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 describes a read operation ('Get'), which implies it's non-destructive, but doesn't address other behavioral aspects like permissions needed, rate limits, error handling, or what the output format looks like (especially since there's no output schema). For a tool with zero annotation coverage, this is a significant gap.

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 clearly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the output contains (e.g., the structure of 'history and accuracy trends'), potential limitations, or error conditions. For a tool that retrieves historical data, more context is needed to use it effectively.

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 both parameters ('model_sys_id' and 'days') with descriptions. The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. This meets the baseline for high schema coverage but doesn't enhance understanding.

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: 'Get training run history and accuracy trends for an ML solution over time'. It specifies the verb ('Get'), resource ('training run history and accuracy trends'), and scope ('for an ML solution over time'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'ml_evaluate_model' or 'ml_train_anomaly_detector', which prevents a perfect score.

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, when not to use it, or how it differs from other ML-related tools in the sibling list (e.g., 'ml_evaluate_model' or 'ml_train_anomaly_detector'). This leaves the agent without context for tool selection.

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