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 retrieves information ('Get'), implying a read-only operation, but does not specify any behavioral traits such as permissions required, rate limits, response format, or whether it accesses real-time or cached data. This is a significant gap for a tool with no annotation coverage.

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 key action and resources without any wasted words. It is appropriately sized for its purpose, making it easy to parse quickly.

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 complexity of ML evaluation (which can involve multiple metrics and states), the lack of annotations and output schema means the description should provide more context. It does not explain what specific metrics are returned, the format of the output, or any dependencies, leaving the agent with incomplete information for effective 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 'model_sys_id' documented as 'ML solution sys_id'. The description does not add any additional meaning beyond this, such as format examples or where to find the sys_id, so it meets the baseline score when schema coverage is high.

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 with a specific verb ('Get') and resource ('accuracy, training status, and metrics for a trained ML solution'), making it easy to understand what it does. However, it does not explicitly differentiate from sibling tools like 'ml_model_training_history' or 'ml_predict_change_risk', which might also involve ML model evaluation or metrics, so it misses full 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 does not mention prerequisites (e.g., needing a trained model), exclusions, or compare it to other ML-related tools in the sibling list, leaving the agent without context for selection.

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