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. While it mentions forecasting based on historical trends, it lacks critical details: what model is used, accuracy or confidence intervals, whether it requires specific permissions or data access, rate limits, or what the output format looks like (since there's no output schema). For an ML tool with zero annotation coverage, this is a significant gap in transparency.

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 ('Forecast incident volume for the next N days') and adds context ('based on historical trends'). There's no wasted verbiage, making it easy to parse. However, it could be slightly more structured by explicitly separating purpose from constraints or usage notes.

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 an ML forecasting tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects (e.g., model type, confidence), output format, error handling, or dependencies on historical data. While the schema covers parameters well, the overall context for safe and effective use is lacking, especially compared to sibling tools that might have richer descriptions.

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 all three parameters (days_ahead, category, priority) with their types and optionality. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain how 'category' or 'priority' affect the forecast, valid values, or interactions between parameters. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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: 'Forecast incident volume for the next N days based on historical trends.' It specifies the verb ('forecast'), resource ('incident volume'), and scope ('next N days based on historical trends'), which is specific and actionable. However, it doesn't explicitly distinguish itself from sibling tools like 'ml_predict_change_risk' or 'ml_detect_anomalies', which are also ML-based predictive tools.

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., historical data availability), exclusions (e.g., when other forecasting methods are better), or how it compares to sibling ML tools like 'ml_predict_change_risk'. Without such context, an agent might struggle to choose this tool appropriately in a complex scenario.

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