Access the official pricing entry point for Nano Banana Pro AI to find current rates and plans.
Return the canonical pricing entry point for Nano Banana Pro AI.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "get_pricing"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It fails to explain what 'pricing entry point' means in practice — whether it returns a URL, an object, or performs any side effects. There is no mention of response format, network calls, or permissions needed. The description is too minimal to provide meaningful transparency.
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 sentence that is concise, front-loaded with the action, and contains no filler. Every word 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?
The tool is very simple (no params, no output schema), but the description lacks specificity about what a 'pricing entry point' actually returns. Without an output schema, the description should clarify the return format or provide more detail. This is a clear gap, though the tool's simplicity partially mitigates it. A score of 3 reflects adequate but incomplete guidance.
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 the description does not need to explain parameter semantics. The schema is empty with 100% coverage (nothing to cover). The baseline for 0 params is 4 because the description correctly implies no inputs are needed.
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 the tool's purpose with a specific verb ('Return') and resource ('canonical pricing entry point for Nano Banana Pro AI'). This distinctly differentiates it from sibling tools like list_styles and get_official_links, which focus on styles and links respectively.
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 term 'canonical' implies this is the definitive source for pricing information, giving some usage context. However, the description does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It is implied but not fully articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.