get_gcp_price

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

No annotations are provided, so the description carries the full burden. It discloses the returned fields (vCPUs, memory, hourly USD, monthly USD) and the specific scope (on-demand Linux, us-east1). However, it does not mention whether the tool makes a live API call, any rate limits, or prerequisites (e.g., credentials). Overall, it is transparent for a simple lookup.

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, front-loaded sentence that contains all essential information: purpose, scope, and output. No unnecessary words, making it highly efficient.

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 no output schema, the description adequately explains the return fields (vCPUs, memory, hourly USD, monthly USD). It does not mention error handling or edge cases, but it is complete enough for a straightforward lookup tool.

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 coverage is 100% and the description adds meaning by clarifying the parameter 'machine_type' with examples and an enumeration of available values. The description also explains the return value, which goes beyond the parameter schema.

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 looks up on-demand Linux hourly + monthly price for a GCP Compute Engine machine type in us-east1. It specifies the resource (GCP price), region, OS, and pricing terms, and lists the returned fields (vCPUs, memory, hourly USD, monthly USD), distinguishing it from siblings for other clouds.

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 implicitly indicates when to use this tool (for GCP Compute Engine prices in us-east1) versus alternatives like get_aws_price and get_azure_price. It does not explicitly state when not to use it or provide alternative tool names, but the context is clear enough for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.