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 mentions 'historical ML analysis' but does not clarify key behaviors: whether this is a read-only prediction (likely, but not stated), if it requires specific permissions, what the output format is (risk level scale), or any rate limits. For a prediction tool with zero annotation coverage, this leaves significant gaps in understanding how the tool operates.

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 directly states the tool's purpose without redundancy. It is front-loaded with the core action ('Predict the risk level') and method ('using historical ML analysis'), making it easy to parse. There is no wasted verbiage, earning a top score for conciseness.

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 prediction tool with no annotations and no output schema, the description is incomplete. It lacks essential context: what the risk levels are (e.g., low/medium/high), how the prediction is generated (e.g., based on a pre-trained model), error handling, or example usage. Without this, the agent cannot fully understand how to interpret or rely on the tool's results.

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 (change_sys_id, type, category) with descriptions. The description adds no additional parameter semantics beyond implying these inputs are used for ML prediction. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description does not compensate with extra details like format examples or constraints.

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: 'Predict the risk level of a change request using historical ML analysis.' It specifies the verb ('predict'), resource ('risk level of a change request'), and method ('historical ML analysis'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'ml_train_change_risk' or 'ml_forecast_incidents', which prevents a score of 5.

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 related tools like 'ml_train_change_risk' for model training. Without any usage context, the agent must infer when this tool is appropriate, which is insufficient for effective tool selection.

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