servicenow-mcp

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

With no annotations provided, the description carries full burden for behavioral disclosure. It indicates this is a write operation ('[Write]'), but lacks crucial details: whether training is resource-intensive, time-consuming, asynchronous, or requires specific permissions. No information about rate limits, side effects, or what happens if training fails is included.

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 brief (one sentence plus a write indicator) and front-loaded with the core purpose. The '[Write]' annotation is efficiently appended. However, the structure could be slightly improved by separating the behavioral indicator into its own sentence for clarity.

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 machine learning training tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after training (e.g., model storage, evaluation metrics, or how to use the trained model), nor does it cover error conditions or training limitations. The absence of behavioral details makes this incomplete for proper agent usage.

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%, so the schema already documents both parameters ('table' and 'field') adequately. The description adds minimal value by mentioning 'specific table/field' but doesn't provide additional context about parameter relationships, valid values, or format requirements beyond what the schema provides.

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 action ('Trigger training') and target ('anomaly detection model for a specific table/field'), making the purpose immediately understandable. However, it doesn't differentiate from sibling ML tools like 'ml_train_change_risk' or 'ml_train_incident_classifier' beyond the 'anomaly detection' focus.

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?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., data requirements, existing models), nor does it specify when training is appropriate versus using other ML tools like 'ml_detect_anomalies' or 'ml_evaluate_model'.

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