servicenow-mcp

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions what the tool does but doesn't disclose important traits like computational requirements, execution time, output format, error conditions, or whether this is a read-only analysis versus a model training operation. The description is insufficient for a tool with ML capabilities.

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 extremely concise - a single sentence that directly states the tool's purpose with relevant examples. Every word earns its place with zero wasted text, making it front-loaded and efficient for quick understanding.

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 an ML analysis tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the output looks like (anomaly scores, flagged records, visualizations), doesn't mention statistical methods used, and provides no context about the tool's limitations or integration with other ML tools on the server.

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 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3. No additional context about parameter interactions or constraints is provided.

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: 'Run anomaly detection on operational metrics' with specific examples (alert volume, incident trends). It uses a specific verb ('Run') and identifies the resource type (operational metrics), but doesn't explicitly differentiate from sibling ML tools like 'ml_forecast_incidents' or 'ml_predict_change_risk' beyond the anomaly detection focus.

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 minimal usage guidance - only mentioning the type of data (operational metrics) without specifying when to use this tool versus alternatives like 'ml_forecast_incidents' or 'ml_predict_change_risk'. No context about prerequisites, limitations, or appropriate scenarios is provided.

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