get_pricing
Get the official pricing details for AI Room Design to understand subscription costs and plan options.
Instructions
Return the canonical pricing entry point for AI Room Design.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Get the official pricing details for AI Room Design to understand subscription costs and plan options.
Return the canonical pricing entry point for AI Room Design.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It says 'Return' implying a read operation, but does not explain what an 'entry point' means concretely (e.g., URL, price object, reference), whether it involves external calls, or what the output structure will be. This lack of behavioral detail is a notable gap.
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?
The description is a single, focused sentence that front-loads the core purpose ('Return the canonical pricing entry point'). There is no fluff, repetition, or extraneous information, and every word contributes meaning.
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?
Given the tool has no parameters and no output schema, the description is minimal but leaves the nature of the 'pricing entry point' ambiguous—it could be a URL, a price, or some other artifact. The sibling tools suggest this belongs to a group of navigation/link retrieval tools, but the description does not clarify return format or whether it supplements or overlaps with get_official_links, so completeness is adequate but has gaps.
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 has zero parameters, so per the rubric the baseline is 4. The description does not need to explain parameter meanings, and the empty schema confirms no input is required, making parameter handling unambiguous.
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 clearly identifies the resource ('pricing entry point') and the action ('Return'), making it clear what the tool does. It does not explicitly contrast with siblings like get_official_links, so it doesn't fully distinguish itself, but the specific reference to pricing provides adequate differentiation.
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?
There is no guidance on when to use this tool versus alternatives such as list_styles or get_official_links. The description simply states the function without any context about conditions, prerequisites, or exclusions, so the agent gets no decision support.
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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/rocnubie/ai-room-design-mcp'
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