ml_model_training_history

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

Annotations already declare readOnlyHint=true and openWorldHint=true, establishing this is a safe read operation. The description adds the return scope (history + accuracy trends over time) but does not disclose behavior like result ordering, pagination, or limits. This is acceptable but not rich context beyond the annotations.

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 single, front-loaded sentence of 13 words. It is concise and every word adds meaning: 'Get' (verb), 'training run history and accuracy trends' (resource), and 'for an ML solution over time' (scope). 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?

For a read-only, two-parameter tool with 100% schema coverage and no output schema, the description adequately communicates what is returned (training run history and accuracy trends). It does not detail output structure or pagination, but the tool's simplicity and strong annotations make the description largely sufficient.

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 100%: both model_sys_id and days are documented in the input schema with meaningful descriptions. The description's phrase 'over time' loosely aligns with the days look-back parameter, but it adds no syntax or semantics beyond what the schema already provides, matching the baseline.

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 uses a specific verb ('Get') and a specific resource ('training run history and accuracy trends') scoped to 'an ML solution over time'. This clearly distinguishes it from ML siblings like ml_evaluate_model, ml_train_*, and ml_predict_*, none of which retrieve training history.

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 implies a clear use case: retrieve training run history and accuracy trends for a specific ML solution. However, it does not explicitly state when not to use it, nor does it name alternatives such as ml_evaluate_model for model evaluation or get_devops_insights for pipeline history.

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