Check my House AI design credits
get_my_creditsHow many House AI designs the user can still make today or this plan period.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_my_creditsHow many House AI designs the user can still make today or this plan period.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare this a safe, read-only, idempotent, closed-world operation. The description adds useful behavioral context by clarifying that credits apply to today or the current plan period, resolving a key ambiguity about the returned quantity.
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 sentence that is front-loaded and free of waste. Every word contributes to defining the returned quantity.
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 zero parameters, complete annotation coverage, and no output schema, the description provides enough context to call the tool. It could clarify the exact return shape, but that is a minor gap.
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 are no parameter semantics to document. Baseline for zero-parameter tools is a 4.
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 states that this tool reports the remaining House AI design credits, a specific resource and quantity. It does not explicitly distinguish itself from sibling tools, though the credit-related purpose is distinct from design-action siblings.
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 implied by the name and description: check remaining design capacity. There is no explicit guidance on when to call it versus alternatives, nor any exclusion conditions.
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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