segment_cells_3d

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a side effect (output_path saves masks) and that a dictionary is returned, but it omits details about GPU usage implications, potential errors, resource requirements, or the structure of the returned dictionary. For a compute-heavy segmentation operation, this is inadequate.

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 well-structured docstring with clear Args and Returns sections. It is not verbose, and the main purpose is stated upfront. Each parameter description is concise and scannable.

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?

With 10 parameters, no annotations, and only a vague output description ('dictionary with 3D segmentation results'), the description lacks essential context. It does not explain the keys in the returned dictionary, error handling, installation prerequisites, or how to interpret results. It also fails to guide the agent on choosing between this and closely related siblings, leaving critical gaps.

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 Args section is the only documentation for parameters. It provides meaningful semantics for each parameter (e.g., 'do_3d: Use full 3D segmentation (True) or slice + stitch (False)' and 'anisotropy: Z-axis anisotropy factor'). While a few descriptions are vague (model_type), overall it adds value beyond the bare 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 states a specific verb ('Segment') and a clear resource ('cells in a 3D volume') while naming the method (Cellpose). It directly distinguishes from siblings like segment_cells_2d (2D vs 3D) and segment_cells_batch (batch), so the agent can differentiate immediately.

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 explicit guidance is provided on when to use this tool versus alternatives. The description only explains parameters (e.g., do_3d) but does not state when to prefer 3D over 2D, when to use batch mode, or any prerequisites. This leaves the agent to infer usage context from sibling names.

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