run_inference

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

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about whether the model/pipeline must already be loaded, expected runtime, GPU/memory implications, or whether it is a blocking long-running operation. Only 'max_num_tokens, lower = less memory' hints at behavior, and that is already in the schema.

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?

Two short sentences, front-loaded with the core action followed by the argument list; no wasted wording. It is terse to the point of omitting necessary context, but that is a completeness issue rather than verbosity.

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 GPU inference tool with no annotations and no output schema, the description should explain the prerequisite pipeline state and what it returns (a GLB file at --output, presumably). Neither is stated, leaving critical invocation knowledge missing.

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 100%, so each parameter is already documented with its CLI flag and default. The description merely restates the flag names, adding no semantics beyond the schema, so the baseline 3 is appropriate.

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?

States a specific verb+resource: 'Run Pixal3D image-to-3D inference', which tells an agent exactly what the tool produces. However it never distinguishes itself from the sibling run_inference_sequence, so an agent must guess which entry point to pick.

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?

No guidance on when to use this versus run_inference_sequence, and no mention of prerequisites such as loading pipeline (load_pipeline) or a conda environment (load_conda_env) first, which the sibling set strongly implies are needed.

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