elbow_plot

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

The description explains what is plotted and the concept of the elbow point, but it does not disclose behaviors such as how df_name is used, what save_path does, or what the function returns. With no annotations provided, the description carries the full burden and falls short on these operational details.

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 concise, with three focused sentences and a helpful example. Every sentence adds value, and the format is easy to scan.

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?

While the core purpose is clear, the description omits essential context for parameters like df_name and save_path, and does not specify return behavior or data requirements. For a 5-parameter tool with no annotations or output schema, this is only minimally complete.

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 coverage is 0%, meaning the description must explain all parameters. It only mentions columns and max_k in the example, leaving df_name, save_path, and random_state completely unexplained. This is a significant gap for a 5-parameter tool.

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: creating an elbow plot to find the optimal number of clusters for K-Means. It specifies the resource (inertia vs number of clusters) and distinguishes this from sibling clustering tools like kmeans_cluster or silhouette_score.

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?

It clearly implies when to use the tool (before K-Means to determine K) and provides an example. However, it does not explicitly name alternatives or state when not to use it, so it lacks the explicit exclusion criteria seen in higher-scoring guidelines.

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