tsne_plot

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

With no annotations provided, the description carries the full burden. It discloses that features are auto-standardized and that the method is slower than PCA, adding behavioral context beyond the schema. However, it does not explicitly state whether the tool modifies data or simply generates a plot, though the term 'visualization' implies non-destructive behavior. It also does not mention handling of missing values or randomness.

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 appropriately compact: three sentences plus an example. It front-loads the core purpose ('t-SNE visualization'), adds comparative guidance, and gives a concrete usage example. Every sentence serves a purpose, and the example clarifies parameter usage without repetition.

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 the core purpose, usage context, and a performance caveat, but lacks essential parameter details and return behavior. With 7 parameters and no output schema, an agent still needs to infer the roles of key parameters like perplexity and save_path. The description is adequate for basic orientation but incomplete for robust 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 for all 7 parameters. The example mentions columns, perplexity, and hue but does not explain what perplexity, n_components, random_state, df_name, or save_path mean. This is a significant gap, as parameters like perplexity are critical for t-SNE behavior and are not intuitive.

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: 't-SNE visualization. Non-linear dimensionality reduction for 2D visualization.' It identifies a specific verb+resource: creating a t-SNE plot. It also distinguishes itself from PCA, a likely alternative among siblings, by noting it is better for clusters and local structure.

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 explicit guidance on when to use the tool: 'Better than PCA for revealing clusters and local structure' and 'Slower than PCA — best on datasets < 10,000 rows or use sample_data first.' This directly tells the agent when t-SNE is preferable to PCA and cautions about dataset size, helping select between this and sibling pca_transform.

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