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 the tool triggers training (implying a write/mutation operation) and includes '[Write]' as a hint, but lacks critical details: it doesn't specify whether this is an asynchronous/long-running process, what permissions are required, potential side effects (e.g., overwriting existing models), rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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, efficient sentence that front-loads the core purpose ('Trigger training of the change risk prediction ML model'). The '[Write]' annotation is appended concisely. There is no wasted verbiage, making it easy to parse, though it could be slightly more structured (e.g., separating behavioral hints into a second sentence).

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?

Given the tool's complexity (triggering ML model training, a non-trivial write operation) and the absence of both annotations and an output schema, the description is insufficient. It lacks details on execution behavior (e.g., synchronous/asynchronous), expected outcomes, error handling, and dependencies. While the parameter is well-covered by the schema, the overall context for safe and effective use is incomplete, especially for a mutation tool with no structured safety hints.

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 adds no additional parameter information beyond what the schema provides. Since schema coverage is high, the baseline score of 3 is appropriate—the description doesn't compensate but doesn't need to, as the schema already explains the parameter adequately.

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 specific resource ('change risk prediction ML model'), making the purpose unambiguous. It distinguishes itself from sibling tools like 'ml_predict_change_risk' (prediction vs. training) and 'ml_model_training_history' (history vs. triggering). However, it doesn't explicitly contrast with other ML training tools like 'ml_train_anomaly_detector' or 'ml_train_incident_classifier', which slightly limits sibling differentiation.

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. It doesn't mention prerequisites (e.g., whether data must be prepared), timing considerations (e.g., after model updates), or how it relates to other ML tools like 'ml_evaluate_model' or 'ml_predict_change_risk'. The only contextual cue is the 'Write' annotation, but this is not elaborated in the description itself.

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