PromptEye run to propose new prompts for a prompt group, checking demand and scoring candidate questions; results appear later for review.

Instructions

Asks PromptEye to propose new prompts for one prompt group of the active project, the same cycle the app runs when Generate is pressed on a group: it reads what the group is missing, drafts candidate phrases, checks their demand, expands them into questions and scores each one. PromptEye generates the prompts a project tracks: it works out which questions carry demand and phrases them the way people actually put questions to AI assistants, then proposes each one with the gap in the funnel it fills, the demand behind it, how close to a purchase it is asked and how well it fits the brand. list_prompt_suggestions returns those, ready to be accepted.

Scheduling a run is instant; the cycle itself runs in the background for a minute or more and is not waited on here. Call list_prompt_suggestions with the groupId afterwards for what it produced.

A run is not always worth scheduling — the group might already be healthy, the plan's paid work might not currently cover it, or the last run might still have proposals awaiting a decision. Then nothing is scheduled and runId comes back null with skipped saying why; that is not an error. A run already in progress, or no free plan slots left, is refused by the API instead — call get_prompt_suggestion_availability first to know which case applies.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`groupId`	Yes	Id of the prompt group, as list_prompt_groups reports it.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`runId`	Yes	Id of the run that was scheduled. null = nothing was scheduled, see skipped.
`skipped`	Yes	Why no run was scheduled; set exactly when runId is null. not_eligible = the project's plan does not currently pay for background work, cooldown = the last run finished less than 7 days ago and its proposals still await a decision, nothing_to_suggest = the group is already healthy: no funnel gap to fill and nothing worth imitating.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.0.22Oct 2, 2026

TDQS

A4.6/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only mark this as non-read-only and non-idempotent; the description adds the critical behavior that the call schedules work and returns immediately while the cycle runs in the background for a minute or more. It also documents the skipped case (runId null with a reason, explicitly not an error) versus the refusal case, which an agent could not infer from structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and the async/skip semantics are front-loaded, and the routing advice lands at the end. The middle explanation of what PromptEye generates and scores is somewhat expansive and restates the opening cycle description, costing a little efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a background-triggering mutation with an output schema already present, the description covers everything an agent needs: that it is asynchronous, that results are fetched elsewhere, and how to interpret the skipped and refused outcomes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single parameter with 100% schema description coverage ('Id of the prompt group, as list_prompt_groups reports it'), so the schema carries the load. The description only loosely adds that the group belongs to the active project; it contributes no format or constraint detail beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete verb and resource ('propose new prompts for one prompt group of the active project') and goes further by enumerating the internal cycle it triggers. It is clearly separable from list_prompt_suggestions (returns the results) and get_prompt_suggestion_availability (checks whether a run is possible).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit sequencing: call list_prompt_suggestions afterwards for output, and call get_prompt_suggestion_availability first to distinguish a graceful skip from an API refusal. It also names the conditions under which a run is not worth scheduling (healthy group, plan coverage, pending proposals).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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