propose_training_plan

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

The description discloses key behavioral traits: it reads the CSV, decides predictions, columns to use/leave out (with reasons like target leakage, ID-like columns), and whether to use a log-target. It explicitly states it trains nothing and writes nothing. While annotations are absent, the description carries the burden well. It could add more about side effects or error conditions, but it covers the main behavioral aspects.

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 well-structured and front-loaded. The first sentence states the core purpose. The second paragraph provides workflow guidance. The third paragraph gives clear next steps. Every sentence earns its place, and the description is appropriately sized for the tool's complexity.

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 is quite complete for a planning tool. It explains the tool's role in the workflow, what it returns (plain_summary and questions_for_the_user), and how to proceed. It doesn't describe the output schema in detail, but the output schema exists and the description doesn't need to explain return values. It could mention potential errors or edge cases, but overall it's sufficient for an agent to use the tool correctly.

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 description must compensate. It mentions `features` and `log_target` as outputs to pass to `train`, but does not explain the input parameters `csv_path`, `target`, or `max_features` in detail. The description implies `csv_path` is the CSV to inspect and `target` is the target column, but it doesn't explicitly map them. This is a gap, but the description does provide some context about what the tool does with the CSV and target.

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: inspect a training CSV and return a plain-language plan before training. It specifies the resource (training CSV), the action (inspect and propose), and distinguishes it from the sibling `train` tool by explicitly saying it trains nothing and writes nothing. This is a specific verb+resource combination that an agent can easily understand.

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 explicitly says 'Call this first whenever a user wants to build a should-cost / pricing model.' It also provides a clear workflow: relay the summary and questions to the user, get confirmation, then call `train` with the agreed features and log_target. This is excellent usage guidance, including when to use it and what to do next.

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