get_gpu_infra_costs

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It honestly notes that vendors gate per-range cost data and that the tool reports only what's reachable via credentials, potentially relying on invoice email parsing. This sets realistic expectations about data completeness and dependencies.

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 well-structured with three paragraphs: purpose, honest limitations, and parameters. Examples are provided. It is front-loaded with the primary purpose. However, the 'Args' section could be more concise, and the 'Honest note' paragraph might be trimmed without losing value. Overall efficient but not flawless.

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 complex with no output schema. The description covers purpose, limitations, and parameters adequately, but it lacks details on what the report output contains (e.g., spend per provider, breakdowns, formatting). Given the absence of an output schema, more information on return format would improve completeness.

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 0%, so the description must add meaning. It explains the 'days' parameter (default 30, ignored if start_date set), 'start_date' (ISO format, optional), and 'end_date' (defaults to today). This clarity beyond the raw schema justifies 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 the tool's function: 'Report spend status across serverless-GPU / inference-infra providers, Modal, Together, and Replicate.' It uses a specific verb ('report') and identifies the resource ('spend status on GPU infra providers'). This distinguishes it from sibling tools like get_llm_costs or audit_gcp_waste, which target different cost categories.

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 provides context about when to use this tool, noting it's for AI startups' largest variable cost. It mentions limitations with vendor APIs and a fallback via invoice email parser. However, it fails to explicitly compare with sibling tools like get_llm_costs or benchmark_costs, nor does it state when not to use it. The guidance is implicit rather than explicit.

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