servicenow-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Trigger training' and '[Write]', implying a write operation that initiates a process, but it does not detail behavioral traits like whether training runs asynchronously, expected duration, resource requirements, or potential side effects (e.g., overwriting existing models). This leaves significant gaps for a mutation tool.

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 brief and front-loaded with the core purpose ('Trigger training of the incident classification ML solution'), followed by a minimal annotation-like note ('[Write]'). It avoids unnecessary elaboration, though the '[Write]' could be integrated more smoothly into the main sentence for better flow.

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 mutation tool with no annotations and no output schema, the description is insufficient. It lacks critical information such as what the tool returns (e.g., training status, model ID), error conditions, or dependencies. The context signals indicate a simple parameter structure, but the behavioral and output gaps make it incomplete for effective agent use.

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 input schema has 100% description coverage, with the single parameter 'solution_name' documented as 'ML solution name (default auto-detect)'. The description does not add any further semantic context about this parameter, such as examples or implications of the auto-detect feature. Given the high schema coverage, the baseline score of 3 is appropriate.

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 the target ('incident classification ML solution'), which is specific and unambiguous. However, it does not explicitly differentiate from sibling ML training tools like 'ml_train_anomaly_detector' or 'ml_train_change_risk', which would require mentioning unique aspects such as the specific ML model type or use case.

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 no guidance on when to use this tool versus alternatives, such as other ML training tools in the sibling list (e.g., 'ml_train_anomaly_detector'). It lacks context about prerequisites, timing, or scenarios where this tool is appropriate, leaving the agent without usage direction.

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