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 analyzing performance metrics but doesn't specify whether this is a read-only operation, requires permissions, has rate limits, or what the output format looks like. For an analysis tool with zero annotation coverage, this is a significant gap in transparency about how the tool behaves.

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 without unnecessary words. It directly states what the tool does and the key metrics, making it easy to parse. Every part of the sentence earns its place by conveying essential information.

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 moderate complexity (analysis with two parameters), no annotations, and no output schema, the description is minimally adequate. It covers the purpose and metrics but lacks behavioral details, usage context, and output information. This leaves gaps that could hinder an agent's ability to use the tool effectively in varied scenarios.

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 clear documentation for both parameters ('topic_sys_id' and 'days'). The description doesn't add any parameter-specific details beyond what's in the schema, such as examples or constraints. With high schema coverage, a baseline score of 3 is appropriate, as the schema does the heavy lifting.

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: 'Analyse Virtual Agent NLU performance — conversation completion rates and fallback metrics'. It specifies the verb ('Analyse'), resource ('Virtual Agent NLU performance'), and key metrics, making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'ml_detect_anomalies' or 'ml_forecast_incidents', which are also ML-related but for different purposes.

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, context (e.g., monitoring vs. troubleshooting), or compare it to other ML or VA-related tools in the sibling list. This lack of usage context leaves the agent to infer applicability based on the purpose alone.

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