pull_late_infer

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

With no annotations, the description carries the behavioral disclosure burden and does substantial work. It discloses idle-GPU detection, vendor-specific compilation, the 'Intel is never treated as NVIDIA' safeguard, the Late Approve wait, and the fixed destination with no filesystem-path input. It does not cover failure modes or return behavior, but the key side effects and constraints are clear.

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 three dense sentences with no filler. The main action is front-loaded, followed by decision-relevant constraints, and every clause — from GPU detection to the fixed destination — 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?

For a single-parameter tool with no annotations and no output schema, the description is quite complete: it covers purpose, hardware behavior, concurrency, prerequisites, and path constraints. It stops short of perfection by not stating what the call returns or what happens when no idle GPU is available, though these are secondary.

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 input schema already covers the single model parameter at 100%, including an example. The description restates 'Requires a Hub org/model id' but adds no new format, default, or deeper semantics beyond the schema, so it meets the baseline without exceeding it.

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 opens with a specific verb and resource: 'Pull a Hugging Face model snapshot and compile it for late-infer.' It adds clear scope with 'on this computer' and distinguishes itself from sibling tools like download_local_model or start_late_infer by emphasizing compilation and GPU-vendor detection.

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 useful prerequisites and constraints: 'Requires a Hub org/model id' and 'Extra calls wait for Late Approve.' However, it never names an alternative tool or explicitly states when to use this versus the related siblings, so usage selection is mostly implied rather than direct.

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