plot_latent_factors

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses the exact plot contents, visual styling (single trials thin, group means bold), and the dependency on a prior fit. It does not discuss errors or side effects, but as a plotting tool this is a reasonable and transparent summary.

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 compact, front-loads the figure contents, and adds the key prerequisite in a single short final sentence. Every sentence earns its place with no filler or redundancy.

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 description covers what the figure contains and the necessary prerequisite, which is good for a plotting tool. However, with no annotations, no output schema, and no explanation of max_trials_drawn, there are gaps that leave the agent to infer parameter semantics and some usage details.

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%, and the description does not explain either session_id or max_trials_drawn. The description's mention of 'single trials' only indirectly relates to max_trials_drawn, and no parameter-level semantics are provided.

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 what the tool produces: a figure with latent factors over time, the first-two-factor trajectory, and variance explained. It does not explicitly differentiate from siblings, though 'Requires fit_latent_factors first' signals it is the post-fitting visualization counterpart.

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 gives clear usage context by requiring fit_latent_factors to have been run first, which is critical for correct ordering. It does not mention alternative tools or exclusion cases, but the prerequisite is a strong and useful guideline.

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