estimate_vram

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

No annotations are provided, so the description carries the burden. It discloses that the result is an 'estimate including context overhead' and specifies the return format (dict with vram_gb and recommended GPU tiers). This is transparent for a computation tool. Additional details like potential error conditions or performance characteristics are missing but not critical.

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 concise: one line for purpose, then bullet-like args and returns. No unnecessary words. Front-loaded with the verb.

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's simplicity (2 params, no output schema, no annotations), the description covers inputs and outputs adequately. It explains what the tool does and what it returns, meeting the needs for selection and invocation.

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 schema has 0% description coverage, so the description must compensate. It explains 'quant' with enumerated values ('q4', 'q5', 'q8', 'fp16') and default, and describes 'model_id' as 'the model ID'. This adds value beyond the schema. However, 'model_id' could be more specific (e.g., Hugging Face ID).

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: 'Estimate VRAM required to run a model at a given quantization.' It uses a specific verb ('Estimate') and resource ('VRAM'), and distinguishes itself from siblings like estimate_cost and list_models.

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 implies when to use the tool (when VRAM estimation is needed), but does not explicitly state when not to use it or mention alternatives. The purpose is clear, so a minor deduction.

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