AI Prompt Engineering Studio to craft, refine, and structure prompts for image, video, code, and text workflows.
Scored across 3 tools
generatePrompt and optimizePrompt are clearly distinguished by their input (raw topic vs existing prompt) and getAccountUsage serves a separate account management purpose. No overlapping functionality.
All tool names use consistent camelCase with a verb_noun structure (generatePrompt, getAccountUsage, optimizePrompt), making the set predictable and easy to parse.
Three tools are well-scoped for a prompt generation/optimization service: one to create prompts, one to refine them, and one to check account usage. Each tool has a distinct role and the count fits the narrow domain.
The core lifecycle of generating and optimizing prompts is covered, along with account usage. However, there is no tool to retrieve, list, or manage saved prompts, which could be a minor gap for users needing to reference past work.
Generates a complete, high-quality prompt from a raw topic or idea tailored to a specific format (image, video, code, productivity).
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | advanced | |
| topic | Yes | The topic, idea, or concept for which to generate a prompt. | |
| format | No | productivity | |
| target_platform | No | universal |
| Name | Required | Description |
|---|---|---|
| format | No | The prompt format. |
| generated_prompt | No | The generated prompt text. |
| credits_remaining | No | User remaining credits. |
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.
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.
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.
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.
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.
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.
Returns the authenticated user's available credits, account email, rate limits, and daily request usage.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| status | No | |
| account | No | |
| available_credits | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds that the payload is scoped to 'the authenticated user', implying an auth requirement, but says nothing further about rate-limit behavior on the call itself or token/permission needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One front-loaded sentence listing exactly what is returned, with no filler or redundant restatement of the tool name. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read tool with an output schema that documents the return values, the description only needs to state the resource retrieved, which it does completely. Nothing an agent needs in order to call it is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there is no parameter semantics for the description to clarify; schema coverage is nominal at 100% with an empty object. Baseline 4 applies because there is nothing for the description to compensate for.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Returns') and enumerates the exact resource payload: credits, account email, rate limits, and daily request usage. An agent can immediately tell this is an account-status read, and it is unambiguous against the unrelated siblings generatePrompt and optimizePrompt.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the usage context (checking one's own account standing, quotas, and limits) but never states when to call it or when not to. Since the sibling tools operate in a completely different domain, no alternative-selection guidance is really needed, so this is minimum-viable rather than deficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Transforms, expands, or compresses an existing prompt. Supports format targeting for image generation (Midjourney, DALL-E, Flux), video generation (Sora, Runway), clean code (Cursor, Claude), and general productivity.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Format-specific tags: aspect_ratio, lighting, camera, language, framework. | |
| format | No | The target medium or task format: image, video, code, or productivity. | productivity |
| prompt | Yes | The user's raw prompt or text to optimize, expand, or shorten. | |
| operation | No | optimize to polish; expand to add sensory details and parameters; shorten to compress into token-efficient power prompts. | optimize |
| negative_prompt | No | Whether to return negative prompt tags. | |
| target_platform | No | Target AI model (e.g. midjourney, flux, sora, cursor, claude, chatgpt). | universal |
| Name | Required | Description |
|---|---|---|
| format | No | The target format. |
| optimized_prompt | No | The engineered, high-performance prompt. |
| credits_remaining | No | User remaining credits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false, openWorldHint=true, and destructiveHint=false, so the mutating/open-world nature is covered structurally. The description adds that the tool rewrites rather than creates and that output is tailored per platform, but says nothing about cost/token consumption (relevant given the getAccountUsage sibling) or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core action and followed by the format-target detail. No filler, though the second sentence is essentially a list expansion of schema enums rather than new information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 not be described, and annotations cover safety. However, for a tool in a set with generatePrompt and getAccountUsage, the description omits any cost/quota implications and gives no routing guidance, leaving an agent to infer the workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the enum values, defaults, and tag semantics are fully documented in the input schema. The description only echoes the format targeting (image/video/code/productivity) and platform examples already present in the schema, adding no new parameter syntax or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Names a specific verb set (transforms, expands, compresses) and a specific resource (an existing prompt), and the word 'existing' implicitly contrasts it with the sibling generatePrompt. It does not explicitly name that sibling, but the resource and action are unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied via 'existing prompt' and the format-targeting list; there is no explicit statement of when to choose this over generatePrompt or when not to use it. The format/platform enumeration gives some context for selection but no exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
generatePromptgetAccountUsageoptimizePrompt
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