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generatePrompt

Generates a complete, high-quality prompt from a raw topic or idea tailored to a specific format (image, video, code, productivity).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoadvanced
topicYesThe topic, idea, or concept for which to generate a prompt.
formatNoproductivity
target_platformNouniversal

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoThe prompt format.
generated_promptNoThe generated prompt text.
credits_remainingNoUser remaining credits.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

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

Annotations declare readOnlyHint=false and openWorldHint=true, and the description adds nothing beyond confirming it produces text. It omits any behavioral detail on the mode tiers, whether generation is deterministic, or external dependencies implied by openWorldHint; with an output schema present the return-value gap is acceptable, but the mode behavior gap is not.

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?

A single front-loaded sentence with no filler; the verb and the format tailoring come first. It is efficient, though its brevity is partly the cause of the missing guidance rather than a virtue of tight writing.

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

Completeness2/5

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

An output schema exists so return values need no explanation, but for a 4-parameter tool with two enums and a sibling that overlaps in purpose, the description leaves agent-critical decisions (mode selection, sibling choice, target_platform meaning) unaddressed.

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

Parameters2/5

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

Schema description coverage is only 25%, so the description must compensate. It does enumerate the format enum values (image, video, code, productivity), which is useful, but it says nothing about mode (quick/advanced/ultimate) or target_platform, leaving two of four parameters functionally opaque.

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

Purpose4/5

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

States a specific verb (generates) and resource (prompt) and narrows it to a raw topic tailored to a format, which is more than a restatement of the name. However it does not differentiate itself from the sibling optimizePrompt, so an agent must infer the boundary between generating a new prompt and optimizing an existing one.

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

Usage Guidelines2/5

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

No when-to-use guidance and no alternatives named. The description never explains when to pick generatePrompt over optimizePrompt, nor what the quick/advanced/ultimate modes mean or when to choose each.

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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